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Physical, social and economic surrounding in which a child grows, plays a key role in shaping their cognitive development.

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Teachers can build a positive environment in the classroom 

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Lack of quality education in rural schools 

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children who are raised in stimulating, safe, and enriching environments are more likely to develop strong cognitive skills which include memory, problem solving and even language development. Factors like the quality of housing, parent involvement, and access to educational resources which have a Profund impact on cognitive outcomes.

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The Brain on Poverty | Jessica Sharpe | TEDxGreenville

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Hoe does income affect childhood brain development?

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Hoe does income affect childhood brain development?

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How does income affect childhood brain development? | Kimberly Noble

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Socioeconomic status and health behavior in children and adolescents: a systematic literature review 

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Socioeconomic status and health behavior in children and adolescents: a systematic literature review 

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Socioeconomic status and health behavior in children and adolescents: a systematic literature review - PMC (nih.gov)

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Socioeconomic status and health behavior in children and adolescents: a systematic literature review

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Socioeconomic status affects individuals’ health behaviors and contributes to a complex relationship between health and development. Due to this complexity, the relationship between SES and health behaviors is not yet fully understood. This literature…

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The type and quality of experiences that children have - from early learning activities to relationships- directly shapes their cognitive abilities.

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THE IMPORTANCE OF PLAY-BASED LEARNING IN EARLY CHILDHOOD EDUCATION BY OLIVER TEKYI-ARHIN

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THE IMPORTANCE OF PLAY-BASED LEARNING IN EARLY CHILDHOOD EDUCATION BY OLIVER TEKYI-ARHIN

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Play-based learning is a vital aspect of early childhood education that provides children with a meaningful and enjoyable way to learn and explore the world around them. Unlike traditional teaching methods that rely on rote memorization and drills, play-based learning activities allow children to engage in hands-on experiences that stimulate their creativity, curiosity, and problem-solving skills. In this article, I will explore the benefits of play-based learning in early childhood education, the types of play-based activities that are most effective, and strategies for implementing play-based learning in the classroom. Additionally, I will discuss the common challenges that teachers may face when implementing play-based learning and provide solutions for overcoming these obstacles. By the end of this article, I hope to demonstrate the importance of play-based learning and provide teachers with practical ideas and strategies for incorporating play-based learning activities into their curriculum.

To begin with, play is a natural and essential part of childhood. From building with blocks to pretending to be superheroes, children engage in play every day. However, play is not just a fun and entertaining activity; it is also a powerful tool for learning and development. Research has shown that play-based learning can have a significant impact on children's cognitive, social-emotional, language, and physical development (Bergen, 2002; Christie & Roskos, 2013). By engaging in play-based learning activities, children are better able to learn and retain new information, develop problem-solving skills, build strong relationships with their peers, and develop a positive sense of self (Bodrova & Leong, 2012; Lillard et al., 2013).

Play-based learning can take many forms, including free play, guided play, and intentional teaching through play (Bredekamp & Copple, 2014). In free play, children have the freedom to choose what they want to play and how they want to play, without any interference or direction from adults. Free play provides children with opportunities to develop social skills, language skills, and emotional regulation, as well as to explore their interests and develop their creativity (Bergen, 2002).

Guided play is a more structured form of play in which adults provide some direction or guidance to children's play. Guided play can be used to introduce new concepts or skills, or to support children's learning in specific areas, such as literacy, numeracy, or science (Christie & Roskos, 2013).

Intentional teaching through play involves using play as a deliberate and purposeful teaching strategy. In intentional teaching through play, adults actively engage in play with children and use play activities to teach specific skills or concepts (Bredekamp & Copple, 2014). Intentional teaching through play can be particularly effective in promoting children's language development, as adults can use play activities to model language use and provide opportunities for children to practice their language skills (Lillard et al., 2013).

Despite the benefits of play-based learning, many early childhood education programs still rely on more traditional teaching methods, such as lectures and worksheets. While these methods may be effective for older children, they are not developmentally appropriate for young children, who learn best through hands-on experiences and play (Bredekamp & Copple, 2014). Therefore, it is essential for early childhood educators to understand the importance of play-based learning and to incorporate it into their curriculum.

Generally, this article aims to provide teachers with a comprehensive understanding of the importance of play-based learning and practical strategies for incorporating it into their curriculum. By embracing play-based learning, teachers can create a more engaging, effective, and enjoyable learning experience for young children.

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Let’s Stop Talking About The ’30 Million Word Gap’

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It’s one of the most famous studies ever done on kids. It’s often cited as a reason children from poor families struggle in school. But it may be neither 30 million words, nor exactly a gap.

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Case Study - new research ignites debate on the "30 million word gap"

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Case Study - new research ignites debate on the "30 million word gap"

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In the past few decades, a range of public awareness campaigns have encouraged parents to speak more with their children. "Sing, Talk, And Read (STAR)" to your young child, cheerleads one, and "Talk With Me Baby," begs another. Almost everyone agrees: It's good for children to hear parents and caregivers talking.

This consensus didn't emerge in a vacuum. The public service campaigns are the legacy of a well-known study called Meaningful Differences in the Everyday Experience of Young American Children-more commonly known as the "30 Million Word Gap" study-which concluded that the first three years of a child's life are critical to advancing their language development and can have long-term impacts on their success in school and in life.

In the 1990s, researchers Betty Hart and Todd Risley studied families from different socioeconomic levels and found that their children were exposed to vastly different numbers of words in their formative years-specifically, 32 million more words for higher-income children than for lower-income children. The variability in exposure accounted for significant differences in children's language skills when they entered kindergarten, the researchers found, and had a direct impact on how students fared early on in school.

"The problem of skill differences among children at the time of school entry is bigger, more intractable, and more important than we had thought," wrote Hart and Risley.

But now, the study's conclusions are contested by recently published research from the psychologists Douglas Sperry and his wife, Linda, which found less straightforward connections between the quantity of words children hear and their family's socioeconomic background. Their findings have inspired a growing debate around whether biases about race and class influenced the original study's methodology-and distorted the takeaways.

In the last several decades, the original study has "shaped the way educators, parents and policymakers think about educating poor children. But did you know that the number comes from just one study, begun almost 40 years ago, with just 42 families? That some people argue it contained a built-in racial bias?" wrote Anya Kamenetz, picking up the thread of the argument in a National Public Radio (NPR) article that went viral.

So what does this debate mean for the consensus that was built around children's language development over the past two decades?

A Look at the Methodology

Starting in the early 1990s, a group of researchers led by Hart and Risley tracked 42 Kansas families-13 wealthy, 10 middle-class, 13 low-income, and six on public assistance-from the time their children were 7 to 9 months old until they turned 3 years old.

Every month, the researchers visited the families and collected data for an hour on the adult-child interactions they witnessed. Observers recorded the families' spoken interactions and took notes on the contexts for communication, guided by questions such as: Did caregivers participate in their children's play? How often did they speak with the children? How often did they encourage or reprimand?

After analyzing the resulting 1,300 hours of observations, Hart and Risley determined that an average child in the wealthier families heard more than 2,000 words directed their way during that hour of observation, while an average child in a family on public assistance heard closer to 600. By the time the children turned 4 years old, the researchers calculated, these differences in spoken language resulted in a gap of around 32 million words.

As an extension of the study, the researchers also continued gathering data on some of the children from the initial study as they got older, finding that the children's early language abilities predicted better language skills in elementary school. A later analysis backed those findings up, showing that children who had been exposed to more words early in life were more likely to have stronger language comprehension skills in kindergarten and greater vocabulary growth through elementary school.

New Questions About the Research

But critics of the 30 Million Word Gap warn that the study's sample of families was far too small to sustain the movement it spawned. They point to issues like the fact that only 29 of the initial 42 families were tracked as their children entered elementary school. In addition, critics like the Sperrys question whether there were larger issues of bias and systemic inequities at play that influenced their interpretations.

"Imagine you are an African American mother and you're living in a housing project," Douglas Sperry said in an interview. "When someone comes to videotape you, even though you have the best intentions and they have the best intentions, are you likely to be quiet or to talk a lot?"

Critics like Sperry and others also argue that the study favors White, upper- and middle-class speech norms and focuses exclusively on conversations between parent and child that do not account for cultural differences in how families interact and communicate. "We should be acknowledging up front that there are political differences and power differences between the language dialects that are spoken," said Sperry, who says the word gap study's cultural bias undervalues speech activities in diverse families.

For instance, while children appear to benefit from direct interactions with their parent or caregiver, there may be other linguistic skills to be gained from participating in different kinds of speech. To that end, in their recent study, the Sperrys tried to capture a wider language context for the young children in their study by measuring "bystander speech," or conversations within children's earshot that weren't directed at them.

Even that idea is contested turf, though. University of Delaware education professor and psychologist Roberta Golinkoff, lead author of a viral article defending the 30 Million Word Gap in the wake of the publication of the Sperrys' study, argues against including overheard speech in word gap calculations. "Young children are not capable of picking up that much from overheard speech," says Golinkoff, who has written a number of books (including a best seller) on children's language development. "You have to know more language to be able to do that."

A Story of Deeper Inequities

But critics of the original study and the movement that built around its framing generally agree that adults can boost the development of children in their care by speaking with them-and the bulk of research agrees.

A 2017 study collected speech data on 329 families using automated recorders tucked into children's clothing and found some gaps in children's early language experiences that were similar-but not identical-to the original 30 million word gap. Meanwhile, a 2018 study led by researchers at Harvard and Massachusetts Institute of Technology (MIT) used MRI technology to track how children's brains reacted while they listened to stories. They found that young children who had more frequent conversations with their caregivers had different neurological patterns than peers who didn't.

Research likewise shows that these early language development skills are important for children's longer-term academic success. A 2007 study found that gaps in children's language abilities in kindergarten account for the majority of the achievement gap between children from families of high and low socioeconomic status. And there has also been significant research linking early interventions like quality home visiting programs, child care, and pre-K with improved early language metrics and better long-term outcomes for children.

Yet Sperry and others emphasize that the research on language gaps in the early years can miss more fundamental takeaways about the deeper racial and economic inequities affecting a child's life living in poverty, referencing a body of research building since the 1960s that shows that a family's socioeconomic status and parental education levels heavily shape children's learning.

Indeed, researchers who defend Hart and Risley often agree and generally urge policy makers to do more than fund public service campaigns. Public policy needs to aggressively confront the deeper structural challenges that families face, while continuing to help adults understand the power of engaging in early conversations with young children. "There are horrible and remarkable inequities in American society," says Golinkoff, underscoring the point. "We will not be able to address things like the word gap with palliative kinds of intervention until we do something about these inequities."

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New Research Ignites Debate on the '30 Million Word Gap'

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For decades, the findings of the '30 Million Word Gap' have had a seismic impact on society. But recent research may call those findings into question.

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Social scientists have long understood that a child's environment - in particular growing up in poverty - can have long-lasting effects on their success later in life. What's less well understood is exactly how.

A new Harvard study is beginning to pry open that black box.

Conducted by Robert Sampson, the Henry Ford II Professor of the Social Sciences, and Robert Manduca, a doctoral student in sociology and social policy in the Graduate School of Arts and Sciences, the study points to a handful of key indicators, including exposure to high levels of lead, violence, and incarceration as key predictors of children's later success. The study is described in an April paper published in the Proceedings of the National Academy of Sciences.

"What this paper is trying to do, in a sense, is move beyond the traditional neighborhood indicators people use, like poverty," Sampson said. "For decades, people have shown poverty to be important ... but it doesn't necessarily tell us what the mechanisms are, and how growing up in poor neighborhoods affects children's outcomes."

To explore potential pathways, Manduca and Sampson turned to the income tax records of parents and approximately 230,000 children who lived in Chicago in the 1980s and 1990s, compiled by Harvard's Opportunity Atlas project. They integrated these records with survey data collected by the Project on Human Development in Chicago Neighborhoods, measures of violence and incarceration, census indicators, and blood-lead levels for the city's neighborhoods in the 1990s.

They found that the greater the extent to which poor black male children were exposed to harsh environments, the higher their chances of being incarcerated in adulthood and the lower their adult incomes, measured in their 30s. A similar income pattern also emerged for whites.

Among both black and white girls, the data showed that increased exposure to harsh environments predicted higher rates of teen pregnancy.

Despite the similarity of results along racial lines, Chicago's segregation means that far more black children were exposed to harsh environments - in terms of toxicity, violence, and incarceration - harmful to their mental and physical health.

"The least-exposed majority-black neighborhoods still had levels of harshness and toxicity greater than the most-exposed majority-white neighborhoods, which plausibly accounts for a substantial portion of the racial disparities in outcomes," Manduca said.

"It's really about trying to understand some of the earlier findings, the lived experience of growing up in a poor and racially segregated environment, and how that gets into the minds and bodies of children."

"What this paper shows ... is the independent predictive power of harsh environments on top of standard variables," Sampson said. "It's really about trying to understand some of the earlier findings, the lived experience of growing up in a poor and racially segregated environment, and how that gets into the minds and bodies of children."

The study isn't solely focused on the mechanisms of how poverty impacts children; it also challenges traditional notions of what remedies might be available.

"This has [various] policy implications," Sampson said. "Because when you talk about the effects of poverty, that leads to a particular kind of thinking, which has to do with blocked opportunities and the lack of resources in a neighborhood.

"That doesn't mean resources are unimportant," he continued, "but what this study suggests is that environmental policy and criminal justice reform can be thought of as social mobility policy. I think that's provocative, because that's different than saying it's just about poverty itself and childhood education and human capital investment, which has traditionally been the conversation."

The study did suggest that some factors - like community cohesion, social ties, and friendship networks - could act as bulwarks against harsh environments. Many researchers, including Sampson himself, have shown that community cohesion and local organizations can help reduce violence. But Sampson said their ability to do so is limited.

"One of the positive ways to interpret this is that violence is falling in society," he said. "Research has shown that community organizations are responsible for a good chunk of the drop. But when it comes to what's affecting the kids themselves, it's the homicide that happens on the corner, it's the lead in their environment, it's the incarceration of their parents that's having the more proximate, direct influence."

Going forward, Sampson said he hopes the study will spur similar research in other cities and expand to include other environmental contamination, including so-called brownfield sites.

Ultimately, Sampson said he hopes the study can reveal the myriad ways in which poverty shapes not only the resources that are available for children, but the very world in which they find themselves growing up.

"Poverty is sort of a catchall term," he said. "The idea here is to peel things back and ask, What does it mean to grow up in a poor white neighborhood? What does it mean to grow up in a poor black neighborhood? What do kids actually experience?

"What it means for a black child on the south side of Chicago is much higher rates of exposure to violence and lead and incarceration, and this has intergenerational consequences," he continued. "This is particularly important because it provides a way to think about potentially intervening in the intergenerational reproduction of inequality. We don't typically think about criminal justice reform or environmental policy as social mobility policy. But maybe we should."
This research was supported with funding from the Project on Race, Class & Cumulative Adversity at Harvard University, the Ford Foundation, and the Hutchins Family Foundation.

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Harvard study shows exactly how poverty impacts children's success

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Social scientists have long understood that a child's environment can have long-lasting effects on their success later in life. Exactly how is less well understood. A new Harvard study points to a handful of key indicators, including exposure to high lead levels, violence, and incarceration, as key predictors of children's later success.

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A child's genetic makeup influences their potential for cognitive development, including traits like intelligence and learning abilities

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How genetics and environment work together to shape our destiny

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How genetics and environment work together to shape our destiny

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How genetics and environment work together to shape our destiny: Milena Georgieva at TEDxAUBG (youtube.com)

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How genetics and environment work together to shape our destiny: Milena Georgieva at TEDxAUBG

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Case Study: Identical Twins Reared in Different Cultures | Psychology Today

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Case Study: Identical Twins Reared in Different Cultures

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Imagine being raised in a different culture and hw that would affect who you are,

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Understanding how and why twin studies are used is an important topic in biological psychology because they can give us important insights into the extent to which our behaviour is nature (genetics) or nurture.
Context

Is our behaviour a product of nature or nurture? In other words, are we born the way we are, or have we become this way through years of experiences? This is the classic question in psychology and one that Bouchard and his colleagues have attempted to answer since 1979 at their Minnesota Centre for Twin and Family Research (MICTFR) .

In this time, over 100 sets of reared-apart twins and triplets have participated in the research. Rigorous analysis of scores of data has enabled the researchers to draw some strong conclusions. The following "study" is similar to the Vietnam Head Injury Study ( read more here) in that it is one report from an ongoing longitudinal study.

Twin and kinship studies are used to determine heritability - the extent to which variations in behaviour can be attributed to genetic factors. e.g. 50% heritability for IQ means that differences in IQ are half (50%) due to genetics, and the other half is environment.

Monozygotic (identical) twins have 100% of their DNA in common, whereas dizygotic (fraternal) twins have on average 50% of their DNA in common. Twin studies compare similarities in behaviour between MZ and DZ twins. Comparing similarities in behaviour between MZ and DZ twins allows researchers to see the extent to which these variations are based on genetics.

