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Polygenic scores for social science: Clarification, consensus, and controversy
Abstract
In this response, I focus on clarifying my arguments, highlighting consensus, and addressing competing views about the utility of polygenic scores (PGSs) for social science. I also discuss an assortment of expansions to my arguments and suggest alternative approaches. I conclude by reiterating the need for caution and appropriate scientific skepticism.
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Gene–environment correlations affect associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here we showed in up to 43,516 British siblings that educational attainment polygenic scores capture gene–environment correlations, and that migration extends these gene–environment correlations beyond the family to broader geographic regions. We then ran GWASs on 56 complex traits in up to 254,387 British individuals. Controlling for geographic regions significantly decreased the heritability for socioeconomic status (SES)-related traits, most strongly for educational attainment and income. For most traits, controlling for regions significantly reduced genetic correlations with educational attainment and income, most significantly for body mass index/body fat, sedentary behavior and substance use, consistent with gene–environment correlations related to regional socio-economic differences. The effects of controlling for birthplace and current address suggest both passive and active sources of gene–environment correlations. Our results show that the geographic clustering of DNA and SES introduces gene–environment correlations that affect GWAS results.
Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment ( r = 0.739, standard error (s.e.) = 0.086) and cognitive ability ( r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes.
We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.
The Genetic Lottery: Why DNA Matters for Social Equality aims to convince the reader that recent methodological developments in human genetics should change the broader societal conversation about redistributive justice (Harden, 2021). The author, Dr. Kathryn Paige Harden, is a Professor of Psychology at the University of Texas, Austin, who specializes in behavioral genetics. Her book starts from the premise that human behaviors, and in particular educational attainment, are “heritable,” i.e., that within a study sample, some fraction of the phenotypic variance is explained by differences in genotypes. As is described, we can now identify some of the genetic loci associated with trait variation through genome‐wide association studies (GWAS) and make predictions‐currently, quite noisy predictions‐of individual outcomes from genotypes. In the author's view, GWAS findings underscore that people differ not only in the social circumstances into which they are born but also in the genetics that they happen to inherit. Since neither social circumstances nor genetics are earned or chosen, both result from “luck”. The book argues that both sources of luck contribute commensurately to social inequalities in educational attainment and ultimately in income, and therefore that genetics is needed in order to better understand and redress social inequalities. In particular, in Harden's view, recent GWAS findings should lead us to be mindful of principles of equity and not just equality. This article is protected by copyright. All rights reserved
Professing interactionist bio + social terminology, contemporary biocriminology asserts a break from its biologically essentialist past. Assurances notwithstanding, whether biocriminology has undergone a decisive paradigm shift rejecting notions of biological criminals and bad brains remains uncertain. Unfortunately, discussions of biocriminology's assumptions are mired in politics, obscuring important scientific issues. Motivated to clarify misunderstanding, I address the ontoepistemology of biocriminology from a scientific realist perspective. Drawing on familiar notions of crime as a social construction, I explain how and why biocriminology's ontoepistemology is inconsistent with the social reality of crime for scientific not ideological reasons. I explain that recognizing crime as a social construction does not imply that crime is not real or objective and cannot be studied scientifically. On the contrary, the irreducibly social nature of crime requires that scientific realists reject assumptions of “biological crime” as well as the biologically reductionist epistemology on which biocriminology depends.
During the past decade, polygenic scores have become a fast-growing area of research in the behavioural sciences. The ability to directly assess people’s genetic propensities has transformed research by making it possible to add genetic predictors of traits to any study. The value of polygenic scores in the behavioural sciences rests on using inherited DNA differences to predict, from birth, common disorders and complex traits in unrelated individuals in the population. This predictive power of polygenic scores does not require knowing anything about the processes that lie between genes and behaviour. It also does not mandate disentangling the extent to which the prediction is due to assortative mating, genotype–environment correlation, or even population stratification. Although bottom-up explanation from genes to brain to behaviour will remain the long-term goal of the behavioural sciences, prediction is also a worthy achievement because it has immediate practical utility for identifying individuals at risk and is the necessary first step towards explanation. A high priority for research must be to increase the predictive power of polygenic scores to be able to use them as an early warning system to prevent problems.