In adoption studies (like the one below), the can also compare MZT twins (monozygotic together twins) - those who have been raised in the same household and those who have been raised apart (MZA). This allows researchers to compare the variable of the environmental influences on behaviour (because genetics is a constant variable between these two groups).

The behaviour we will focus on in this particular review is intelligence (IQ).

Methods

The researchers for this study (and continual studies) don't gather all their data at once, but continually, as they find participants to take part in the research. The participants come from mainly the UK and USA, but have also included Australia, Canada, China, New Zealand, Sweden and Germany.

They become involved either because:

the twins, a friend or family member finds out about the research

someone involved in the adoption process works with the MICTFR to put them in contact with the twin

Or...one twin becomes aware that they may have a twin somewhere and they contact the MICTFR and ask for their help in finding their separated twin.

Twin study by Bouchard et al. (1990)

For this study, the average age of the twins when they participate in this study was 41, which is important because most twin research prior to this focused on adolescents. The twins spent an average of 5 months together before being separated and reunited (on average) around 30 years of age.

Physical and psychological data was gathered in a number of different ways, which took around 50 hours. Methods were triangulated, either using researcher triangulation or methodological triangulation. For instance, when measuring IQ, three different IQ tests were used to gather and triangulate the data. And two different researchers conducted similar tests on the same participants. Controls were also established, like conducting the IQ tests on the twins at the same time but in different rooms under strict supervision by researchers.

To control for confounding variables in the environment, rigorous data was gathered on the childhood environments of the participants. For instance, a "Moos Family Environment Scale" was used to compare the impressions of the participants' childhoods and a questionnaire was given to measure access to physical facilities, such as material possessions and cultural, mechanical and scientific goods. For instance, were their dictionaries, artworks and power tools in the house when they were growing up? This type of data enables researchers to draw conclusions regarding socio-economic environment of the families and where the participants grew up.

Results

The analysis of the data revealed no significant difference between MZA twins (reared apart) and MZT twins (reared together) in regards to personality measures such as temperament, hobbies, interests, career pursuits or social attitudes.

Similar to previous research, this study also concluded that about 70% of differences between IQs in twins is due to genetic variation (70% heritability); the remaining 30% of difference is caused by environmental factors, which is similar to previous research.

There was also evidence from this study that suggested that twins that spend more time together after they are re-united are more similar. However, the data also suggested that it is the level of similarity between the twins that determines how much time they spend together, not the other way around.

Through their analysis of genetic and environmental variance, Bouchard et. al. concluded that genetics are an important factor in determining behaviour, but environment is also important. In addition to other research that suggests IQ similarities between children and adults increase over time , the researchers conclude that it can be our genetics that determines our environmental experiences. For example, if we have a naturally introverted disposition due to genetics, this will influence our psychological and personal experiences in life. If we think about neuroplasticity (the brain's ability to change as a result of experience), you should be able to see how our intelligence may be malleable and this malleability is caused by a combination of genetic and environmental factors.
Critical Thinking Questions

How do the results of this study show intelligence is influenced by genetics and/or our environment? ( Application)

What are some relevant ethical considerations particular to this study? ( Analysis)

What are the strengths and limitations of this longitudinal study? ( Evaluation)

References
Bouchard, Thomas J, Jr. Lykken, David T. McGue, Matthew. Segal, Nancy L and Tellegen, Auke. " Sources of Human Psychological Differences: The Minnesota Study of Twins Researched Apart." Sciences, New Series, Vol. 250 (1990), pp223-8. Accessed from web.missouri.edu
Video
The film "Three Identical Strangers" provides us with an interesting first-hand look into the world of psychological studies on twins separated at birth (link).

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Key Study: The Minnesota Twin Study of Twins Reared Apart | IB Psychology

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Understanding how and why twin studies are used is an important topic in biological psychology because they can give us important insights into the extent to which our behaviour is nature (genetics) or nurture. Context Is our behaviour a product of nature or nurture?

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The study of concentrated poverty and its effects on kids and social mobility has been a sociological topic for a long time, but new research published in Science Advances is among the first to explore neighborhood effects on cognitive development that considers the mediating role of neurotoxic air pollution exposure in early life.

Lead researcher Geoffrey Wodtke, associate professor in the Department of Sociology, said he was drawn toward the intersection of neighborhood effects research and environmental health epidemiology by previous research showing socio-economic gaps in child development. Those gaps emerge as early as 6 months of age and definitely by 2 years old-meaning they're pretty firmly entrenched before kids even start school.

"If differences in neighborhood poverty matter for the emergence of those gaps, something has to be going on in those neighborhoods that is relevant for babies and toddlers and preschoolers," he said. "But most sociological theories of neighborhood effects focus on things like peer socialization, school quality differences, and so forth, which are mainly relevant for older kids."

Research from a variety of disciplines points toward early childhood being a critical period, so the researchers sought to consider what differs between these neighborhoods that would have a relevant effect for such young children.

That led Wodtke, who is also the director of the Stone Center for Research on Wealth Inequality and Mobility, and his team to consider environmental health hazards, such as air pollution.

The researchers used data from the Early Childhood Longitudinal Study, Birth Cohort, from the U.S. Department of Education, which is a large, nationally representative sample of children born in 2001. The survey provides information about where kids live, in particular their zip code, which allowed the team to match that data with information about the socio-economic composition of their neighborhoods and other data on air quality from the Environmental Protection Agency.

The team found that living in a high-poverty neighborhood increases exposure to many different air toxics during infancy, that it reduces cognitive abilities measured later at age 4 by about one-tenth of a standard deviation, and that about one-third of this effect can be attributed to disparities in air quality.

"What's important is we provide some initial and relatively strong evidence that being born into a poor neighborhood harms early cognitive development, and this is at least partly due to exposure to neurotoxic air pollution," Wodtke said.

He also noted some surprising results as well, as the causal process connecting neighborhoods to air pollution-and air pollution to cognitive ability-is more complex than the team initially expected. For instance, kids in poor neighborhoods are much more likely to be exposed to traffic-related pollution like nitrogen dioxide and carbon monoxide, but actually less likely to be exposed to ozone.

"You have this complicated process where living in a poor neighborhood makes you more likely to breathe certain harmful things, but less likely to breathe other harmful things," Wodtke said. "When I went into the study, I had more of a simple idea of what was going on: that poor neighborhoods are going to uniformly suffer from greater exposure to all different kinds of air pollution. But that wasn't exactly the case."

As this is still a relatively immature area of research, Wodtke said we're likely a ways removed from concrete policy proposals, but said that it does point toward one possible class of interventions that could mitigate neighborhood effects, and such regulations would clean up the air and reduce exposures to air pollution.

Wodtke also coauthored a similar study on the effects of concentrated poverty and environmental lead contamination on early childhood development, published in the journal Demography. The paper used blood-lead surveillance data collected by the Chicago Department of Public Health.

"Lead is a dangerous, neurotoxic heavy metal that is especially harmful for very young kids and is highly unequally distributed across poor neighborhoods with older housing, deteriorating lead paint, and older and potentially corroding lead plumbing," Wodtke said. Poor neighborhoods also tend to be located closer to industrial facilities that emit lead into the air that settles on the ground where it stays indefinitely.

The study found that sustained exposure to disadvantaged neighborhoods reduces vocabulary skills during early childhood, and that this effect operates by way of a causal mechanism that involves lead contamination.

"What was surprising was just how big of a role lead seemed to play in the data I was analyzing," Wodtke said.
-Adapted from an article published by UChicago's Division of the Social Sciences.

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Higher air pollution in low-income areas affects early childhood development

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The study of concentrated poverty and its effects on kids and social mobility has been a sociological topic for a long time, but new research published in Science Advances is among the first to explore neighborhood effects on cognitive development that considers the mediating role of neurotoxic air pollution exposure in early life.

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Dell Technologies + Trains (youtube.com)

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Michael Yogman, Andrew Garner, Jeffrey Hutchinson, Kathy Hirsh-Pasek, Roberta Michnick Golinkoff, COMMITTEE ON PSYCHOSOCIAL ASPECTS OF CHILD AND FAMILY HEALTH, COUNCIL ON COMMUNICATIONS AND MEDIA, Rebecca Baum, Thresia Gambon, Arthur Lavin, Gerri Mattson, Lawrence Wissow, David L. Hill, Nusheen Ameenuddin, Yolanda (Linda) Reid Chassiakos, Corinn Cross, Rhea Boyd, Robert Mendelson, Megan A. Moreno, MSEd, Jenny Radesky, Wendy Sue Swanson, MBE, Jeffrey Hutchinson, Justin Smith; The Power of Play: A Pediatric Role in Enhancing Development in Young Children. Pediatrics September 2018; 142 (3): e20182058. 10.1542/peds.2018-2058

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The Power of Play: A Pediatric Role in Enhancing Development in Young Children

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Children need to develop a variety of skill sets to optimize their development and manage toxic stress. Research demonstrates that developmentally appropriate play with parents and peers is a singular opportunity to promote the social-emotional, cognitive, language, and self-regulation skills that build executive function and a prosocial brain.

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The High/scope Perry Preschool Study Through age 40

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The High/scope Perry Preschool Study Through age 40

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specialsummary col 06.07.indd (highscope.org)

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Teniel Sabin, Educación y Salud Mental Infantil

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Positive Interactions ⭐STARS⭐ - Serve and Return to build the brain🧠

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Supporting the development of your child's brain architecture

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Supporting the development of your child's brain architecture

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Supporting the Development of Your Child’s Brain Architecture Part 1B (youtube.com)

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Supporting the Development of Your Child’s Brain Architecture Part 1B

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What is epigenetics? - Carlos Guerrero-Bosagna

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What Twins Can Teach Us About Genetic and Environment Influences | Psychology Today

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What Twins Can Teach Us About Genetic and Environment Influences

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Studying twins provides insight into the brain, behavior, and child development.

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Genetics and intelligence differences: five special findings

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Genetics and intelligence differences: five special findings

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What is intelligence and why is it important?

Although there are many types of cognitive ability tests of individual differences, they almost all correlate substantially and positively; people with higher ability on one cognitive task tend to have higher ability on all of the others. Intelligence (more precisely, general cognitive ability or g, as discovered and defined by Spearman in 1904) indexes this covariance, which accounts for about 40 per cent of the total variance when a battery of diverse cognitive tests is administered to a sample with a good range of cognitive ability. As long as a battery of cognitive tests is diverse and reliable, a general 'factor' (often represented by the first unrotated principal component, which is not strictly a factor, but that is the terminology that is often used) indexing intelligence differences will emerge and correlate highly with such factors derived from other batteries using wholly different cognitive tests. The general intelligence component (factor) is a universally found statistical regularity, which means that some have tried to provide an epithet for what it might capture. According to one view, the core of this general intelligence factor is 'the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience' (Gottfredson et al. 21 p.13; see also Deary). Intelligence is at the pinnacle of the hierarchical model of cognitive abilities that includes a middle level of group factors, such as the cognitive domains of verbal and spatial abilities and memory, and a third level of specific tests and their associated narrow cognitive skills.

Intelligence is important scientifically and socially. Because intelligence represents individual differences in brain processes working in concert to solve problems, it is central to systems approaches to brain structure and function, and to the conceptualisation of how diverse cognitive abilities decline with age. It is also one of the most stable behavioural traits, yielding a correlation of 0.63 in a study of people tested at age 11 and then again at age 79. Socially, intelligence is one of the best predictors of key outcomes such as education and occupational status. People with higher intelligence tend to have better mental and physical health and fewer illnesses throughout the life course, and longer lives.

The rest of this review describes five genetic findings that are special to intelligence differences: dramatic increases in heritability during the life course, high genetic correlations among diverse cognitive abilities, high assortative mating, the positive genetics of high intelligence and the impact of intelligence on 'social epidemiology'. Most of these findings are not new, but highlighting these findings as special for intelligence is novel. Moreover, support for these findings has increased in recent years from traditional quantitative genetic research using the twin design that compares identical and fraternal twins, and, importantly, from a new quantitative genetic method that uses DNA alone to estimate overall genetic influence in large samples of unrelated individuals. This method, which we will refer to as Genome-wide Complex Trait Analysis (GCTA), is the first new human quantitative genetic method in a century, and is described in Box 1.

A new method for estimating genetic influence using DNA is a welcome addition to the armamentarium of quantitative genetics. The significance of the method is that it can estimate the net effect of genetic influence using DNA of unrelated individuals rather than relying on familial resemblance in groups of special family members such as monozygotic and dizygotic twins who differ in genetic relatedness. The method is often called GCTA, although its developers refer to it as Genomic-Relatedness-Matrix Restricted Maximum Likelihood. Other methods and modifications are also emerging.

Like other quantitative genetic designs such as the twin design, GCTA uses genetic similarity to predict phenotypic similarity. However, instead of using genetic similarity from groups differing markedly in genetic similarity such as monozygotic and dizygotic twins, GCTA uses genetic similarity for each pair of unrelated individuals based on that pair's overall similarity across hundreds of thousands of single nucleotide polymorphisms (SNPs) for thousands of individuals; each pair's genetic similarity is then used to predict their phenotypic similarity. Even remotely related pairs of individuals (genetic similarity greater than 0.025, which represents fifth-degree relatives) are excluded so that chance genetic similarity is used as a random effect in a linear mixed model. The power of the method comes from comparing not just two groups like monozygotic and dizygotic twins, but from the millions of pair-by-pair comparisons in samples of thousands of individuals. In contrast to the twin design, which only requires a few hundred pairs of twins to estimate moderate heritability, GCTA requires samples of thousands of individuals because the method attempts to extract a small signal of genetic similarity from the noise of hundreds of thousands of SNPs. A handy power calculator is available, which underlines the large samples needed for GCTA ( http://spark.rstudio.com/ctgg/gctaPower/).

GCTA detects only those genetic effects tagged by the common SNPs (allele frequencies typically much greater than 1%) that have until recently been incorporated in commercially available DNA arrays used in GWA studies. This limitation is changing as exome arrays became available in 2013 that included rare SNPs in or near exomes ( http://res.illumina.com/documents/products/datasheets/datasheet_human_core_exome_beadchip.pdf); the limitation will be lifted as whole-genome sequencing is more widely used. In addition, GCTA is limited to detecting the additive effects of SNPs; it cannot detect gene-gene or gene-environment interaction. Thus, GCTA heritability represents the upper limit for detection of SNP associations in GWA studies, which, like GCTA, are limited to detecting additive effects of common SNPs. Conversely, GCTA heritability represents the lower limit for heritability estimated in twin studies because twin studies can detect genetic influence due to DNA variants of any kind. In this way, the comparison between GCTA and twin study estimates of heritability reveals fundamental information about the genetic architecture of complex traits, including intelligence.

Similar to other complex traits, GCTA heritability estimates for intelligence are about half the heritability estimates from twin studies. This finding suggests that despite the modest yield so far from GWA studies of intelligence, with sufficiently large samples, it should in theory be possible to detect as much as half the heritability with the additive effects tagged by the common SNPs on currently available DNA arrays. The missing heritability gap between GCTA and twin studies is likely to be filled in part by less common DNA variants which will be detected as whole-genome sequencing comes on line.

The value of GCTA has been greatly increased by extending it beyond the univariate analysis of the variance of a single trait to the bivariate analysis of the covariance between two traits or the 'same' trait at two ages; a recent approach is multivariate rather than just bivariate. Bivariate GCTA was first applied to intelligence, yielding a high genetic correlation between intelligence scores in childhood and old age, as described in the text along with other examples of bivariate GCTA.

Heritability increases dramatically from infancy through adulthood despite genetic stability

It would be reasonable to assume that as we go through life, experiences-Shakespeare's 'whips and scorns of time'-have a cumulative effect on intelligence, perhaps overwhelming early genetic predispositions. However, for intelligence, heritability increases linearly, from (approximately) 20% in infancy to 40% in adolescence, and to 60% in adulthood. Some evidence suggests that heritability might increase to as much as 80% in later adulthood but then decline to about 60% after age 80.

Most genetic research has been consistent with this dramatic increase in heritability for intelligence in the early human life course. shows the results of the first study to demonstrate significant increases in heritability in cross-sectional analyses of 11 000 twin pairs from childhood (~40%) to adolescence (~50%) to young adulthood (~60%). The non-overlapping standard errors across the three ages indicate that the increases in heritabilities are significant. Although these findings have been criticised because they rely on cross-sectional comparisons (Mackintosh p. 278), similar results showing increases in heritability have been found in longitudinal adoption studies as well as in longitudinal twin studies from early to middle childhood and from middle childhood to adolescence. Although GCTA can be used to test this finding of increasing heritability across development, the first two attempts to do so using longitudinal data did not have sufficient power to detect the hypothesised age differences in GCTA heritability. One study reported an increase in GCTA heritability of intelligence from 0.26 (0.17 standard error) at age 7 to 0.45 (0.14) at age 12. Another study reported a decrease in GCTA heritability from 0.48 (0.18) at age 11 to 0.28 (0.18) in old age. Given the differences in the ages tested in these latter two studies, they are not directly comparable. As indicated by the large standard errors, larger longitudinal studies are needed.

Why does the heritability of intelligence increase so dramatically from childhood to adulthood, as seen in twin studies? A clear yet apparently contradictory finding constrains possible answers to this question. Despite this great increase in heritability, the same genes affect intelligence from age to age. For example, a recent twin study reported a genetic correlation of 0.75 (standard error=0.08) from age 7 to age 12, despite increasing heritability from 0.36 (0.03) to 0.49 (0.04) and despite mean changes in brain structure and function from childhood to adolescence. GCTA analyses in the same study but using unrelated individuals yielded a highly similar genetic correlation of 0.73 (0.29) from age 7 to age 12. Most strikingly, a 60-year longitudinal study of intelligence, which was the first application of bivariate GCTA, yielded a genetic correlation of 0.62 (0.22) from age 11 to 69.

Thus, the question becomes, why does the heritability of intelligence increase during development despite strong genetic stability from age to age? That is, the same genes largely affect intelligence across the life course and yet genes account for more variance as time goes by. Increasing heritability despite genetic stability implies some contribution from what has been called genetic amplification. This has recently been supported in a meta-analysis of 11500 twin and sibling pairs with longitudinal data on intelligence that found that a genetic amplification model fit the data better than a model in which new genetic influences arise with time. Genotype-environment correlation seems the most likely explanation in which small genetic differences are magnified as children select, modify and create environments correlated with their genetic propensities. This active model of selected environments-in contrast to the traditional model of imposed environments-offers a general paradigm for thinking about how genotypes become phenotypes.

Intelligence indexes general genetic effects across diverse cognitive and learning abilities

Another special genetic feature of intelligence is that its differences are caused by genes that affect cognitive abilities as diverse as, for example, spatial ability, vocabulary, processing speed, executive function and memory. Most of the genetic action lies with these general (highly pleiotropic) effects, captured by intelligence, rather than effects specific to each ability, leading to a Generalist Genes Hypothesis. This is a surprising finding because very different neurocognitive processes appear to be involved in such cognitive abilities. Although these genetic correlations put intelligence at the pinnacle of the hierarchical model of cognitive abilities mentioned earlier, there is also genetic specificity that builds the genetic architecture for the rest of the hierarchical structure of group factors and specific tests.

In a meta-analysis of 322 studies, the average correlation among individual diverse cognitive tests is about 0.3. Genetic correlations among cognitive tests are typically greater than 0.6, indicating that the same genes are responsible for the heritabilities of these tests. Genetic correlations estimate the extent to which genetic effects on one trait are correlated with genetic effects on another trait independently of the heritabilities of the two traits. They can be thought about roughly as the probability that genes associated with one trait are also associated with the other trait. Genetic correlations are derived from the genetic analysis of covariance between traits using the same quantitative genetic methods used to analyse variance.

These general genetic effects permeate not only cognitive abilities such as spatial and vocabulary that are used as part of the assessment of intelligence but also extend to education-related learning abilities such as reading and arithmetic. shows the results of a multivariate genetic analysis of 14 tests that comprise four distinct test batteries-intelligence, reading, mathematics and language-for more than 5000 pairs of 12-year-old twins. The genetic correlations (and 95% confidence intervals) between intelligence and learning abilities are uniformly high: 0.88 (0.84-0.92) with reading, 0.86 (0.81-0.90) with mathematics and 0.91 with language (0.87-0.94). Weighting these genetic correlations by the heritabilities of the latent factors, it can be shown that about two-thirds of the phenotypic correlations between the factors can be explained genetically. One advantage of using such latent factors is that they exclude uncorrelated measurement error. As a result, these genetic correlations are higher than those found when uncorrected composite scores rather than latent factors are analysed: 0.66 (0.05 standard error) for reading, 0.73 (0.03) for mathematics and 0.80 (0.06) for language.

The first attempts to use bivariate GCTA (see Box 1) to verify these twin findings support the hypothesis of general genetic effects on broad cognitive and learning ability-related differences. The GCTA estimates of genetic correlation (and standard error) between intelligence and learning abilities are highly similar to the twin study estimates just mentioned for composite scores uncorrected for error: 0.89 (0.26) for reading, 0.74 (0.15) for mathematics and 0.81 (0.15) for language, estimated from unrelated individuals from the same sample. Within intelligence, the major group factors of verbal and nonverbal ability yielded a genetic correlation of 1.0 (0.32) in a bivariate GCTA in the same sample. The high GCTA genetic correlation between verbal and nonverbal based on unrelated individuals supported the twin study estimate of 0.60 (0.09) in the same study.

An important feature of bivariate GCTA is that it yields genetic correlations similar to genetic correlations estimated from the twin method, even though heritabilities are considerably lower for GCTA than for twin estimates. In the study just mentioned, GCTA heritabilities were consistently lower than twin heritabilities: 0.35 vs 0.47 for intelligence, 0.16 vs 0.59 for reading, 0.32 vs 0.48 for mathematics and 0.35 vs 0.41 for language. As noted in Box 1, GCTA heritability estimates are limited to the additive effects tagged by the common single nucleotide polymorphisms (SNPs) used on DNA arrays (i.e., the direct effects of the SNPs on the array and those variants with which they are in linkage disequilibrium); GCTA heritability is lowered by imperfect tagging of causal SNPs. As a result, GCTA heritability estimates are typically about half the heritability estimates from twin studies. This 'missing GCTA heritability' is due in part to non-additive effects and the effects of rarer DNA variants. Why then are GCTA estimates of genetic correlation so similar to twin study estimates? The likely reason is that the GCTA estimate of the genetic correlation is derived from the ratio between genetic covariance and the genetic variances of the two traits. Because GCTA's underestimation of genetic influence applies to genetic covariance as well as to genetic variance, the ratio between genetic covariance and genetic variance cancels out this bias, leaving an unbiased GCTA estimate of genetic correlation.

This finding of strong genome-wide pleiotropy across diverse cognitive and learning abilities, indexed by general intelligence, is a major finding about the origins of individual differences in intelligence. Nonetheless, this finding seems to have had little impact in related fields such as cognitive neuroscience or experimental cognitive psychology. We suggest that part of the reason for this neglect is that these fields generally ignore individual differences. Another reason might be that the evidence for this finding rested largely on the twin design, for which there have always been concerns about some of its assumptions; we judge that this will change now that GCTA is beginning to confirm the twin results.

This finding of strong genome-wide pleiotropy across diverse cognitive and learning abilities is compatible with multiple neurocognitive models of causal pathways. The modularity model of cognitive neuroscience might suggest that genetic correlations among cognitive abilities are epiphenomenal in the sense that multiple genetically independent brain mechanisms could affect each ability, creating genetic correlations among abilities. However, the genetic principles of pleiotropy (each gene affects many traits) and polygenicity (many genes affect each trait) lead us to predict that generalist genes have their effects further upstream, creating genetic correlations among brain structures and functions, a prediction that supports a network view of brain structure and function.

In summary, multivariate genetic research-both from twin studies and GCTA-suggests that most of the genetic action is general across diverse cognitive abilities rather than specific to each ability. Intelligence is a good target for gene-hunting because it indexes these generalist genes.

Assortative mating is greater for intelligence than for other traits

Although the phenotypic correlation between spouses, assortative mating, might seem an esoteric topic, it has important implications for the genetic architecture of intelligence. Assortative mating is far greater for intelligence than for most other traits. For example, assortative mating is about 0.20 for height and for weight, and about 0.10 for personality. For intelligence, assortative mating is about 0.40. Moreover, verbal intelligence shows greater assortative mating (~0.50) than nonverbal intelligence (~0.30), perhaps because it is easier to gauge someone's verbal ability such as vocabulary than their nonverbal intelligence such as spatial ability. Assortative mating for intelligence is caused by initial selection of a mate (assortment) rather than by couples becoming more similar to each other after living together (convergence). In part, spouses select each other for intelligence on the basis of education-spouses correlate about 0.60 for years of education-which correlates about 0.45 with intelligence. Assortative mating may be greater than it is for intelligence for a few other traits such as social attitudes, smoking and drinking, although these traits might be affected by convergence. It should also be noted that not all of the genetic variance for intelligence is additive. For example, dominance, which involves interaction among alleles at a locus, is indicated by research showing inbreeding depression for intelligence. When assortative mating is taken into account in variance components analysis, some evidence for nonadditive genetic variance emerges.

The significance of high assortative mating for intelligence is that assortative mating for polygenic traits increases additive genetic variance. Additive genetic variance refers to the independent effects of alleles or loci that 'add up', in contrast to non-additive effects of dominance within a locus, and epistasis across loci in which the effects of alleles or loci interact. Assortative mating of parents increases additive genetic variance in their offspring because offspring receive a random sampling of half of each parent's genes and resemble their parents to the extent that each allele shared with their parents has an average additive effect. Because offspring inherit only one of each of the parents' pairs of alleles, offspring differ from their parents for non-additive interactions.

For example, if spouses mated randomly in relation to intelligence, highly intelligent women would be just as likely to mate with men of low as high intelligence. Offspring of the matings of women of high intelligence and men of low intelligence would generally be of average intelligence. However, because there is strong positive assortative mating, children with highly intelligent mothers are also likely to have highly intelligent fathers, and the offspring themselves are likely to be more intelligent than average. The same thing happens for less intelligent parents. In this way, assortative mating increases additive genetic variance in that the offspring differ more from the average than they would if mating were random. The increase in additive genetic variance can be substantial because its effects accumulate generation after generation until an equilibrium is reached. For example, if the heritability of intelligence with random mating were 0.40, the additive genetic variance of intelligence would increase by one-quarter at equilibrium given assortative mating of 0.40, Falconer and MacKay equation 5, Table 10.6, p. 176.

The extra additive genetic variance for intelligence induced by assortative mating is important for three genetic reasons. First, parents share only additive genetic variance with their offspring, so that genetic predictions from parent to offspring ought to be greater for intelligence when polygenic scores, composite scores based on associations of many loci with intelligence, are available. Second, because GCTA has so far been limited to detecting additive genetic variance, GCTA heritability should be greater for intelligence than for traits that show less assortative mating such as personality. Some evidence supports this prediction in that GCTA heritability estimates for personality appear to be much lower than for intelligence, even taking into account the lower twin-study heritability estimates for personality than for intelligence. Moreover, GCTA heritability estimates are greater, although not significantly so, for verbal than non-verbal intelligence, which is consistent with the greater assortative mating for verbal than non-verbal intelligence. Third, because both GWA and GCTA are limited to detecting additive genetic variance, the GCTA estimate of substantial additive genetic influence on intelligence makes intelligence a good target for GWA studies.

Two additional points about assortative mating for intelligence warrant mention. First, unlike inbreeding, which reduces heterozygosity across the genome, assortative mating is trait specific-it increases additive genetic variance (changing genotypic frequencies but not allelic frequencies) only for genes associated with the trait for which mates assort and its genetically correlated traits. Second, assortative mating induces a genetic correlation between mates for a particular trait to the extent that the trait is heritable, regardless of whether assortative mating is driven by genetic assortment or by environmental factors such as propinquity. A recent study using genome-wide genotypes showed that spouses are more genetically similar than two individuals chosen at random. This DNA estimate of genetic similarity between spouses is substantially less than assortative mating for education levels, suggesting that assortative mating may be driven by 'social sorting processes in the marriage market'.

Thinking positively: the genetics of high intelligence

Unlike psychiatric and other disorders, intelligence is normally distributed with a positive end of high performance as well as a problematic end of intellectual disability. High intelligence is responsible for exceptional performance in many societally valued outcomes, as documented in long-term longitudinal studies. Although many other traits, such as those related to athletic performance, are also normally distributed, the importance of high intelligence makes it especially interesting. Genetic exploration of the positive tail of normally distributed traits is important conceptually because it moves away from the notion that we are all the same genetically except for rogue mutations that cause disorders, diseases and disabilities.

Quantitative genetic research on intelligence indicates that the genetic causes of high intelligence are quantitatively, not qualitatively, different from the rest of the distribution. A recent study of 11000 twin pairs found that the top 15% of the intelligence distribution was just as heritable (0.50) as the rest of the distribution (0.55). Most recently, in a study of 370 000 sibling pairs and 9000 twin pairs in Sweden from 3 million 18-year-old males whose intelligence was assessed as part of compulsory military service, not only was high intelligence (top 4%) just as familial and heritable as the rest of the distribution, a method called DF extremes analysis suggested that the same genetic factors are at work. DF extremes analysis focuses on the genetic causes of the average difference between an extreme group, however defined, and quantitative trait scores for the population, comparing the differential regression to the population mean for the co-twins of identical and fraternal twin probands. To the extent that genetics is found to account for this average difference (called 'group' heritability), it implies that there is a high genetic correlation between the extreme group and the quantitative trait. In the Swedish study, DF extremes analysis showed that genetics explained about half of the mean difference between the high-intelligence group and the rest of the distribution, which was similar to the traditional heritability of individual differences and implies strong genetic links between high intelligence and normal variation in intelligence.

It is possible that scores more extreme than the top 4% of the intelligence distribution are aetiologically different from the normal distribution, which has been called the Genetic Discontinuity Hypothesis. The most persuasive argument for genetic discontinuity for extremely high intelligence was made by David Lykken who noted that a key problem of genius is 'its mysterious irrepressibility and its ability to arise from the most unpromising of lineages and to flourish even in the meanest of circumstances' (Lykken p. 29). Lykken proposed that genius emerges from unique combinations of genes; he referred to these higher-order nonadditive (epistatic) interactions as emergenic. The emergenesis hypothesis does not necessarily predict that different genes affect high intelligence, but it does predict that genetic effects are non-additive for high intelligence. The hallmark of an epistatic trait is one for which identical twins are more than twice as similar as fraternal twins. However, in the two twin studies described above, high intelligence did not show this pattern of twin results and model-fitting analyses found that all genetic influence was additive for high intelligence as well as for the entire distribution of intelligence. Although these results do not support the Discontinuity Hypothesis, the studies were limited to the top 15% and top 4% of the intelligence distribution, which is far short of the extremes of genius, which Galton benchmarked as the top 0.1%.

The aetiology of high intelligence is also interesting in comparison to intellectual disability. Similar to high intelligence, most intellectual disability is the low end of the normal distribution of intelligence. This has been shown most recently in the Swedish conscript sample mentioned above, with results replicated in a similarly large conscript sample in Israel. However, extremely severe intellectual disability appears to be aetiologically distinct, as proposed by Lionel Penrose in 1938 and confirmed in the Swedish and Israeli studies. One critical piece of evidence is that siblings of persons with severe intellectual disability have an average intelligence quotient (IQ) near 100 whereas siblings of persons with mild intellectual disability have an average IQ of about 85, about one standard deviation below the population mean. The absence of genetic links between severe intellectual disability and normal variation in intelligence fits with current molecular genetic research that finds noninherited de novo mutations associated with severe intellectual disability.

An hypothesis to integrate these genetic results for the low and high ends of intelligence is this: Normal development of intelligence can be disrupted by any of many mutations including non-inherited de novo mutations as well as prenatal and postnatal trauma, but high intelligence requires that everything works right, including most of the positive alleles and few of the negative alleles associated with intelligence. This hypothesis is the rationale for a recent genome-wide case-control association study for cases with extremely high intelligence (IQ>150). However, one study has found no association between rare SNPs and intelligence in the normal range of intelligence. In addition, several studies have found no association between copy-number variants, which are typically rare variants, and intelligence in the normal range, although such studies may have been underpowered both in terms of sample and difficulties in assessing copy-number variants.

Although the normal phenotypic distribution of intelligence makes it an obvious target for investigating the high as well as low extremes, the larger significance of positive genetics for psychiatric genetics is that polygenic scores created from GWA studies of psychiatric disorders will be normally distributed, which means that there is a positive end with just as many people as the negative end. This implies that at the level of DNA variation there are no common disorders, only normally distributed quantitative traits. It also raises the question of who these people are at the positive end of the polygenic distribution of 'risk' for psychological and other traits. Are they merely individuals at low risk for problems or do they have special powers? Thinking positively begins by thinking quantitatively-about 'dimensions' rather than 'disorders' and about genetic 'variability' rather than genetic 'risk'.

Intelligence brings (some) genetics to 'social' epidemiology

It has long been known that intelligence, education and class are correlated. The causes of these associations and their relative contribution to social mobility is much disputed. Education and social class are also well-established associates of health inequalities, including all-cause mortality. However, intelligence is a new player in health; its associations with many health and illness outcomes and all-cause and several specific causes of mortality have been discovered in the last decade or so.