Sociogenomics examines the extent to which genetic differences between individuals relate to differences in social and economic behaviors and outcomes. The field evokes mixed reactions. For some, sociogenomics runs the risk of normalizing eugenic attitudes and legitimizing social inequalities. For others, sociogenomics brings the promise of more robust and nuanced understandings of human behavior. Regardless, a history of misuse and misapplication of genetics raises important questions about researchers’ social responsibilities. This paper draws on semi-structured interviews with sociogenomics researchers who investigate intelligence and educational attainment. It does so to understand how researcher’s motivations for engaging in a historically burdened field connect to their views on social responsibility and the challenges that come with it. In interviews, researchers highlighted the trade-off between engaging in socially contested research and the potential benefits their work poses to the social sciences and clinical research. They also highlighted the dilemmas of engaging with the public, including the existence of multiple publics. Finally, researchers elucidated uncertainties over what social responsibility is in practice and whether protecting against the misuse and misinterpretation of their research is wholly possible. This paper concludes by offering ways to address some of the challenges of social responsibility in the production of knowledge.
Genomic research led the way in open science, a tradition continued by genome-wide association studies (GWAS)—through the sharing of materials, results, and data. Coordinated quality control procedures also contributed to robust findings. However, recent years have seen declines in GWAS transparency. Here, we assess some shifts away from open science practices with the aim of stimulating a discussion of these issues.
Motivation
Polygenic scores have become a central tool in human genetics research. LDpred is a popular method for deriving polygenic scores based on summary statistics and a matrix of correlation between genetic variants. However, LDpred has limitations that may reduce its predictive performance.
Results
Here we present LDpred2, a new version of LDpred that addresses these issues. We also provide two new options in LDpred2: a “sparse” option that can learn effects that are exactly 0, and an “auto” option that directly learns the two LDpred parameters from data. We benchmark predictive performance of LDpred2 against the previous version on simulated and real data, demonstrating substantial improvements in robustness and predictive accuracy compared to LDpred1. We then show that LDpred2 also outperforms other polygenic score methods recently developed, with a mean AUC over the 8 real traits analyzed here of 65.1%, compared to 63.8% for lassosum, 62.9% for PRS-CS and 61.5% for SBayesR. Note that LDpred2 provides more accurate polygenic scores when run genome-wide, instead of per chromosome.
Availability
LDpred2 is implemented in R package bigsnpr.
Supplementary information
Supplementary data are available at Bioinformatics online.
Population stratification continues to bias the results of genome-wide association studies (GWAS). When these results are used to construct polygenic scores, even subtle biases can cumulatively lead to large errors. To study the effect of residual stratification, we simulated GWAS under realistic models of demographic history. We show that when population structure is recent, it cannot be corrected using principal components of common variants because they are uninformative about recent history. Consequently, polygenic scores are biased in that they recapitulate environmental structure. Principal components calculated from rare variants or identity-by-descent segments can correct this stratification for some types of environmental effects. While family-based studies are immune to stratification, the hybrid approach of ascertaining variants in GWAS but re-estimating effect sizes in siblings reduces but does not eliminate stratification. We show that the effect of population stratification depends not only on allele frequencies and environmental structure but also on demographic history.
Although early-life adversity can undermine healthy development, children growing up in harsh environments may develop intact, or even enhanced , skills for solving problems in high-adversity contexts (i.e., “hidden talents”). Here we situate the hidden talents model within a larger interdisciplinary framework. Summarizing theory and research on hidden talents, we propose that stress-adapted skills represent a form of adaptive intelligence that enables individuals to function within the constraints of harsh, unpredictable environments. We discuss the alignment of the hidden talents model with current knowledge about human brain development following early adversity; examine potential applications of this perspective to multiple sectors concerned with youth from harsh environments, including education, social services, and juvenile justice; and compare the hidden talents model with contemporary developmental resilience models. We conclude that the hidden talents approach offers exciting new directions for research on developmental adaptations to childhood adversity, with translational implications for leveraging stress-adapted skills to more effectively tailor education, jobs, and interventions to fit the needs and potentials of individuals from a diverse range of life circumstances. This approach affords a well-rounded view of people who live with adversity that avoids stigma and communicates a novel, distinctive, and strength-based message.
There has been widespread adoption of genome wide summary scores (polygenic scores) as tools for studying the importance of genetics and associated life course mechanisms across a range of demographic and socioeconomic outcomes. However, an often unacknowledged issue with these studies is that parental genetics impact both child environments and child genetics, leaving the effects of polygenic scores difficult to interpret. This paper uses multi-generational data containing polygenic scores for parents (n = 7193) and educational outcomes for adopted (n = 855) and biological (n = 20,939) children, many raised in the same families, which allows us to separate the influence of parental polygenic scores on children outcomes between environmental (adopted children) and environmental and genetic (biological children) effects. Our results complement recent work on “genetic nurture” by showing associations of parental polygenic scores with adopted children’s schooling, providing additional evidence that polygenic scores combine genetic and environmental influences and that research designs are needed to separate these estimated impacts.
The increasing predictive power of polygenic scores for education has led to their promotion by some as a potential tool for genetically informed policy. How accurately polygenic scores predict an individual pupil’s educational performance conditional on other phenotypic data is however not well understood. Using data from a UK cohort study with data linkage to national schooling records, we investigated how accurately polygenic scores for education predicted pupils’ test score achievement. We also assessed the performance of polygenic scores over and above phenotypic data that are available to schools. Across our sample, there was high overlap between the polygenic score and achievement distributions, leading to poor predictive accuracy at the individual level. Prediction of educational outcomes from polygenic scores were inferior to those from parental socioeconomic factors. Conditional on prior achievement, polygenic scores failed to accurately predict later achievement. Our results suggest that while polygenic scores can be informative for identifying group level differences, they currently have limited use for accurately predicting individual educational performance or for personalised education.
Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
Over the past several decades, Gottfredson & Hirschi's self-control theory (SCT) has dominated research on self-control and crime. In this review, I assess the current state of self-control knowledge and encourage the field to move beyond SCT, as its peculiar conceptualization of self-control and causal models presents challenges for integrative scholarship. Drawing heavily on scholarship outside criminology, I clarify the definition of self-control; describe the malleable nature of trait self-control; highlight its situational variability as state self-control; and consider the multiplicity of contextual, situational, and individual factors that affect its operation in relation to crime. This specification of contingencies and the interplay between impulse strength and control efforts in the process of self-control is intended as a springboard for research moving beyond SCT and its key premise that self-control (ability) is sufficient to explain individual variation in crime (i.e., is tantamount to criminality). Finally, I address what I see as important areas for further study in light of current knowledge.
Expected final online publication date for the Annual Review of Criminology, Volume 3 is January 13, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Intelligence typically is defined as consisting of “adaptation to the environment” or in related terms. Yet, it is not clear that “general intelligence” or g, traditionally conceptualized in terms of a general factor in a psychometrically-based hierarchical model of intelligence, provides an optimal way of defining intelligence as adaptation to the environment. Such a definition of adaptive intelligence would need to be biologically based in terms of evolutionary theory, would need to take into account the cultural context of adaptation, and would need to take into account whether thought and behavior labeled as “adaptively intelligent” actually contributed to the perpetuation of the human and other species, or whether it was indifferent or actually destructive to this perpetuation. In this article, I consider the similarities and differences between “general intelligence” and “adaptive intelligence,” as well as the implications especially of the differences.
Since the inception of large‐scale human genome research, there has been much caution about the risks of exacerbating a number of socially dangerous attitudes linked to human genetics. These attitudes are usually labeled with one of a family of genetic or genomic “isms” or “ations” such as “genetic essentialism,” “genetic determinism,” “genetic reductionism,” “geneticization,” “genetic stigmatization,” and “genetic discrimination.” The psychosocial processes these terms refer to are taken to exacerbate several ills that are similarly labeled, from medical racism and psychological fatalism to economic exploitation and social exclusion. But as genomic information becomes more familiar in clinical and research settings as well as other life activities, do we need to continue to worry so much about this family of attitudes and their impact on existing problems?
In genomics, the underlying anxiety has been that disclosure of genomic information will trigger a series of (seemingly unavoidable) negative responses that will affect individuals, their families, and their communities at large. The fundamental social challenges that hyperbolic genomic messaging, low genomic literacy, and “folk biology” help sustain remain to be addressed. If we hope to break the cycle of genomic isms and ations, we will have to get better at resisting overinterpretations of the relevance that genomics has for people’s future potentials, ancestral vulnerabilities, community memberships, and ethnic affiliations.
Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.
Editorial note:
This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
Several recent papers have reported strong signals of selection on European polygenic height scores. These analyses used height effect estimates from the GIANT consortium and replication studies. Here, we describe a new analysis based on the the UK Biobank (UKB), a large, independent dataset. We find that the signals of selection using UKB effect estimates are strongly attenuated or absent. We also provide evidence that previous analyses were confounded by population stratification. Therefore, the conclusion of strong polygenic adaptation now lacks support. Moreover, these discrepancies highlight (1) that methods for correcting for population stratification in GWAS may not always be sufficient for polygenic trait analyses, and (2) that claims of differences in polygenic scores between populations should be treated with caution until these issues are better understood.