We shall explain in this section that, akin to, but broader than cognitive and learning abilities, intelligence shares genetic causes with education and social class, which are touchstone 'environmental' variables of diverse social scientists. Major human phenomena studied by these social scientists are social mobility and health inequalities, which are unarguably important. They are studied by sociologists, epidemiologists and economists. Finding out why some people more than others make positive progress in their social position through the life course, and why some people are more prone to illnesses and early death have drafted in the two favourite 'environmental' social science variables of education and social class. Education and parental social class are predictors of people's social position in adulthood. Both, and the person's own adult social class, are associated with health, illness and mortality: less educated people and those in less professional jobs tend to die earlier. However, there is a third variable in social mobility research, and a third variable in health inequalities research: intelligence. Both education and social class are substantially correlated with intelligence.

Education and social class (which is indexed by occupation, or income, or by the relative deprivation-affluence of where a person lives) are often assumed to be indicators of a person's environmental influences, but they are correlated with intelligence, which has a high heritability. Indeed, epidemiologists even use height-shorter stature is associated with earlier mortality-as an indicator of childhood social-environmental influences, though it has high heritability. For example, a recent social epidemiology article described height 'as a marker of early life insults'. Here, we emphasise that it is an empirical question rather than something that can be assumed a priori as to whether the three key variables in social mobility and health inequalities research-education, social class and intelligence-correlate because of shared genetic and/or environmental causes.

Twin and family studies have shown that educational attainment and social class are somewhat heritable. For example, the pedigree-based estimates of heritability (here as percentages of phenotypic variance explained) in the Generation Scotland family-based study of over 20 000 people were 54% (s.e.=2%) for general intelligence, 41% (2%) for education and 71% (1%) for social deprivation using the Scottish Index of Multiple Deprivation. The genetic correlation was 0.65 (s.e.=0.02) between intelligence and education, 0.40 (0.02) between intelligence and deprivation and 0.48 (0.02) between education and deprivation. An earlier report on a smaller sample ( N>6000) of the same study found genetic correlations between intelligence and being physically active outside work (0.25), fruit and vegetable intake (0.23), ever smoking (0.45), smoke exposure (0.53) and income (0.45), with high bivariate heritabilities for all of these. Another study identified over 2500 pairs of school-age twins from population samples totalling over 300 000 in England and the Netherlands and found moderate to large genetic correlations and bivariate heritability between intelligence and national examination results in language, mathematics and science. Analyses of older Danish twins found evidence for genetic correlation between cognitive ability and education and health.

GCTA studies have recently explored the heritability and genetic correlations of intelligence, education and social class. A combined analysis of Swedish and Australian unrelated subjects ( N~11 500) used GCTA to provide an estimate of 22% (s.e.=4%) for the heritability of years in education and 25% (8%) for attending college. In the Twins Early Development Study for 3000 unrelated children, GCTA-based estimates of heritability were 21% (12%) for parental social class and 28% (17%) for children's IQ at age 7 and 32% (14%) at age 12. The GCTA-estimated genetic correlation between parental social class and IQ was 1.00 (s.e.=0.47) at age 7 and 0.66 (0.31) at age 12. GCTA-based estimates of heritability on over 6500 unrelated people with genome-wide SNP data in the Generation Scotland study were 29% (5%) for general intelligence, 21% (5%) for education and 18% (5%) for social deprivation. The genetic correlations were 0.95 (0.13) for intelligence and education, 0.26 (0.16) for intelligence and deprivation, and 0.45 (0.18) for education and deprivation. Therefore, some of the variance in the social scientists' key environmental variables can be found in DNA variation, some of which is shared with the DNA variation that causes some of people's differences in intelligence. Another 'environmental' social science variable, height, shows a similar set of findings in the Generation Scotland study sample. The GCTA-estimated heritability of height was 58% (5%), its phenotypic correlation with intelligence was 0.16, the GCTA-based genetic correlation was 0.28 (0.09), and the bivariate heritability was 71%. Bivariate GCTA-derived genetic correlations between intelligence and health variables will require large numbers which are rare, as yet. An analysis of data from the Swedish Twin Registry ( N=5650 unrelated individuals) found GCTA-derived genetic correlations of 0.13 (s.e.=0.23) and 0.33 (s.e.=0.33) between self-rated health and, respectively, years in education and attending college).

The genetics of intelligence has a special place, therefore, in the heretofore-named 'social' epidemiology. Indeed, these new findings from twin/family-based and GCTA-based studies give a corrective to the suggestion that 'cognitive epidemiology' be re-named 'social epidemiology'. Singh-Manoux's suggestion was partly made because epidemiologists preferred to use cognitive epidemiology for those studies in which cognition was the outcome, and so there was an objection to Deary and Batty's (2007) definition, that is, 'the use of cognitive ability test scores as risk factors for human health and disease outcomes, including mortality'. Relevant to the genetic associations discussed in this section was Singh-Manoux's further discussion,

'Given the association between intelligence and education, extensively discussed by Deary and Johnson, this definition of cognitive epidemiology puts it squarely in the domain of social epidemiology, a discipline concerned with the social distribution of determinants of health. Location in this broader church, rather than the micro-discipline of cognitive epidemiology, will avoid a narrow focus on intelligence that ignores its associations with markers of social position such as education, income and occupation.'

One might say in reply that this conceptualisation ignores possible genetic contributions to social/cognitive epidemiology. To sum up: there are genetic causes of some of the educational and social class differences in the populations studied, and these overlap with the genetic causes of intelligence differences. Intelligence genetics is special here, because it offers the possibility of finding some of the connections between social and medical outcomes, perhaps via genetic contributions to system integrity, allostatic load and the adoption of health-promoting/reducing behaviours.

Five special findings and polygenic scores

These five special findings about the genetics of intelligence differences have emerged from traditional quantitative genetic research, primarily twin studies, and they are beginning to be replicated using GCTA. However, nothing would advance the field more than moving beyond GCTA to G, C, T, and A-that is, identifying specific DNA variants that contribute to the high heritability of intelligence. As is the case for all complex traits and common disorders in the life sciences, we now know that this will be a difficult task. As discussed earlier, GWA studies have shown that there are no large effect sizes in the population, which implies that the heritability of intelligence is caused by thousands of DNA variants, many of these effects are likely to be infinitesimal or even idiosyncratic. Nonetheless, GCTA has shown that additive effects of common SNPs can theoretically account for at least half of the heritability of intelligence, which means that a brute force approach using ever larger samples will identify some of these genes. In addition, whole-genome sequencing will identify DNA variants of any kind anywhere in the genome, not just common SNPs.

Associations of small effect size between DNA variants and intelligence can be summed across multiple loci to create a polygenic score, which is analogous to aggregating items to create a scale. Polygenic scores can aggregate a few candidate SNPs or thousands of SNPs across the genome, called genome-wide polygenic scores (GPS), as described in Box 2.

There are at least a dozen labels to denote such polygenic scores, most of which involve the word 'risk', such as polygenic risk scores, but we prefer the term polygenic score because it makes more sense for quantitative traits like intelligence with positive as well as negative poles. A polygenic score is created by adding genotypic values across loci. For example, for one locus (A) with two alleles (A1 and A2), with the A2 allele associated with higher intelligence scores, additive genotypic values can be assigned for locus A so that A1A1 individuals=0, A1A2 individuals=1 and A2A2 individuals=2. For each individual, these 0, 1 and 2 additive genotypic values for locus A can be added to those for locus B to create a polygenic score that varies from 0 to 4, and so on for dozens, hundreds or thousands of loci. A refinement is to weight each loci by the strength of its association with intelligence. For example, 100 associations that each account for 0.1% of the variance of intelligence on average could together account for 10% of the variance. Their effects should add up because DNA variants are uncorrelated unless they are very close together on a chromosome. Any loci could be aggregated to create a polygenic score, such as candidate genes thought to be associated with a trait. However, the most productive use of polygenic scores is to aggregate genotypic scores for DNA variants (usually SNPs) known to be associated with a trait. For example, much research on body weight has used a polygenic score based on 32 SNPs that have shown replicated associations with body mass index, even though this polygenic score only accounts for 2% of the heritability of body mass index.

A more recent variant of polygenic scores goes beyond aggregating a few dozen individual SNPs associated with a trait to include thousands of SNPs from GWA studies in a genome-wide polygenic score (GPS) that includes thousands of SNPs or even all SNPs on a DNA array weighted by the strength of their association. The idea is that a GPS will be enriched for positive associations even though the GPS will certainly include false-positive associations. Although GPS can theoretically account for all the heritability shown in GCTA, GWA studies of intelligence and other traits have resulted in GPS that fall far short of GCTA estimates of heritability. For intelligence in childhood, a GPS derived from a discovery sample of more than 12 000 children accounted for about 2% of the variance in independent samples of more than 5000. For intelligence in adulthood, about 1% of the variance was explained by a GPS derived from an adult sample of 3200 individuals and tested in an independent sample of 670 individuals, even though GCTA estimates of heritability are about 30%. Using years of education rather than intelligence per se, the meta-analysis mentioned earlier with 125 000 individuals in a discovery sample yielded a GPS that accounted for 2 and 3% in two independent samples. More variance in intelligence is likely to be explained with GPS derived from larger samples, whole-genome sequencing and more novel strategies such as using networks of functionally linked genes.

Having a GPS for intelligence that reliably accounts for even as little as a few percent of the variance will enhance research on intelligence. It will enable DNA analyses at the level of individuals rather than families which can address the major questions of quantitative genetic research such as developmental, multivariate and GE interplay issues mentioned earlier. Polygenic scores can be used in the same way that candidate genes have been used. A neuroscientist might not find a polygenic score useful for investigating molecular pathways between genes and behaviour through the brain, except perhaps to emphasise the need for a network approach governed by pleiotropy (each gene affects many traits) and polygenicity (each trait is affected by many genes). A GPS for intelligence would be like the other GPS (global positioning system) making it possible to triangulate on the genetics of intelligence from all domains of the life sciences, for example, integrating research on the genetics of intelligence from the genome, epigenome, transcriptome, proteome and metabalome to the brain and behaviour without the need to assess intelligence.

Anticipating that GPS will be available for research on intelligence, we close by revisiting the five special findings about genetics and intelligence, drawing hypotheses that can tested using a GPS for intelligence, an exercise that we hope will help to make the five special findings more concrete.

Heritability of intelligence increases dramatically from infancy through adulthood despite genetic stability

GPS hypotheses follow directly from the finding that the heritability of intelligence increases throughout the life course despite strong genetic stability from age to age: Variance explained by a GPS should increase with age, and a GPS discovered at one age, adulthood for example, is expected to predict intelligence at other ages such as childhood.

Intelligence indexes general genetic effects across diverse cognitive and learning abilities

A GPS hypothesis follows directly from finding strong genome-wide pleiotropy across diverse cognitive abilities: A GPS that is discovered for any cognitive or learning ability should also predict any other ability. Also, a GPS for intelligence should predict better than a GPS for any other trait. It has been suggested that a pleiotropic GPS that explictly targets the substantial covariance among diverse cognitive and learning abilities will be even better than a GPS based on a single composite measure of intelligence.

Assortative mating is greater for intelligence than for any other trait

GPS support for the previous two hypotheses seems likely because preliminary GCTA results discussed above already provide some support for these hypotheses. In the case of assortative mating, GPS could provide a novel test of the extent to which assortative mating for intelligence is mediated genetically by correlating GPS between spouses. Another question that emerges from previous genetic research is whether GPS assortative mating is greater for verbal than for nonverbal ability.

Thinking positively: the genetics of high intelligence

Finding that the same genes affect high intelligence to the same extent as the rest of the normal distribution leads to the hypothesis that a GPS for intelligence from unselected samples can also be used to predict high intelligence.

Intelligence brings (some) genetics to 'social' epidemiology

Finding that, in twin and GCTA studies, the same genes influence intelligence and social epidemiologists' 'environmental' variables of education, social class, and height can enlighten research in health and social inequalities. It leads to the hypothesis that GPS scores for intelligence might contribute to health outcomes and mortality, and that these might account for some of the associations between education and class and mortality.

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Genetics and intelligence differences: five special findings

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Although there are many types of cognitive ability tests of individual differences, they almost all correlate substantially and positively; people with higher ability on one cognitive task tend to have higher ability on all of the others.

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Abstract

Cognitive ability and educational success predict positive outcomes across the lifespan, from higher earnings to better health and longevity. The shared positive outcomes associated with cognitive ability and education are emblematic of the strong interconnections between them. Part of the observed associations between cognitive ability and education, as well as their links with wealth, morbidity and mortality, are rooted in genetic variation. The current review evaluates the contribution of decades of behavioural genetic research to our knowledge and understanding of the biological and environmental basis of the association between cognitive ability and education. The evidence reviewed points to a strong genetic basis in their association, observed from middle childhood to old age, which is amplified by environmental experiences. In addition, the strong stability and heritability of educational success are not driven entirely by cognitive ability. This highlights the contribution of other educationally relevant noncognitive characteristics. Considering both cognitive and noncognitive skills as well as their biological and environmental underpinnings will be fundamental in moving towards a comprehensive, evidence-based model of education.

Keywords: cognitive ability, academic performance, noncognitive skills, behavioural genetics, education

Education is one of the major investments undertaken by contemporary society and the level of educational attainment has been steadily increasing worldwide ( OECD, 2018). Educational attainment is a measure of human capital and is indicative of the skills of a population. As countries' economies gradually shift away from mass production towards becoming knowledge economies, governments are eager to increase the skills and welfare of the population through educational attainment ( OECD, 2018). Higher levels of educational attainment are associated with higher employment rates, better job prospects and higher earnings ( Furnham & Cheng, 2016; Oreopoulos & Salvanes, 2011; Ritchie & Bates, 2013).

The positive life outcomes associated with educational attainment extend far beyond wealth and professional success, to include physical and mental health, wellbeing and even longevity ( Cutler & Lleras-Muney, 2012; Montez & Hayward, 2014). Similar long-term positive associations with health and wealth are observed for general cognitive ability: higher cognitive skills have been linked to higher earnings ( Daly, Egan, & O'Reilly, 2015; Kalechstein, Newton, & van Gorp, 2003), better physical and mental health ( Batty, Deary, & Zaninotto, 2016; Baune et al., 2010; Latvala, Kuja-Halkola, D'Onofrio, Larsson, & Lichtenstein, 2016; Mollon, David, Zammit, Lewis, & Reichenberg, 2018; Snyder, Miyake, & Hankin, 2015) and lower mortality ( Deary, Weiss, & Batty, 2010). The shared positive life outcomes associated with cognitive ability and educational attainment are likely to be intrinsically linked via the strong connection between these two traits. Indeed, extant research has identified general cognitive ability as the major source of variation in academic performance, measured as both school achievement and how long people spend in education -i.e. educational attainment ( Krapohl et al., 2014; Mackintosh & Mackintosh, 2011).

Part of the observed associations between cognitive ability and education, as well as their links with wealth, morbidity and mortality, are rooted in genetic variation. Converging evidence from decades of twin research and, more recently, molecular genetic studies has shown that general cognitive ability and educational attainment are heritable, highly polygenic, and that shared genetic factors account for part of their observed covariation ( Deary, Harris, & Hill, 2019; Hill et al., 2018; Lee et al., 2018; Plomin & Von Stumm, 2018; Rimfeld et al., 2018; Tucker-Drob & Briley, 2014). While there are several parallels in the development and manifestations of cognitive ability and educational attainment, emblematic of which is their close association across the lifespan, there are also key distinctions that characterize the development, origins and expression of both traits.

The aim of the current work is to review and evaluate how genetic research has contributed to furthering our knowledge and understanding of the associations between cognitive ability and education during development. We discuss the copious amount of knowledge that has emerged from behavioural genetic studies of cognitive and educational skills and their links. First, we review the wealth of research that has applied classical twin design to examine the origins of the associations between cognitive ability and academic performance (i.e. academic achievement and educational attainment). We highlight similarities as well as differences in the aetiology and developmental profiles that characterize these two broad dimensions. Second, we examine how molecular genetic research, particularly recent cutting-edge advances in DNA-based methods, has furthered our knowledge and understanding of cognitive ability, academic performance and their association. At every stage we discuss the strengths and limitations of applying each methodological approach to the investigation of individual differences in human behaviour and cognitive ability. Third, given that education extends beyond academic performance, we briefly widen our focus to discussing research on the association between cognitive ability, academic performance and other important educationally relevant 'noncognitive' traits. We conclude by discussing gaps in the current state of knowledge and future directions. In particular, we evaluate how the knowledge that has emerged from behavioural genetic research can help the field of education moving towards a more comprehensive, biologically oriented model of individual differences in cognitive ability and learning.