Editorial note:
This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.
Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when making inference from genotype data in large studies.
In this chapter, I examine the connections between interpersonal racial discrimination, cultural adaptations, including racial socialization, and crime. I focus on racialized experiences as risk factors for crime in the context of a racialized general theory of crime, the social schematic theory (SST), with a particular emphasis on the criminogenic effects of anti-Black interpersonal discrimination. I expound on the evolutionary developmental underpinnings of SST to elucidate both the nature and logic of allegedly ‘maladaptive’ adaptions to racism. Next, I extend this theoretical framework to fill gaps in undertheorized, yet salient, structure shapes culture arguments in criminology. My aim is to unite findings and reframe them within an approach that focuses on harsh, unpredictable environments and contextually appropriate adaptations with an underlying evolutionary developmental logic. The end result is a framework that links racial discrimination and “race-neutral” risk factors (profoundly shaped by racism) to psychosocial orientations that tend to increase the risk of street crime and other behaviors that can reinforce disadvantage through their consequences. I conclude by discussing the implications of this perspective drawing on scholarship that points to the need to ameliorate harsh, racist contexts of development as well as working with rather than against the strengths of stress-adapted individuals.
People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to the development of larger brains. We tested this hypothesis using four large imaging genetics studies (combined N = 7965) with polygenic scores derived from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We conducted meta-analysis to test associations among participants' genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants' education polygenic scores and their cognitive test performance. Effect sizes were larger in the population-based samples than in the convenience-based samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging to understand neurobiology linking genetics with cognitive performance.
Neuroticism has been shown to act as an important risk factor for major depressive disorder (MDD). Genetic and neuroimaging research has independently revealed biological correlates of neurotic personality including cortical alterations in brain regions of high relevance for affective disorders. Here we investigated the influence of a polygenic score for neuroticism (PGS) on cortical brain structure in a joint discovery sample of n = 746 healthy controls (HC) and n = 268 MDD patients. Findings were validated in an independent replication sample (n = 341 HC and n = 263 MDD). Subgroup analyses stratified for case-control status and analyses of associations between neurotic phenotype and cortical measures were carried out. PGS for neuroticism was significantly associated with a decreased cortical surface area of the inferior parietal cortex, the precuneus, the rostral cingulate cortex and the inferior frontal gyrus in the discovery sample. Similar associations between PGS and surface area of the inferior parietal cortex and the precuneus were demonstrated in the replication sample. Subgroup analyses revealed negative associations in the latter regions between PGS and surface area in both HC and MDD subjects. Neurotic phenotype was negatively correlated with surface area in similar cortical regions including the inferior parietal cortex and the precuneus. No significant associations between PGS and cortical thickness were detected. The morphometric overlap of associations between both PGS and neurotic phenotype in similar cortical regions closely related to internally focused cognition points to the potential relevance of genetically shaped cortical alterations in the development of neuroticism.
Significance
Genome-wide association study (GWAS) discoveries about educational attainment have raised questions about the meaning of the genetics of success. These discoveries could offer clues about biological mechanisms or, because children inherit genetics and social class from parents, education-linked genetics could be spurious correlates of socially transmitted advantages. To distinguish between these hypotheses, we studied social mobility in five cohorts from three countries. We found that people with more education-linked genetics were more successful compared with parents and siblings. We also found mothers’ education-linked genetics predicted their children’s attainment over and above the children’s own genetics, indicating an environmentally mediated genetic effect. Findings reject pure social-transmission explanations of education GWAS discoveries. Instead, genetics influences attainment directly through social mobility and indirectly through family environments.
Recent studies evince that interpersonal racial discrimination (IRD) increases the risk of crime among African Americans and familial racial socialization fosters resilience to discrimination's criminogenic effects. Yet, studies have focused on the short-term effects of IRD and racial socialization largely among adolescents. In this study, we seek to advance knowledge by elucidating how racialized experiences—in interactions and socialization—influence crime for African Americans over time. Elaborating Simons and Burt's (2011) social schematic theory, we trace the effects of childhood IRD and familial racial socialization on adult offending through cognitive and social pathways and their interplay. We test this life-course SST model using data from the FACHS, a multisite study of Black youth and their families from ages 10 to 25. Consistent with the model, analyses reveal that the criminogenic consequences of childhood IRD are mediated cognitively by a criminogenic knowledge structure and socially through the nature of social relationships in concert with ongoing offending and discrimination experiences. Specifically, by increasing criminogenic cognitive schemas, IRD decreases embeddedness in supportive romantic, educational, and employment relations, which influence social schemas and later crime. Consonant with expectations, the findings also indicate that racial socialization provides enduring resilience by both compensating for and buffering discrimination's criminogenic effects.