Measuring general cognitive ability and academic performance

Success in education has traditionally been closely associated with general cognitive ability. The first test of general cognitive ability (Binet, 1905) was developed with the aim of predicting individual differences in educational outcomes, which remains one of the main targets of cognitive tests to date ( Deary, Strand, Smith, & Fernandes, 2007). Contemporary standardized tests of intelligence derive from this early measure and assess performance across multiple dimensions. For instance, each test featured in the most widely adopted intelligence test batteries for adults and children (e.g. the Wechsler Scale of Intelligence and the Wechsler Intelligence Scale for Children; Wechsler, 2003, 2011), measures a specific dimension of cognitive functioning. These specific dimensions of cognitive functioning include skills such as verbal ability, spatial ability, non-verbal reasoning, processing speed, and memory. Although separable, these dimensions have been shown to correlate with one another, thus resulting in a measure of general cognitive ability.

General cognitive ability, also termed intelligence or g, is a psychometric construct that emerged at the beginning of the twentieth century from observations that almost all cognitive abilities correlate substantially and positively ( Spearman, 1904). In other words, individuals performing highly in one cognitive test are also likely to show good performance in other tests of cognitive abilities ( Carroll, 1993). ' g' indexes this covariation observed between cognitive tests. One way of interpreting this factor is that it represents individual differences in the domain-general abilities to plan, learn, think abstractly, and solve problems, all skills that contribute to successful completion of cognitive tests ( Deary, 2013). Spearman's g factor correlates very strongly (> .80) with a g factor derived from the first unrotated principal component across multiple cognitive tests and with the score obtained from a full-scale intelligence quotient (IQ) test ( Ceci, 1991).

As such, in the current review we consider these three formats as measures of g. The g factor is universally observed ( Lubinski, 2004), is stable across the lifespan (e.g. r = .63 between g scores taken at age 11 and again 68 years later; Deary, Whalley, Lemmon, Crawford, & Starr, 2000), and predicts important life outcomes including wealth, morbidity and mortality. Although the developmental stability of g increases sharply from mid-childhood ( Tucker-Drob & Briley, 2014), modest rank-based stability is observed from a very young age: g measured in four-year-olds was found to correlate modestly (~.20) with g a decade later, largely for genetic reasons ( Arden, Trzaskowski, Garfield, & Plomin, 2014).

Standardized tests have also been developed to assess academic performance. These tests measure performance in key academic skills such as reading fluency, reading comprehension, computational skills and problem solving ( Kaufman, Reynolds, Liu, Kaufman, & McGrew, 2012). It has been argued that standardized tests of academic achievement are an index of cognitive ability rather than a true measure of academic performance, and that classroom performance, in the form of as teacher assessments or cumulative grades, might constitute a more realistic assessment of academic performance (e.g. McCall, Evahn, and Kratzer, 1992; Kaufman et al., 2012). However, correlations between numerous standardized tests of academic abilities and non-verbal intelligence are estimated at ~ .50 ( Guez, Panaïotis, Peyre, & Ramus, 2018), suggesting that these measures are not entirely alike. Furthermore, recent work examining the concordance between standardized exam scores and teacher assessments throughout compulsory education has highlighted the strong overlap between these two formats (correlations of > .70) and their comparable associations with further educational attainment ( Rimfeld, Malanchini et al., 2019). Consequently, in the current review we consider both standardized tests of academic achievement and teacher assessments, in addition to educational attainment, as useful indices of academic performance.

Psychological research into the association between cognitive ability and academic performance

The observation that g reliably predicts educational outcomes is a fascinating phenomenon that has been extensively studied in the literature. Taken at any point across development, g shares a moderate to strong correlation with academic achievement, ranging from .40 to .80 ( Meike Bartels, Rietveld, Van Baal, & Boomsma, 2002; Ian J. Deary et al., 2007; Sternberg, Grigorenko, & Bundy, 2007). A meta-analysis of 240 independent samples including over 100,000 participants found a population correlation of .54 between intelligence and school grades; effect sizes were similar across subjects, ranging between .49 for mathematics and science and .41 for languages, with two exceptions: music (.19) and sports (.09) ( Roth et al., 2015). The same meta-analysis showed that the magnitude of the correlation between intelligence and school grades increased with age, from .45 during the primary school years to .58 in high school, and effects were consistent across gender ( Roth et al., 2015).

Strong associations between cognitive abilities and academic achievement have also been observed longitudinally. One of the largest prospective studies exploring the association between g and academic achievement, including over 70,000 children from England, found that g at age 11 correlated strongly with achievement at age 16 and predicted individual differences in every school subject, accounting for between 59% of the variance in mathematics to 18% of the variance in Art and Design ( Deary et al., 2007). Even more strikingly, tests of cognitive abilities taken very early in life are valid predictors of academic achievement and abilities later in development. Longitudinal investigations have found moderate associations between early cognitive skills and achievement several years later. g measured at four years of age was found to predict individual differences in mathematics ability and achievement eight years later with moderate effect sizes ( Malanchini et al., 2016). Similarly, another study found that non-verbal intelligence and working memory measured at age 5 correlated moderately ( r = .30 − .40) with individual differences in literacy and numeracy six years later ( Alloway & Alloway, 2010).

The observed long-term predictive power of cognitive ability measured early in development suggests that g might have a causal effect on academic performance over development ( Watkins, Lei, & Canivez, 2007). Studies have supported this proposition, finding for example that fluid intelligence ( gf) had a positive influence on academic achievement, particularly on quantitative abilities, and that this influence was stronger during childhood and early adolescence ( Ferrer & McArdle, 2004). However, the observed associations between early g and later academic performance are likely to conceal reciprocal links: students with higher cognitive skills are likely to achieve better grades and consequently spend more time in education, but also spending more time in education fosters cognitive development.

A recent meta-analysis of the effects of education on cognitive ability including data from over 600,000 individuals supports the proposition that time spent in education exerts a positive causal effect on the development of cognitive skills ( Ritchie & Tucker-Drob, 2018). Leveraging quasi-experimental designs (i.e. longitudinal studies, compulsory policy changes and regression discontinuity), the meta-analysis found that the effect of one additional year in education corresponded to gains of between 1 and 5 standardized IQ points, depending on the design considered. These gains in cognitive ability elicited by additional time spent in education were observed for a general cognitive ability composite as well as when tests of fluid and crystallized intelligence were considered separately. Furthermore, the effects persisted across the lifespan ( Ritchie & Tucker-Drob, 2018). These findings are in line with those of an earlier review ( Ceci, 1991).

In line with Spearman's proposition of a domain-specific effect of education on cognitive skills ( Spearman, 1927), another study found that the improvements associated with longer time spent in education were observed for specific cognitive abilities rather than for g ( Ritchie, Bates, & Deary, 2015). In addition, education was found to have a beneficial impact on intelligence in old age, and particularly so for individuals scoring lower on an IQ test in childhood, but no effect on lower-level cognitive abilities such as processing speed ( Ritchie, Bates, Der, Starr, & Deary, 2013). These findings point to the importance of examining domain-general as well as domain-specific effects of the association between cognitive ability and academic performance - note that there were too few studies examining domain-specificity for a useful meta-analysis to be performed on this question.

Surprisingly few studies have examined the reciprocal links between g and academic achievement over multiple developmental stages applying formal longitudinal designs ( Deary & Johnson, 2010). One such study found that cognitive ability measured in early childhood predicted achievement in reading and mathematics and, in turn, mathematics achievement in early adolescence predicted adolescent g ( McCoach, Yu, Gottfried, & Gottfried, 2017). While this study included multiple measures of reading and mathematics achievement over development, g was only assessed once before the start of schooling and once in adolescence, preventing conclusions on the reciprocal effects of g on achievement and vice versa over a more fine-grained time scale. Evidence for the positive effect of reading on cognitive development comes for a study observing that reading comprehension contributed to growth in verbal ability over and above general cognitive ability from age 8 to 16 ( Cain & Oakhill, 2011). A further study applied cross-lagged panel analysis to examine the reciprocal links between achievement in reading and mathematics and g measured at three ages during primary school, providing evidence for reciprocal influences ( Cowan, Hurry, & Midouhas, 2018).

Therefore, cognitive ability and academic performance share a substantial, reciprocal association that emerges early in development. But what are the biological and environmental mechanisms underlying this association? Behavioural genetic research has investigated the origins of individual differences in cognitive abilities and academic performance, and of their links, across the lifespan applying multiple methodologies. In the sections that follow we begin by reviewing and evaluating evidence from several decades of twin studies before moving on to examine recent evidence from molecular genetic research.

Twin studies of the genetic and environmental underpinnings of cognitive ability, academic performance and their association.

Studies using genetically informative methodologies have explored the origins of individual differences in general cognitive ability and of its well-established association with academic performance. Indeed, investigating why individuals differ in their cognitive skills has been one of the main interests of behavioural genetic research ( Plomin & Deary, 2015). The relative contribution of genetic and environmental factors to variation in a trait has been classically estimated using twin and family studies ( Polderman et al., 2015).

The twin method: A brief overview

Twin design capitalizes on the genetic relatedness between different types of twin pairs to estimate the extent to which differences between individuals in a given trait are accounted for by genetic and environmental factors. The method is grounded in the fact that monozygotic twins share one hundred percent of their genetic makeup, and dizygotic twins share on average fifty percent of the genes that differ between individuals. Furthermore, the method makes the key assumption that both types of twins who are raised in the same family home share their rearing environments to approximately the same extent ( Conley, Rauscher, Dawes, Magnusson, & Siegal, 2013; Kendler, Neale, Kessler, Heath, & Eaves, 1993). By comparing how similar monozygotic and dizygotic twins are for a given trait, it is possible, under these assumptions, to calculate the extent to which differences between individuals in that population at that particular time are due to genetic and environmental influences.

The twin method estimates the relative contribution of three main sources of variation in the population: heritability, shared environment and nonshared environment. Heritability describes the amount of variance in a trait that can be attributed to genetic differences in a given population, and can be roughly estimated by doubling the difference in the correlation between the monozygotic and dizygotic twin pairs ( Martin & Eaves, 1977). Shared environment describes the extent to which twins raised in the same family resemble each other beyond their genetic similarity. Finally, non-shared environment describes environmental variance that does not contribute to similarities between twin pairs, which in formal structural equation models can also incorporate measurement error.

Twin studies of the genetic and environmental underpinnings of general cognitive ability and academic performance

Research applying the twin design has consistently shown that genetic differences between individuals play an important role in explaining variation in g. The heritability of g -the extent to which genetic differences between individuals explain differences in their observed cognitive performance- has been found to increase substantially from early childhood to adulthood. Genetic factors were found to explain around 20% of individual differences in g in infancy, around 40% in late childhood, and about 60% of the variation in g in adolescence and adulthood ( Haworth et al., 2010), and to reach approximately 80% in older adulthood ( Plomin & Deary, 2015). The observation of a linear increase in the heritability of g over development has been replicated across numerous samples cross-culturally ( Tucker-Drob, Briley, & Harden, 2013).

Two main theoretical accounts, not mutually exclusive, have been proposed as potential explanations for the observed increase in the heritability of g over development. The first sees transactional models ( Tucker-Drob & Briley, 2014; Tucker-Drob et al., 2013), rooted in gene-environment correlation ( Plomin, DeFries, & Loehlin, 1977), as the main mechanism through which children experience, evoke, select and consolidate their genetic propensity through environmental experiences, resulting in amplified genetic effects on cognitive ability ( Tucker-Drob et al., 2013).

Gene-environment correlation describes the processes through which individuals experience environments that correlate with their genotype, rather than being exposed to random environmental experiences. This can happen through three mechanisms. First, passive processes: children and adolescents tend to grow up with their parents who shape the raring environment on the basis of their own genotype, which they share with their offspring. Second, evocative processes: individuals may elicit their experiences on the basis of their partly genetically influenced traits, such as dispositions and characteristics. Third, active processes: individuals actively select and modify their experiences based on their genetic propensities, dispositions and appetites ( Plomin et al., 1977; Plomin, 2014).

An alternative account proposes that novel genetic influences might come into play over development and result in the observed increased in the heritability of cognitive ability. Novel genetic influences on cognitive ability were found to come into play early in development, before middle childhood ( Tucker-Drob & Briley, 2014). At the same time, strong genetic stability, indexed by substantial genetic correlation ( ra), has been observed in g from age 7 to age 12 ( ra = .75), as well as between g at age 11 and age 69 ( ra = .62; Deary et al., 2012), suggesting a greater role of transactional models in accounting for the increased heritability of g over the lifespan ( Tucker-Drob et al., 2013).

Academic performance is also highly heritable. Studies found that genetic differences between people explain a substantial portion of their individual differences in academic achievement at every stage of compulsory education ( Kovas et al., 2007; Shakeshaft et al., 2013; Tosto, Malykh, Voronin, Plomin, & Kovas, 2013), as well as in standardized tests of academic abilities, such as for example reading and mathematics ( Malanchini, Engelhardt, Grotzinger, Harden, & Tucker-Drob, 2018; Malanchini et al., 2017; Petrill et al., 2012; Tosto et al., 2017; Tucker-Drob, Briley, Engelhardt, Mann, & Harden, 2016). A meta-analysis of the relative contribution of genetic, shared environmental and nonshared environmental influences on academic achievement in primary school found evidence for the importance of genetic variation at all ages cross-culturally ( de Zeeuw et al., 2015).

Genetic differences between students have also been found to play a major role in students' choice of academic career beyond compulsory education, for example, A-level choice and achievement ( Rimfeld, Ayorech, Dale, Kovas, & Plomin, 2016). After compulsory education, students in the UK can choose to continue studying for two years in preparation for university, freely selecting the subjects they wish to focus on. At the end of these two years, students are required to take 'A-level' exams, which are fundamental for admission to university. Genetic factors were found to account not only for differences in A-level grades, but also for the choice of continuing on to A-levels and for specific subject choices ( Rimfeld et al., 2016). Similarly, genetic factors were found to account for variation in several measures of educational attainment, including the choice of enrolling in an undergraduate degree and university success ( Smith-Woolley, Ayorech, Dale, von Stumm, & Plomin, 2018).

Different developmental trajectories characterize the aetiology of g and academic performance

It is reasonable to assume that the high heritability of academic achievement observed at every stage in development is largely explained by its robust association with g. However, this proposition is not in line with evidence showing that academic achievement in literacy and numeracy in early and mid-childhood is significantly more heritable than g ( Kovas et al., 2013). As the heritability of g increases over development ( Plomin & Deary, 2015), by early adolescence g and achievement show comparable heritabilities. The difference in the heritability of g and achievement in the early school years lessens the plausibility of the proposition that genetic variation in g is the major source underlying the high heritability of academic achievement. An even stronger line of evidence for the high heritability of academic achievement, independent of g, comes from a recent large-scale longitudinal investigation of the genetic and environmental stability of academic achievement over compulsory education in the United Kingdom ( Rimfeld, Malanchini et al., 2018). Applying a longitudinal twin design, the study showed that academic achievement is highly stable over compulsory education and its stability is largely due to genetic influences even after accounting for g ( Rimfeld, Malanchini et al., 2018). Although genetic innovation was observed at every stage, indicating new genetic effects coming into play, these effects were not specific to each developmental stage, instead they were passed on to the following developmental stages.

Research examining the continuity of genetic and environmental influences on g over development also reports strong genetic stability, as largely the same genetic effects are found to contribute to variation in cognition over the lifespan. Results of a meta-analysis of 21 studies from 15 independent samples from early childhood to older adulthood showed that the stability of g increases sharply from early to middle childhood and remains high throughout the lifespan ( Tucker-Drob & Briley, 2014). Coupled with a sharp increase in stability observed around age 7, the meta-analysis observed shifts in the extent to which genetic and environmental influences contributed to the covariation between measures of g over time. Shared environment played a significant role in explaining early stability, but its contribution was greatly reduced nearly to zero in late adolescence. On the contrary, genetic factors accounted for a small proportion of the stability of early g, but their contribution increased sharply during middle childhood and remained stable throughout adolescence and adulthood ( Tucker-Drob & Briley, 2014). This is in line with earlier evidence on the genetic stability of intelligence from age 5 to 12 in a Dutch sample ( Bartels, Rietveld, Van Baal, & Boomsma, 2002). Finally, the contribution of nonshared environmental influences to the stability of g was negligible throughout childhood and adolescence, but increased to moderate during adulthood ( Tucker-Drob & Briley, 2014).

The findings are consistent with those observed for academic achievement over compulsory education, particularly when considering that schooling was measured over four waves from the age of 7 to the age of 16 ( Rimfeld, Malanchini, et al. 2018). Over this developmental time, the stability of both academic achievement and g is mostly due to genetic variation, with shared environmental variation playing a modest role and nonshared environment having a negligible effect ( Rimfeld, Malanchini et al., 2018; Tucker-Drob & Briley, 2014). Studies that have examined the stability of academic achievement ( Luo, Haworth, & Plomin, 2010) and of specific academic abilities, including reading comprehension ( Malanchini et al., 2017), teacher ratings of reading ability ( Harlaar, Dale, & Plomin, 2007) and teacher ratings of mathematics ability ( Luo, Kovas, Haworth, & Plomin, 2011) over a shorter time span provide support for the central role of genetic variation.