Socioeconomic differences in behaviour are pervasive and well documented, but their causes are not yet well understood. Here, we make the case that there is a cluster of behaviours associated with lower socioeconomic status, which we call the behavioural constellation of deprivation. We propose that the relatively limited control associated with lower socioeconomic status curtails the extent to which people can expect to realise deferred rewards, leading to more present-oriented behaviour in a range of domains. We illustrate this idea using the specific factor of extrinsic mortality risk, an important factor in evolutionary theoretical models. We emphasise the idea that the present-oriented behaviours of the constellation are a contextually appropriate response to structural and ecological factors, rather than pathology or a failure of willpower. We highlight some principles from evolutionary theoretical models that can deepen our understanding of how socioeconomic inequalities can become amplified and embedded. These principles are that: 1) Small initial disparities can lead to larger eventual inequalities, 2) Feed-back loops can operate to embed early life circumstances, 3) Constraints can breed further constraints, and 4) Feed-back loops can operate over generations. We discuss some of the mechanisms by which socioeconomic status may influence behaviour. We then review how the contextually appropriate response perspective that we have outlined fits with other findings about control and temporal discounting. Finally, we discuss the implications of this interpretation for research and policy.
Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R-2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.
Uchiyama et al. propose a unified model linking cultural evolutionary theory to behavior genetics (BG) to enhance generalizability, enrich explanation, and predict how social factors shape heritability estimates. A consideration of culture evolution is beneficial but insufficient for purpose. I submit that their proposed model is underdeveloped and their emphasis on heritability estimates misguided. I discuss their ambiguous conception of culture, neglect of social structure, and the lack of a general theory in BG.
Behavior genetics is a controversial science. For decades, scholars have sought to understand the role of heredity in human behavior and life-course outcomes. Recently, technological advances and the rapid expansion of genomic databases have facilitated the discovery of genes associated with human phenotypes like educational attainment and substance use disorders. To maximize the potential of this flourishing science, and to minimize potential harms, careful analysis of what it would mean for genes to be causes of human behavior is needed. In this paper, we advance a framework for identifying instances of genetic causes, interpreting those causal relationships, and applying them to advance causal knowledge more generally in the social sciences. Central to thinking about genes as causes is counterfactual reasoning, the cornerstone of causal thinking in statistics, medicine, and philosophy. We argue that within-family genetic effects represent the product of a counterfactual comparison in the same way as average treatment effects from randomized controlled trials (RCTs). Both average treatment effects from RCTs and within-family genetic effects are shallow causes: they operate within intricate causal systems ( non-unitary ), produce heterogeneous effects across individuals ( non-uniform ), and are not mechanistically informative ( non-explanatory ). Despite these limitations, shallow causal knowledge can be used to improve understanding of the etiology of human behavior and to explore sources of heterogeneity and fade-out in treatment effects.
The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a “cautionary tale.” The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher’s “infinite infinitesimal allele” model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome’s underlying complexities and the limitations of Fisher’s model in the postgenomics era.
The 2010 special issue of Journal of Health and Social Behavior, titled “Fifty Years of Medical Sociology,” defined the contours of the medical sociological perspective. We use this as a backdrop to outline and assess the continued integration of genetics into medical sociology research. We contend that the explosion of genetic and epigenetic data in population health data sources has made the medical sociological perspective increasingly relevant to researchers outside of sociology, including public health, epidemiology, and quantitative genetics. We describe vast, underappreciated, and mostly unsolved challenges that limit the scientifically appropriate interest in incorporating genetics into existing paradigms. It is our hope that medical sociologists continue this integration but redouble efforts to maintain the core insights in social science research, such as the importance of environmental and structural (i.e., nonbiological) factors in determining health processes and outcomes and the use of rich, integrated, and rigorous empirical analyses.