Therefore, as represented in Figure 1 , the phenotypic stability of both g and academic achievement increases modestly from mid-childhood to late adolescence (e.g. Roth et al., 2015) and is largely accounted for by genetic factors. Shared environmental influences account for a much lower proportion of the covariance between measures over time, amounting to about one third of the magnitude of genetic effects when considering academic achievement ( Rimfeld, Malanchini et al., 2018). Nonshared environments are largely specific to each measurement occasion and do not contribute consistently to the stability of achievement and g from childhood to late adolescence and emerging adulthood. The same pattern of results is observed for different academic domains and does not seem to be explained by the association between g and achievement, as the phenotypic and genetic stability of achievement remained high even after statistically accounting for g ( Rimfeld, Malanchini et al., 2018). This suggests that several other -partly genetically influenced- factors contribute to the genetic stability in academic achievement beyond g ( Krapohl, Rimfeld et al., 2014; Malanchini et al., 2018). This will be discussed in more detail below.

Twin studies of the association between general cognitive ability and academic performance: pleiotropic effects are observed throughout development

The association between academic performance and g is also stable over development and twin research has shown that genetic factors explain a substantial portion of their links across the lifespan ( Calvin et al., 2012; Johnson, Deary, & Iacono, 2009). In one of the first investigations into the genetic and environmental underpinnings of the covariation between cognitive ability and academic performance, Thompson, Detterman and Plomin (1991, p. 164) state: '... the covariance between ability and achievement is primarily genetically determined ... ability-achievement discrepancies are due to environmental differences.' ( Thompson, Detterman, & Plomin, 1991). Over the following nearly three decades numerous studies have supported this general conclusion about the association between cognitive ability and academic performance.

Strong correlations between g and academic performance are observed when considering both standardized tests of academic abilities -e.g. reading comprehension and mathematics computation abilities ( Harlaar, Hayiou-Thomas, & Plomin, 2009) as well as for more general measures of academic performance such as exams scores and teacher grades ( Kovas et al., 2007). In the same way that the associations between g and reading, and g and mathematics, are characterized by comparable moderate effect sizes (~.40), the proportion of their correlation that is accounted for by genetic and environmental factors is substantial for both academic domains ( Kovas, Harlaar, Petrill, & Plomin, 2005). The proportion of the phenotypic correlation that is due to genetic factors is known as the bivariate heritability, and it is also possible to calculate bivariate estimates for the shared and nonshared environments. In a sample of nearly 6,000 7-year-old twins, the bivariate heritability observed for g and mathematics was .83 and that for g and reading .65, with partly overlapping confidence intervals around the estimates, indicating that genetic factors accounted for over 60% of the correlation between g and academic achievement in different domains. Shared environment accounted for 27% of the correlation between g and reading and 9% of that between g and mathematics. Nonshared environment accounted for 8% of the correlation between g and both academic subjects ( Kovas et al., 2005).

Substantial pleiotropic effects, indexed by the genetic correlation between traits, were observed between academic achievement in all domains and g (.44-.69; Rimfeld, Kovas, Dale, & Plomin, 2015). Pleiotropy (genes influence multiple traits; Lynch & Walsh, 1998) between g and academic achievement is not specific to middle childhood and adolescence but observed consistently across development. Findings from a study leveraging whole population cohorts across two countries, the United Kingdom and the Netherlands, provide general support for the substantial role of genetic covariance in accounting for the observed correlation between g and academic achievement ( Calvin et al., 2012). Moreover, pleiotropic effects between g and achievement have been observed longitudinally between early g and mathematics achievement at age 12 ( Malanchini et al., 2016).

Therefore, a substantial part of the genetic variance accounting for individual differences in g is also implicated in academic performance. The notion that partly overlapping genes contribute to individual differences in all aspects of cognitive ability and learning is summarized by the 'generalist genes' account of learning abilities and disabilities ( Plomin & Kovas, 2005). The theory, grounded in the two notions of pleiotropy (one gene affects many traits) and polygenicity (several genes influence one trait), proposes that genetic influences on different cognitive and academic abilities and disabilities overlap. Although this account emerged largely as a function of findings from twin studies ( Davis et al., 2008; Kovas et al., 2005), more recent studies applying molecular genetic methods have provided support for the general effects of the genes implicated in cognitive ability and academic achievement ( Bulik-Sullivan et al., 2015; Lee et al., 2018; Rimfeld et al., 2015; Maciej Trzaskowski et al., 2013).

Although the substantial genetic overlap between cognitive and educational phenotypes is consistent with widespread pleiotropy between multiple educationally relevant and cognitive traits, it is possible that alternative biological mechanisms may underlie their covariation. Indeed, the observed genetic correlations might reflect genetic causality, whereby genetic factors influence one trait, for example g, and in turn g influences another trait, for example academic performance ( Ligthart & Boomsma, 2012). Longitudinal genetically-informative models, such as for example cross-lagged panel analyses ( Malanchini et al., 2017) will be able to shed light on the mechanisms supporting the nearly ubiquitous pattern of genetic associations observed across multiple aspects of cognitive ability and educational achievement. Research that has applied this methodology in the context of education, focusing on the association between academic performance and motivation, provides support for the existence of both mechanisms. The study found that reading achievement and motivation correlated substantially for genetic reasons (supporting pleiotropic mechanisms). In addition, the two traits mutually influenced each other longitudinally, and these reciprocal links were partly genetic in origin (supporting genetic causality; Malanchini et al., 2017). However, no research to date has applied this type of design to the investigation of the associations between cogntive ability and academic performance over development.

The role of individual-specific and family-wide environmental factors

Other studies have focused on examining the role of environmental influences on cognitive ability and academic performance over time applying genetically informative longitudinal methods. One such study leveraged monozygotic twin differences with a cross-lagged design to explore the role of nonshared environmental influences on the association between g and academic performance ( Ritchie, Bates, & Plomin, 2015). Because monozygotic twins are genetically identical and grow up in the same home environment, the monozygotic twin differences design allows for the examination of environmental influences that are unique to each twin, free from the confounds of genetic and shared environmental effects ( Vitaro, Brendgen, & Arseneault, 2009). Examining discordances between monozygotic twin pairs in reading and g longitudinally, this study found support for nonshared environmental effects on reading ability having a weak causal effect on g at subsequent developmental stages. These results support the view that individual-specific environmental effects on reading ability are partly stable over time and transfer to more general cognitive skills resulting in improved performance over development ( Ritchie, Bates, & Plomin, 2015). However, the use of a state-trait model, which accounts more strongly for the phenotypic stability of reading and of intelligence, resulted in substantially lower estimates of the potential effects of reading ability on broader cognitive skills ( Bailey & Littlefield, 2017).

Other studies have provided support for the role of shared environmental factors in accounting for the association between cognitive abilities and academic performance. As previously discussed, the role of shared environmental influences on the stability of g and academic performance and of their developmental association is generally found to be small, particularly from mid-childhood. However, studies have found evidence for stronger effects, in early childhood ( Lemelin et al., 2007) as well as in countries characterized by less standardized educational experiences ( Petrill & Wilkerson, 2000). Interestingly, although the proportion of the cross-sectional and longitudinal links accounted for by shared environmental influences ( bivariate shared environmentality) is relatively small, shared environmental correlations between multiple measures of academic performance and cognitive abilities tend to be very strong, suggesting that shared environmental influences are translational across domains of cognitive ability and learning and are stable over time. For example, a recent meta-analysis of twin studies of the association between reading and mathematics abilities and disabilities found a meta-analytic estimate of .90 for the shared environmental correlation between reading and mathematics ( Daucourt et al., 2019).

Assumptions and limitations of twin studies

The wealth of knowledge that has emerged from twin studies of cognitive ability, academic performance and their associations needs to be evaluated in light of the limitations that apply to the methodology. The twin method is based on several assumptions. First, the equal environments assumption is the idea that environmental similarity is the same for monozygotic and dizygotic twin pairs growing up in the same family ( Knopik, Neiderhiser, Defries, & Plomin, 2016). Studies have observed that monozygotic twins are more likely to share analogous environmental experiences than dizygotic twins: they tend to be treated more similarly and to more often share friends. Nevertheless, studies assessing the impact of sharing more environmental experiences did not find that this to have substantial influence on the degree of phenotypic concordance ( Conley et al., 2013; Kendler, Kessler, Neale, Heath, & Eaves, 1993).

A second assumption of the twin method is random mating: people are assumed to mate at random, and not with other people that resemble them. In reality this assumption is violated as people tend to mate with people who resemble them both phenotypically and genetically, a concept known as assortative mating ( Ask, Idstad, Engdahl, & Tambs, 2013) Assortative mating is especially strong for cognitive and educational traits ( Abdellaoui et al., 2015), and thus it could impact the outcomes of research using the twin method on these variables. The most basic assumption of the model is that the coefficient of relatedness is 1 (i.e. 100% genetic similarity) between monozygotic twin pairs and .5 (i.e. 50% similarity on average) between dizygotic twin pairs. Assortative mating will increase the genetic similarity between dizygotic twins but cannot increase genetic similarity between monozygotic twins because they are already 100% similar genetically. In this way, assortative mating will lead to underestimation of genetic effects and overestimation of shared environmental effects ( Røysamb & Tambs, 2016). Although evidence of assortative mating is well established particularly for cognitive and educational phenotypes, given the weak estimates of shared environmental effects evinced by the association between g and achievement from middle childhood, this limitation is unlikely to have had a major impact on the research reviewed so far.

One additional limitation that is likely to impact not only twin studies specifically, but all cohort studies more generally is the possibility that the sample is subject to self-selection biases and therefore not representative of the larger population. Moreover, it is particularly important to consider this limitation when studying early development in twin samples since evidence suggests that twins might be at a slight disadvantage during perinatal development as compared to singletons ( Martin, Boomsma, & Machin, 1997). However, evidence from a whole population twin study across two countries found highly consistent patterns of results to those obtained from cohort studies of twins on the association between cognitive ability and academic performance ( Calvin et al., 2012).

A fifth limitation of research applying the twin method is that it does not identify the specific genes involved in the observed variation and covariation between traits. However, molecular genetic studies are not subject to this limitation, and can estimate the genetic influences on traits and their covariations free from most of the assumptions and limitations reviewed above. Furthermore, very recently, studies using DNA-based methods have started addressing the role of gene-environment interplay in variation in cognitive ability and academic performance ( Bates et al., 2018; Cheesman et al., 2019; Kong et al., 2018; Selzam et al., 2019). Although this special issue is mostly concerned with reviewing and evaluating evidence that has emerged from twin research, in the section that follows we provide an overview of the most recent findings on the genetics of the association between cognitive ability and academic performance stemming from studies that have applied DNA-based methods, which largely confirm the results of twin studies. We evaluate advantages and limitations of this fast-growing field of research.

Moving into the molecular space: the genetics of cognitive ability and academic performance using DNA-based methods

Early research concerned with identifying genetic variants associated with individual differences in behaviour started by focusing on examining the role of single or a small number of genetic variants, often selected on the basis of their biological or functional significance. This candidate gene approach, at odds with the substantial polygenicity observed for all behavioural traits, yielded several false positive results that lacked solid replications ( Chabris et al., 2012). One of the major problems with the candidate gene approach was its reliance on small sample sizes, which yielded little power to detect associations of small effect size: the associations that were found, therefore, suffered from the "winner's curse", where they were likely to be inflated and due to chance. Small samples were largely due to the fact that genotyping was very expensive for the first decade of the twenty-first century. A technological advance that assessed hundreds of thousands of DNA differences (single-nucleotide polymorphisms, SNPs) enabled an atheoretical approach to identify associations across the genome, called genome-wide association (GWA). The substantial decrease in cost of GWA analyses during the past decade coincided with larger samples becoming available, and large-scale biobank studies were launched.

Genome-wide association studies of cognitive ability and academic performance

Recently, GWA studies have begun to identify SNPs associated with individual differences in cognitive ability and educational attainment ( Davies et al., 2018; Lee et al., 2018; Okbay et al., 2016; Rietveld et al., 2013; Savage et al., 2018; Sniekers et al., 2017). The first major finding that has emerged from GWA studies of all complex traits is that no associations of large effect size have been discovered - the largest effects are much smaller than anyone anticipated. This means that heritability is due to many DNA differences, that is, virtually all phenotypes are highly polygenic. This finding is so common that it has been described as a "Law" of behavioural genetics: '... typical human behavioural trait is associated with very many genetic variants, each of which accounts for a very small percentage of the behavioural variability' ( Chabris, Lee, Cesarini, Benjamin, & Laibson, 2015).

GWA studies have investigated the specific genetic variants that are associated with cognitive performance ( Lee et al., 2018; Sniekers et al., 2017) and educational attainment ( Lee et al., 2018; Okbay et al., 2016; Rietveld et al., 2013). As the sample sizes of these investigations increase, with the latest GWA of educational attainment (EA3) including >1.1 million participants, and that of cognitive performance (IQ3) including >250,000 participants ( Lee et al., 2018; Savage et al., 2018), increasingly more insight into the molecular genetic architecture of cognitive ability and academic performance has started to emerge. These continuously-increasing sample sizes have allowed for a great deal of statistical power, and the ability to uncover more and more SNPs -each of miniscule effect size- that are associated with educational attainment and cognitive performance. In fact, the latest GWA analyses identified 1,271 approximately independent loci associated with educational attainment (EA3), and 225 loci associated with cognitive performance (IQ3; Lee et al., 2018). The IQ3 results stemming from a complementary effort in partly overlapping cohorts, with a total sample size of 269,867 were highly consistent, identifying 205 mostly overlapping loci ( Savage et al., 2018).

The success of a GWA study for a given trait depends on multiple factors including the genetic architecture of such trait, the sample size, and trait heterogeneity; the latter is directly related not only to the biology of the trait, but also to how accurately we can measure that trait in the population ( Visscher et al., 2017). The GWA studies of educational attainment and cognitive performance are the most powerful gene-discovery efforts in the behavioural sciences to date ( Plomin & von Stumm, 2018). In addition, these powerful GWA studies point, more generally, to the reliability of the methodology. GWA studies have been met with criticism both regarding their purpose and their discoveries ( Visscher, Brown, McCarthy, & Yang, 2012; Visscher et al., 2017). However, two recent independent efforts to uncover the genetic variants associated with cognitive performance in partly overlapping samples ( Savage et al., 2018; Lee et al, 2018) have resulted in highly consistent findings. Similarly, results are highly consistent across iterations of the GWA studies of educational attainment (i.e. EA1, EA2 and EA3), and genetic correlations are substantial, although not perfect, between different cohorts ( Lee et al., 2018). As noted by the authors: 'imperfect genetic correlation across cohorts will be the norm for phenotypes, such as educational attainment, that are environmentally contingent.' ( Lee et al., 2018, p. 6). Therefore, the accomplishments of GWA studies of cognitive performance and educational attainment extend beyond their successful discoveries of genetic variants, to highlight the robustness of GWA discoveries.

Pleiotropic effects between cognition and education leveraging GWA discoveries

Methodological advances have made it possible to calculate genetic correlations between traits based on GWA discoveries. The most widely used method is cross-trait linkage disequilibrium (LD) score regression, which estimates genetic correlations based on GWA summary statistics unbiased by sample overlap ( Bulik-Sullivan et al., 2015). Applying LD score regression, it is therefore possible to calculate genetic correlations between educational attainment and cognitive performance, entirely from DNA, free from the majority of the assumptions that apply to family-based designs such as the twin method. A substantial genetic correlation was found between educational attainment and g ( r g = .66; Lee et al., 2018), which is in line with estimates of genetic correlations obtained from twin studies and Genome-wide Complex Trait Analysis (GCTA; Rimfeld et al., 2015). Applying LD score regression, strong correlations have also been observed between the genetics of cognitive performance and the genetics of two additional educationally-relevant traits: highest level of mathematics class completed ( r g = .64) and self-rated mathematics ability ( r g = .60); in addition, these two traits shared strong genetic correlations with educational attainment ( r g = .80 and .51, respectively; Lee et al., 2018). Overall, these results are in line with estimates of genetic associations between cognitive and academic performance that emerged from decades of twin research and DNA-based methods like GCTA ( Yang, Lee, Goddard, & Visscher, 2011).

Importantly, the GPS constructed from the GWA studies of cognitive ability educational attainment are not limited to predicting their target traits ( Davies et al., 2018; Savage et al., 2018; Selzam et al., 2017). For example, a GPS constructed from an earlier GWA analysis of educational attainment including >290,000 participants (EA2; Okbay, Beauchamp, Fontana, Lee, Pers, Rietveld, Turley, Benjamin, et al., 2016) was found to predict variation in teacher-rated academic achievement at every stage of compulsory education ( Selzam et al., 2017). Interestingly, the effect size of this DNA-based prediction increased with age, with the EA2 GPS accounting for nearly 3% of the variance in achievement at age 7, >4.5% at age 12 and > 9% of the phenotypic variance in standardized educational exam scores at age 16. The EA2 GPS also accounted for individual differences in g (approximately 3.5%; Selzam et al., 2017). Highly consistent predictions are observed when considering teacher-rated and test-based measures of academic achievement over development using the same EA2 GPS ( Rimfeld, Malanchini et al., 2018). The predictive power of the GPS generated from the EA3 GWA study is even greater. In two independent samples of American adults, the EA3 GPS was found to predict 12.7% and 10.6% of the phenotypic variance in educational attainment, respectively ( Lee at el., 2018). Moreover, the EA3 GPS based on educational attainment in adults predicts up to 15% of the variance in tested educational achievement at age 16 ( Allegrini et al., 2019), which is the most powerful GPS prediction reported to date in the behavioural sciences ( Plomin, 2018).

Multivariate approaches to genome-wide association studies

A recent study has applied MTAG to combine the IQ2 ( Sniekers et al., 2017) and EA2 GWA in order to create a more powerful GWA analysis of cognitive ability. This multivariate approach led to a substantial increase in sample size and power which in turn resulted in the identification of 187 independent loci ( Hill et al., 2018) relevant to cognitive ability. This constituted a substantial boost in power as compared to the individual EA2 and IQ2 GWA analyses, which identified 74 and 18 genome-wide significant independent loci, respectively. GPS calculated after applying MTAG to GWA summary statistics have also resulted in increased predictive ability. The multivariate GPS constructed from aggregating the GWA summary statistics of four cognitive and educationally-relevant traits (EA3, IQ3, highest level of mathematics class completed and self-rated mathematics ability) was found to increase the prediction of educational attainment and cognitive ability by between 2.7% and 6.9% of the variance, depending on the sample and phenotype ( Lee et al., 2018).

A further multivariate GWA approach is genomic structural equation modeling (Genomic SEM; Grotzinger et al., 2019). Based on the principles of SEM widely used in twin analyses and integrated with cross-trait LD score regression ( Bulik-Sullivan et al., 2015), Genomic SEM jointly analyses GWA summary statistics for multiple traits to test hypotheses about the structure of the genetic covariance between traits. By creating latent factors from GWA summary statistics, this approach can also be used to boost power for GWA discovery and polygenic prediction ( Grotzinger et al., 2019). Examining the predictive power of GPS across multiple multivariate methods, a recent study found that aggregating across educationally-relevant GWA summary statistics (EA3, IQ3, income, 'age when education was completed', and 'time spent using the computer') using Genomic SEM increased prediction of academic achievement and cognitive ability ( Allegrini et al., 2019). While a GPS constructed from the single EA3 GWA summary statistics accounted for a maximum of 14.8% of the variance in academic achievement and the GPS constructed from IQ3 accounted for a maximum of 6.7% in cognitive ability, aggregating across GWA summary statistics accounted for 16% of the variance in academic achievement and 11% of the variance in g at age 16. Similar boosts to predictive power were obtained when the multivariate GPS was constructed using MTAG ( Allegrini et al., 2019).

A different approach that has been applied with the aim of increasing GPS prediction is the multi-GPS technique ( Krapohl et al., 2017). Different from the two methods described above, this approach works at the level of the GPS scores of individuals rather than at the level GWA summary statistics. In this framework, elastic net regularization (a regularized regression method) is applied to a number of GPS to predict trait variation. While multiple linear regressions subject to problems of multicollinearity and overfitting when a large number of correlated predictors are included in the model, elastic net regression overcomes these problems by shrinking (i.e. penalizing) parameter estimates and, at the same time, performing model selection by dropping sets of non-informative predictors ( Zou & Hastie, 2005). As a consequence, the multi-GPS approach allows for the inclusion of a very large number of variables in the regression model. Applying this multi-GPS approach has resulted in increases in the GPS predictive power of academic achievement (+ 1.1%) as compared to models including a single GPS, and similarly larger effect sizes were observed for other traits such socioeconomic status and body mass index ( Krapohl et al., 2017).

Therefore, in line with the evidence emerging from decades of twin studies, findings from research applying the newest DNA-based methods consistently report substantial estimates of genetic influences on cognitive educational traits ( Figure 2 ), as well as strong genetic correlations between these two broad traits.

Molecular genetic investigations into transactional processes of gene-environment interplay

But what are the implications of high heritability and strong genetic covariance between traits? What does it mean for traits to be heritable and for their covariance be explained by genetic factors?

Heritability is maximized when people are free to choose their own experiences, partly based on their genetic propensities. As such, heritability can be considered an index of equal opportunities. In line with transactional models of cognitive development, the heritability of cognitive ability ( Tucker-Drob et al., 2013) and, to a lesser extent, that of academic performance ( de Zeeuw et al., 2015) has been found to increase with age. This observed increase in the heritability of g over development has been replicated using DNA-based methods ( Trzaskowski, Yang, Visscher, & Plomin, 2014). As children grow older, they are increasingly able to select their own experiences. Their selection of experiences is not random, instead they will be exposed to and seek out experiences that correlate with their genetic propensity, resulting in increased heritability estimates. Clever designs applying DNA-based methods have started providing concrete evidence for the effects of GE correlation on heritability estimates and GPS predictions.

One such design leverages genetic trios of mother, father, and offspring to partition the variance in the parental genotype into two parts: the genetic variants that are transmitted to the offspring and the genetic variants that are not transmitted ( Bates et al., 2018; Kong et al., 2018). This design allows to examine the effects of 'genetic nurture', or the 'nature of nurture' ( Plomin & Bergeman, 1991), on variation in a trait. Genetic nurture tests whether non-inherited genetic variants contribute to variation in phenotypes through their impact on parents and siblings, thus providing evidence of genetic effects acting through environmental pathways ( Koellinger & Harden, 2018). Applying this method, a recent study found that the GPS constructed for the non-transmitted genetic variants predicted variation in educational attainment in offspring, with an effect size of approximately one third that of the GPS calculated for the transmitted genetic variants ( Kong et al., 2018). This provides support for genetic effects influencing offspring's characteristics through their correlations with the environment (passive GE correlation). A further study using the same logic found that, in a different sample, the parental educational attainment GPS correlated with the socio-economic environment, which in turn was related to offspring's educational attainment ( Bates et al., 2018). The genetic nurture design is conceptually similar to an adoption design, as it allows to separate the effects of genotype and family rearing environment. As previously discussed, the inability to do so is one of the main limitations that apply to twin studies.

Another ingenious and conceptually related design allows to disentangle the effects of genetic and environmental factors on GPS without requiring intergenerational data. This method compares the GPS prediction in adoptees and non-adoptees, based on the assumption that adoptees, by virtue of growing up with their genetically unrelated adoptive parents, are exposed to rearing environments that are less correlated with their genotypes ( Cheesman et al., 2019). Applying this design in the UK Biobank sample, the study found that GPS were twice as predictive of educational attainment in non-adoptees as compared to adoptees. This is yet another line of evidence for GE correlation processes operating on educational success. In addition, when considering the bottom 10% of the education GPS in both adoptees and non-adoptees, adoptees showed greater educational attainment than non-adoptees, pointing to the potentially protective factors of being adopted in education-conducive environments ( Cheesman et al., 2019).

A further conceptually related design leverages differences in GPS predictions between siblings growing up in the same family to separate genetic and environmental pathways that contribute to the GPS predictive power ( Selzam et al., 2019). Siblings, like dizygotic twins, share on average 50% of the genes that differ between individuals and inherit their genotype from their parents in at random. At the same time, by virtue of growing up in the same family they are exposed to similar rearing environments. As a consequence, an association between the difference in GPS between pairs of siblings and their differences in observed trait variation can be interpreted as a causal effect of the genotype on the observed phenotype, which is also free from the confounding effects of assortative mating and population stratification. Applying this method to investigating the effects of GE correlation on cognitive ability and education, a study found that GPS predictions for academic achievement and g were much greater at the between-family level than at the within-family level (~60%). Interestingly, the majority of this difference in prediction observed between and within families was found to be accounted for by family socio-economic status. These results provide a strong line of evidence for the importance of socioeconomic differences between families as a mechanism through which passive GE correlation is operationalized ( Selzam et al., 2019).

Heritability is not only maximized when individuals are exposed to, or free to choose, experiences that correlate with their genetic propensities at the personal level, but also major changes at the societal level may result in amplified heritability. In line with this proposition, one study found that the heritability of educational attainment in Estonia post-Soviet era, a time characterized by an increase in equal opportunities and meritocracy, was substantially higher than the heritability during the Soviet era ( Rimfeld et al., 2018). Therefore, genetic effects were found to vary as a function of social policies and of major shifts in societies, which is consistent with GE interaction processes. Providing additional support, the EA2 GPS was approximately twice as predictive of educational attainment in the post-Soviet era ( Rimfeld et al., 2018). These results are in line with those of a meta-analysis testing GE interaction effects on the heritability of g applying twin design. As previously discussed, evidence for GE interaction was found only in the United States, and to be potentially explained by societal differences between the United States and Europe, for example educational quality, access to healthcare, welfare and education and social mobility ( Tucker-Drob & Bates, 2016).

Key limitations of DNA-based methods

Several limitations should be considered when evaluating the findings stemming from molecular genetic research on cognitive and educational traits. The most striking limitation to date is the fact that DNA-based results are mostly based on samples of European ancestry. This limits the extension of genetic findings to the entire population ( Martin et al., 2019; Popejoy & Fullerton, 2016). Although GWA studies in populations of non-European ancestry are beginning to emerge ( Emmanuel & von Schantz, 2018) and genotyping companies are providing increasingly powerful tools for genotyping on a global scale, this remains to date a major limitation of DNA-based methods.

A second major limitation of DNA-based methods is that the genetic variants that have emerged from the most recent and powerful GWAS, explain only a small portion of the heritability of traits (narrow sense or chip heritability) as compared to twin heritability estimates (broad sense heritability). This is particularly the case for psychological and behavioural traits for which only between one to two-thirds of the twin heritability is accounted for by common genetic variants ( Visscher et al., 2012). This gap in the heritability estimated from classical twin design and DNA-based methods is known as missing heritability ( Maher, 2008). Several processes have been proposed to account for this missing heritability gap, including the possibility that the stringent cut-off that is applied to each SNP in GWA analyses may conceal the significant contribution of SNPs of modest effects ( Manolio, 2009). A second proposition is that the current inability of GWAS to tag interactive effects between genes (epistatic effect), and between genes and environments (GE interaction) may result in the observed missing heritability gap ( Aschard et al., 2012). However, as previously discussed, solid evidence of interaction effects is currently lacking in the behavioural and psychological literature and seems to include processes that are highly culture-specific. A third proposition is that rare variants that are not tagged by SNP arrays commonly used in GWA play a substantial role in trait variation. This hypothesis has recently been tested using whole genome sequencing data in a sample of >20,000 for two anthropometric traits: height and body-mass index ( Wainschtein et al., 2019). The study found that including rare variants, particularly those in low LD, in the models recovered the majority of the heritability derived from twin studies ( Wainschtein et al., 2019). However, whether similar processes apply to complex psychological and behavioural traits, including cognitive ability and educational achievement remains speculative. Finally, it has also been proposed that the twin-based heritability estimates might be inflated ( Young, 2019).

Several other, more technical, limitations apply to GWA and the DNA-based methods that derive from GWA discoveries. It is beyond the scope of the current review to discuss all these at length, but an in-depth discussion of the benefits and limitations of the GWA design is available ( Tam et al., 2019). In light of the limitations that currently apply to both DNA-based methods and the limitations of the classical twin design, applying multiple methodologies in conjunction would result in increasingly robust findings ( Lawlor, Tilling, & Davey Smith, 2016). Convergence between twin and DNA-based findings is beginning to emerge. For example, DNA-based findings provide some support for two developmental findings for g from twin research. Twin research finds strong genetic stability for g despite increasing heritability. A DNA-based study showed similar results, with a genetic correlation of .73 from age 7 to age 12 and heritability increasing from .26 at age 7 to .45 at age 12 ( Trzaskowski et al, 2014). Similarly, DNA-based research has provided support for the finding of substantial genetic correlation between g and academic performance, with DNA-based genetic correlations between g and language, reading, and mathematics found to exceed .70 ( Trzaskowski et al., 2013).'

Triangulating across multiple genetically informative methodologies is also likely to provide increasingly actionable knowledge about the biological and environmental processes that shape the development of cognitive ability and educational achievement. Novel methods have been developed to combine classical behavioural genetic methodologies with DNA-based methods. A recent example is a new technique that allows for the integration of GPS in classical twin models to estimate the effects of GE correlation ( Dolan, Huijskens, Minică, Neale, & Boomsma, 2019).

Regardless of the limitations that characterize each method and of the desirable push towards triangulating across multiple methodologies, genetic research has provided compelling evidence that has resulted in greatly advancing our knowledge and understanding of the association between cognitive ability and learning. Through rigorous genetic research that has applied both twin and DNA methods scientists have reached a much greater understanding of the genetic and environmental processes that shape cognitive and educational traits during development, from their stability to their molecular basis. Among the most noteworthy findings emerging from behavioural genetic research are the notions that cognitive ability and academic performance share a strong association throughout the lifespan. This increases sharply over childhood, and from mid-childhood onwards is mostly accounted for by genetic influences. Nevertheless, the phenotypic and the genetic correlation between cognitive ability and academic performance are far from unity. Although it may be intuitive to speculate that the strong stability and heritability of academic performance is driven by g, genetic research has shown that the stability and genetic effects on academic performance remain substantial even after accounting for g. This suggests that several other educationally relevant characteristics contribute to the genetic variation in academic performance. It is fundamental to acknowledge these 'noncognitive' processes and to position them within a genetically oriented model of cognitive ability and learning. Consequently, the section that follows provides a brief overview of genetically informative research that has examined variation in such educationally relevant noncognitive characteristics.

The genetics of education extends beyond academic performance.

Although the notion that skills beyond cognitive ability are important in promoting learning has been embraced by cognitive scientists for over a century (e.g. Binet & Simon, 1916; Wechsler, 1943), the interest of behavioural genetics in noncognitive skills has emerged only recently. In fact, as compared to cognitive ability and academic performance, a significantly smaller body of genetically informative research has examined educationally relevant noncognitive characteristics. A first limitation that has hindered progress in the systematic investigation of noncognitive skills is their negative definition: they are not defined by what they are, rather by what they are not: they are not cognitive skills. A recent surge in interest in the role of noncognitive characteristics across the social sciences ( Heckman, Stixrud, & Urzua, 2006; West et al., 2016), has led to efforts in refining noncognitive traits. When considering educationally-relevant noncognitive skills, researchers have identified five key domains that contribute to educational success: personality traits, motivational factors, self-regulation, student's approaches to learning, and psychosocial influences ( Richardson, Abraham, & Bond, 2012). These domains are conceptually overlapping, yet distinguishable, and it is therefore fundamental to consider both their specific and collective role in promoting educational success.

All these different noncognitive processes have been found to relate to academic performance to varying degrees. While g remains the best predictor of academic performance, accounting for between one quarter and one third of its variance, research has shown that noncognitive skills predict academic performance beyond g. In a sample of 16-year-olds from the United Kingdom, self-efficacy and personality, in addition to other constructs such as wellbeing, behavioural problems, health, and perceived home and school environments, collectively accounted for a comparable proportion of variance in academic performance as g ( Krapohl et al., 2014). In addition, measures of personality, self-regulation and motivation, are related to variation in academic performance beyond measures of cognitive ability (e.g. Tangney, Baumeister, & Boone, 2004; Diamond, 2013; Muenks, Wigfield, Yang, & O'Neal, 2017; Tangney et al., 2004; Garon-Carrier et al., 2016; Guay, Ratelle, Roy, & Litalien, 2010; E. M. Tucker-Drob & Harden, 2012, 2014; Chamorro-Premuzic, Harlaar, Greven, & Plomin, 2010; Tucker-Drob & Briley, 2012).

Twin studies of the association between noncognitive processes, general cognitive ability and academic performance

Behavioural genetic studies have examined the genetic and environmental underpinnings of personality traits, finding moderate genetic influences across personality characteristics, little to no contribution of shared environment, and an increasingly important role of nonshared environmental influences on personality traits over the lifespan (see Briley & Tucker-Drob, 2014, 2017 for comprehensive reviews of the available evidence). On the contrary, research that has investigated the genetic and environmental origins of self-regulatory processes, measured through executive functions, has found support for a major contribution of genetic factors ( Engelhardt, Briley, Mann, Harden, & Tucker-Drob, 2015; Laura E Engelhardt et al., 2016; Friedman & Miyake, 2017). Indeed, the heritability of executive functions was found to approach unity ( Engelhardt et al., 2015).

Less genetically informative research has been devoted to investigating the genetic and environmental underpinnings of motivational factors and students' approaches to learning. The available evidence suggest that motivational processes, such as self-efficacy and interest, are moderately heritable and that nonshared environmental factors contribute substantially to individual differences in motivation during childhood and adolescence across several countries ( Kovas et al., 2015; Tucker-Drob et al., 2016). Genetic and environmental effects on students' approaches to learning, measured as goal-orientation, have been found to shift with age, with environmental influences being the primary source of variation during childhood and a gradual increase in genetic influence during adolescence and adulthood ( Zheng et al., 2019). Combining evidence from phenotypic and genetic research points to how noncognitive skills represent a very broad, eclectic and complex phenotype.

In an attempt to dissect the complexity of the noncognitive phenotype, genetically informative studies have examined associations between a few 'key' educationally relevant noncognitive variables and academic performance, most notably, grit ( Duckworth, Peterson, Matthews, & Kelly, 2007) and motivation. Grit, a psychological construct that describes perseverance and passion to achieve long-term goals ( Duckworth et al., 2007), is closely related to the personality dimensions of conscientiousness ( Rimfeld, Kovas, Dale, & Plomin, 2016) and self-control ( Duckworth & Gross, 2014), sharing strong phenotypic and genetic correlations with them ( Malanchini et al., 2018). In sample of 16-year-olds, the modest correlation between grit and academic achievement was found to be largely due to shared genetic variance, and to a lower extent to nonshared environmental variance ( Rimfeld, et al., 2016). These genetic and environmental effects, however, could almost entirely be accounted for by conscientiousness, questioning the role off grit as a specific key component of academic success ( Credé, Tynan, & Harms, 2017; Rimfeld, et al., 2016).

Reciprocal models of the association between noncognitive factors and academic performance propose that their association is subject to a process of mutual influence that results in their relation being maintained over development (Morgan & Fuchs, 2007). Longitudinal studies have supported these reciprocal models, finding longitudinal developmental links between noncognitive characteristics and achievement (e.g. Chamorro-Premuzic et al., 2010; Guay et al., 2003; Marsh & Martin, 2011; Muijs, 1997). A handful of studies have examined the origins of such longitudinal links applying genetically informative designs. For example, the longitudinal relations between self-perceived ability and academic performance between the ages of 9 and 12 was found to be characterized by reciprocal links, mediated largely through genetic pathways ( Luo et al., 2010).

Two further studies in the same sample have explored associations between multiple aspects of motivated behaviour (self-perceived ability and interest) and teacher-rated achievement in the context of mathematics learning ( Luo, Kovas, Haworth, & Plomin, 2011) and reading ability ( Malanchini et al., 2017). In the context of mathematics achievement, one study found that while the link from earlier achievement (age 9) to subsequent motivation (age 12) was mostly attributable to genetic factors, the link from early motivation to subsequent achievement was mediated through both genetic and child-specific environmental pathways ( Luo et al., 2011). Results of the investigation into the association between motivation for reading and reading ability provided highly consistent results. Applying for the first time a genetically sensitive full cross-lagged panel analysis (ACE cross-lagged; Malanchini et al., 2017), the study found that, while the path from early reading to later variation in motivation was almost entirely genetic in origin, the path from early motivation to subsequent reading comprehension was explained by both genetic and environmental factors ( Malanchini et al., 2017). Interestingly, both studies found that longitudinal associations remained significant and similar in their aetiology even when statistically controlling for g ( Malanchini et al., 2017; Luo & Kovas, 2011).

While some genetically informative research has examined the genetic and environmental underpinnings of the association between targeted noncognitive characteristics and academic performance, a handful of investigations have examined the collective contribution of multiple aspects of the noncognitive umbrella to variation in academic achievement. In one of the most comprehensive investigations of educationally-relevant noncognitive skills to date, Tucker-Drob et al. (2016) found that two second-order latent factors that captured covariation among multiple measures of childhood character (motivation, attitudes and personality processes) were moderately heritable and shared genetic links with academic achievement, even after controlling for fluid intelligence ( Tucker-Drob et al., 2016).

An even more comprehensive investigation conducted in a partly overlapping sample of twins examined how multiple aspects of self-regulation, personality and motivation contributed, individually and collectively, to variation in reading and mathematics ability ( Malanchini et al., 2018). The investigation found that beyond cognitive ability, self-regulation contributed substantially to variation in reading and mathematics, and that these pathways were largely genetic in origin. In addition, while aspects of personality, motivation and attitudes related to conscientiousness (e.g. effortful persistence, self-discipline and diligence) did not account for further variation in academic achievement, aspects of personality, motivation and attitudes related to openness (e.g. curiosity, interest and self-efficacy) further contributed to academic performance, particularly in reading. These links between facets of openness and achievement was largely mediated by genetic effects. Remarkably, when examined in conjunction, measures of cognitive abilities, self-regulation, personality, motivation and attitudes towards learning accounted for the entirety of the genetic variance in reading and mathematics in this child sample ( Malanchini et al., 2018). No study to date has tested how these multiple noncognitive, cognitive and educational processes interact and influence each other over development applying formal longitudinal methods. Overall results are in line with the description of intellectual curiosity as the ' third pillar of academic performance' beyond intelligence and conscientiousness ( von Stumm, Hell, & Chamorro-Premuzic, 2011).

Molecular genetic research into educationally relevant noncognitive skills

While these two studies applied GWA discoveries in the field of educational attainment to the investigation of noncognitive skills, one recent study has looked into the heritability and covariation between multiple noncognitive skills using DNA-based methods. Estimating univariate heritability and pairwise genetic correlations in large samples of unrelated individuals using GCTA ( Yang et al., 2011), the investigation found that across multiple measures of noncognitive skills (including personality, self-regulation, and motivation), estimates of heritability and genetic correlations between measures were weak ( Morris, Smith, Berg, & Davies, 2018). In addition, all noncognitive measures shared weak genetic associations with educational or professional success. These results, inconsistent with the moderate heritability estimates and correlations emerging from twin research, could point to several weaknesses and limitations that currently exist in the quest to identify the genetic architecture of noncognitive skills.

The first challenge in pushing the identification of the genetic architecture of noncognitive skills forward is measuring them reliably, an issue that is rooted in their broad all-encompassing definition, as well as in the fact that measurement relies nearly exclusively on self-reports. The fact the noncognitive phenotype is an agglomerate of many different skills and characteristics that correlate to varying degrees poses a major challenge: the availability of a comprehensive enough battery of tests that would enable extraction of reliable components (e.g. Tucker-Drob et al., 2016). Reliable composite measures that reflect commonalities across noncognitive skills would likely result in advancing our understanding of the molecular genetic architecture of noncognitive skills. However, administering long batteries of noncognitive tests to the very large samples needed for GWA discovery is likely to prove challenging. One potential way forward could be creating short, easy to administer, online batteries that specifically target characteristics that are common across noncognitive skills in order to quickly extract few reliable measures. A second possibility would be to leverage recent advances in multivariate GWA methods, for example Genomic SEM ( Grotzinger et al., 2019), to create latent GWA analyses of noncognitive skills (Demange et al., in preparation).

The difficulty in reliably getting to the molecular basis of noncognitive skills points to a broader issue: the importance of phenotypes in genetic discoveries. Although this fundamental issue is beyond the scope of the current review, measurement heterogeneity is likely to have a major impact in GWA discoveries, particularly when working with complex behaviours and conditions such as human motivation or psychopathologies ( Chabris et al., 2013). Methods such as Genomic SEM that can parse generality from specificity are likely to contribute importantly to the quest to dissect the heterogeneity of phenotypes. Alternative toolkits are being developed, for example, to dissect the genetic heterogeneity in major depression ( McIntosh, Sullivan, & Lewis, 2019).

Towards a comprehensive, evidence-based model of learning: cognitive ability, academic performance and noncognitive skills in the context of genotypes and environments

Although noncognitive skills have been the subject of much less behavioural genetic research, recent studies have begun to clarify how genetic and environmental factors contribute to individual differences in this heterogeneous phenotype and, importantly, the genetic and environmental underpinnings for its association with academic performance and cognitive ability. Indeed, considering both cognitive and noncognitive skills and their biological and environmental underpinnings is fundamental if the goal is to move towards a comprehensive, evidence-based model of education.

Extant studies point to the great benefits that come from considering both cognitive and noncognitive skills in conjunction in order to predict life outcomes such as educational and professional success, morbidity and mortality. One study conducted in a large longitudinal sample has shown that the strength of the prediction of adult outcomes from childhood risk increased significantly when cognitive and noncognitive characteristics (childhood IQ and childhood self-control) were considered jointly rather than in isolation ( Caspi et al., 2016). In line with Pareto's principle, the study showed that around 20% of the population was found to account for around 80% of adult economically burdensome outcomes, from tobacco smoking to criminal convictions and hospitalizations. Childhood cognitive and noncognitive skills, together with growing up in socioeconomic disadvantage, predicted these economically burdensome outcomes with great accuracy (Area Under the Curve of .87; Caspi et al. 2016). Such evidence not only highlights the importance of considering cognitive and noncognitive skills in conjunction for both prediction and intervention purposes, but also of considering environmental risk factors together with genetic predispositions.

In addition to being partly rooted in genetic variation, the association between g, educational performance and life outcomes is also shaped by the socioecological context ( Engelhardt, Church, Harden, & Tucker-Drob, 2018) and its related behaviours, also described in the literature as the behavioural constellation of deprivation ( Pepper & Nettle, 2017). It has been proposed that the reduced wealth and influence that are generally associated with socioeconomic deprivation are likely to result in an increased inability to affect one's future outcomes, from educational attainment to illness. One of the psychological processes proposed to be key for these observed links between deprivation and unfavourable life outcomes is a lack of experienced and perceived personal control, since 'Limited personal control may include a restricted ability to ensure that returns on investments made in the present, for payoffs in the future, will be received' ( Pepper & Nettle 2017, p.3). As a consequence, motivational processes based on different expectations of the future may contribute to, or exacerbate, the discrepancy in life outcomes observed between individuals belonging to different socioeconomic brackets.

As previously discussed, social processes such as those included in the behavioural constellation of deprivation ( Pepper & Nettle, 2017) and genetic processes likely act in concert to give rise to variation in traits that are ultimately linked to differential life outcomes. Transactional models, rooted in GE correlation, provide a suitable framework for pushing our conceptualization of education towards embracing the important discoveries that stem from behavioral genetic research. As discussed earlier, transactional models propose that genotype-environment correlation promotes differences in environmental experiences, which in turn impact cognitive development and academic achievement (Briley & Tucker-Drob, 2013). Some of the genetically-influenced environmental experiences that have been associated with being exposed to, selecting, and evoking educationally relevant environments are noncognitive skills such as attitudes and motivation ( Tucker-Drob & Harden, 2012). In line with this framework, students would select, evoke and experience learning environments, partly depending on their differences in cognitive and noncognitive characteristics, which are themselves partly genetically influenced.

Six main criteria have been proposed as necessary in order to find empirical support for the transactional model of the association between noncognitive traits and academic achievement. First, a correlation between the noncognitive trait and achievement is necessary, although not sufficient. Second, their correlation should be significant beyond their association with general cognitive ability. Third, the model requires noncognitive factors to be moderately heritable. Fourth, there should be a degree of genetic correlation between the noncognitive trait and academic achievement. Fifth, the direction of causation, evaluated through longitudinal panel analyses, should be significant from the noncognitive trait to achievement. And sixth, environmental experiences should mediate the association between noncognitive traits and achievement though a genetic pathway ( Tucker-Drob & Harden, 2017). The evidence reviewed so far supports the first five criteria and consequently the possibility that transactional processes operate not only for cognitive ability and achievement, but also for noncognitive characteristics.'

Future research identifying the specific environmental experiences that mediate these genetic links will prove essential for the development of evidence-based interventions. As Petrill and Wilkerson state in an earlier review on the genetics of the association between intelligence and achievement: ' Far from passively receiving an educational program, children may be actively seeking out and receiving enriched environments based, in part, on genetic influences. Not only should our research begin to identify these multiple influences on intelligence and achievement, but our educational practices should also be more sensitive to these sources of individual differences' ( Petrill & Wilkerson, 2000).

Evidence stemming from behavioural genetic research on cognitive and educational traits has provided valuable insights into why such stark individual differences are observed at every stage over the lifespan. This wealth of knowledge should also guide how we evaluate educational interventions ( Sokolowski & Ansari, 2018). It is customary to evaluate educational interventions on the basis of two chief goals: first, interventions should provide a shift in the distribution of ability, which would indicate that every child benefit from the intervention; second, interventions should aim to reduce the gap between high and low achievers. As argued by Sokolowski and Ansari, while the first goal is feasible and highly desirable, as interventions should provide children with the opportunity to reach their full potential, given what we know from genetic research, the second goal may not be a reliable index of successful interventions as it is traditionally assumed. In fact, even if environmental experiences are equalized, remarkable individual differences will still be observed, as these are partly rooted in genetic differences between students ( Sokolowski & Ansari, 2018). While equal opportunities of access to educational resources are highly desirable, these are unlikely to result in equivalent abilities or achievement between students, which instead are the product of a complex interplay between genetic and environmental processes.

Implications for the present and future of educational practice.

We propose that the evidence stemming from decades of behavioural genetic research into cognitive ability and education is not only valuable in informing the evaluation of educational interventions but could also be integrated into the development and implementation of successful personalized education. Educational curricula would benefit from embracing individual differences between students, which are partly due to genetic differences between them ( Asbury & Plomin, 2013). Cutting-edge tools, like GPS, that are able to leverage information on genetic differences between individuals to provide a probabilistic index of dispositions and capabilities, could be integrated into the development of early interventions, particularly as an additional tool to inform early screening.

The ability in the future to predict educational difficulties early in development, even before they are manifested, is likely to be fundamental in ameliorating educational outcomes and experiences for all students, but particularly for those at risk of struggling with learning difficulties. Using genetic information as an additional early screening tool may therefore prevent the spiralling negative consequences associated with delayed diagnoses. The same principle can be applied to several other aspects of learning, including noncognitive skills.

There are several caveats and limitations that apply to the possibility of integrating genetic prediction into personalized approaches to learning and interventions. First and foremost, GPS prediction is far from perfect, accounting for at best ~15% of the variation in academic achievement at the end of compulsory education and substantially less at earlier ages ( Allegrini et al., 2019; Selzam et al., 2017; Rimfeld, Malanchini et al., 2018). Of course, no predictions in the behavioural sciences are perfect, which means that predictions at the individual level have very wide confidence intervals. Consequently, predicting variation in academic performance, particularly in the early years, from DNA alone, is unlikely to lead to accurate results for an individual, although at the extremes of the GPS distribution, substantial average differences can be predicted. For example, for the lowest decile of EA3, about 25% go to university, whereas about 75% from the highest decile go to university ( von Stumm et al., 2019).

Conclusions

Genetic research into cognitive ability and education has provided crucial insights into why children differ so widely in their abilities, attitudes and appetites for learning. As our knowledge continues growing and new methods and tools are developed, the information that emerges from genetic research becomes increasingly tangible, translational and actionable, for example by informing the way we evaluate and develop educational interventions. Decades of twin studies and, more recently DNA-based discoveries, point to the importance of genetic variation in cognitive ability and education and in their close association. These genetic influences, however, do not just happen in isolation, rather they are shaped by their interplay with the environment. Recent advances in molecular genetics provide increasingly comprehensive and accessible tools that have been applied to the investigation of these complex processes of correlation and interaction between genes and environments.

While our knowledge of the genetic and environmental underpinnings of the association between cognitive ability and education has grown exponentially in recent years, several gaps in our knowledge still exist. First and foremost, longitudinal genetically informative research into the association between cognitive ability and education is still lacking. This is particularly important as genetic and environmental influences on cognitive ability and educationally relevant processes are not static, rather they evolve and shift during development (e.g. Zheng et al., 2019; Briley & Tucker-Drob, 2017). In addition, we need to develop novel ways of assessing cognitive and educational traits briefly and reliably in large samples to result in breakthroughs in genetic discoveries. Lastly, research investigating how biologically relevant models of cognitive ability and educational traits can be integrated in educational practices, for example, by running randomized control trials of intervention programs, is still lacking. We look forward to the next decades of behavioural genetic research into cognitive ability and education, as we continue learning about learning.

Acknowledgements

We gratefully acknowledge the ongoing contribution of the participants in the Twins Early Development Study (TEDS) and their families. TEDS is supported by a program grant to R.P. from the UK Medical Research Council (MR/M021475/1 and previously G0901245), with additional support from the US National Institutes of Health (AG046938). The research leading to these results has also received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ grant agreement n. 602768. R.P. is supported by a Medical Research Council Professorship award (G19/2). M.M.'s work is partly supported by the David Wechsler Early Career grant for innovative work in cognition.

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Cognitive ability and education: how behavioural genetic research has advanced our knowledge and understanding of their association

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