The first comprehensive synthesis on development and evolution: it applies to all aspects of development, at all levels of organization and in all organisms, taking advantage of modern findings on behavior, genetics, endocrinology, molecular biology, evolutionary theory and phylogenetics to show the connections between developmental mechanisms and evolutionary change. This book solves key problems that have impeded a definitive synthesis in the past. It uses new concepts and specific examples to show how to relate environmentally sensitive development to the genetic theory of adaptive evolution and to explain major patterns of change. In this book development includes not only embryology and the ontogeny of morphology, sometimes portrayed inadequately as governed by "regulatory genes," but also behavioral development and physiological adaptation, where plasticity is mediated by genetically complex mechanisms like hormones and learning. The book shows how the universal qualities of phenotypes--modular organization and plasticity--facilitate both integration and change. Here you will learn why it is wrong to describe organisms as genetically programmed; why environmental induction is likely to be more important in evolution than random mutation; and why it is crucial to consider both selection and developmental mechanism in explanations of adaptive evolution. This book satisfies the need for a truly general book on development, plasticity and evolution that applies to living organisms in all of their life stages and environments. Using an immense compendium of examples on many kinds of organisms, from viruses and bacteria to higher plants and animals, it shows how the phenotype is reorganized during evolution to produce novelties, and how alternative phenotypes occupy a pivotal role as a phase of evolution that fosters diversification and speeds change. The arguments of this book call for a new view of the major themes of evolutionary biology, as shown in chapters on gradualism, homology, environmental induction, speciation, radiation, macroevolution, punctuation, and the maintenance of sex. No other treatment of development and evolution since Darwin's offers such a comprehensive and critical discussion of the relevant issues. Developmental Plasticity and Evolution is designed for biologists interested in the development and evolution of behavior, life-history patterns, ecology, physiology, morphology and speciation. It will also appeal to evolutionary paleontologists, anthropologists, psychologists, and teachers of general biology.
By showing how and why human nature is what it is, evolutionary theory can help us see better what we need to do to improve the human condition. Following evolutionary theory to its logical conclusion, Death, Hope and Sex uses life history theory and attachment theory to construct a model of human nature in which critical features are understood in terms of the development of alternative reproductive strategies contingent on environmental risk and uncertainty. James Chisholm examines the implications of this model for perspectives on concerns associated with human reproduction, including teen pregnancy, and young male violence. He thus develops new approaches for thorny issues such as the nature-nurture and mind-body dichotomies. Bridging the gap between the social and biological sciences, this far-reaching volume will be a source of inspiration, debate and discussion for all those interested in the evolution of human nature and the potential for an evolutionary humanism.
Recent years have seen the birth of sociogenomics via the infusion of molecular genetic data. We chronicle the history of genetics, focusing particularly on post-2005 genome-wide association studies, the post-2015 big data era, and the emergence of polygenic scores. We argue that understanding polygenic scores, including their genetic correlations with each other, causation, and underlying biological architecture, is vital. We show how genetics can be introduced to understand a myriad of topics such as fertility, educational attainment, intergenerational social mobility, well-being, addiction, risky behavior, and longevity. Although models of gene-environment interaction and correlation mirror agency and structure models in sociology, genetics is yet to be fully discovered by this discipline. We conclude with a critical reflection on the lack of diversity, nonrepresentative samples, precision policy applications, ethics, and genetic determinism. We argue that sociogenomics can speak to long-standing sociological questions and that sociologists can offer innovative theoretical, measurement, and methodological innovations to genetic research.
Expected final online publication date for the Annual Review of Sociology, Volume 46 is July 30, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Social science genetics is concerned with understanding whether, how and why genetic differences between human beings are linked to differences in behaviours and socioeconomic outcomes. Our review discusses the goals, methods, challenges and implications of this research endeavour. We survey how the recent developments in genetics are beginning to provide social scientists with a powerful new toolbox they can use to better understand environmental effects, and we illustrate this with several substantive examples. Furthermore, we examine how medical research can benefit from genetic insights into social-scientific outcomes and vice versa. Finally, we discuss the ethical challenges of this work and clarify several common misunderstandings and misinterpretations of genetic research on individual differences. Harden and Koellinger discuss the goals, methods and challenges of social science genetics, which aims to unravel the genetic underpinnings of individual differences in social, behavioural and health outcomes.
Education has been found to have a positive influence on intelligence, but to be able to inform policy, it is important to analyse whether the observed association depends on the educational duration and intelligence prior to variations in educational attainment. Therefore, a longitudinal cohort study was conducted of all members of the Metropolit 1953 Danish Male Birth Cohort who were intelligence tested at age 12 and appeared before the Danish draft boards (N = 7389). A subpopulation also participated in the Copenhagen Aging and Midlife Biobank (N = 1901). The associations of educational attainment with intelligence in young adulthood and midlife were estimated by use of general linear regression with adjustment for intelligence test score at age 12 and family socioeconomic position. Results showed a positive association of educational attainment with intelligence test scores in both young adulthood and midlife after prior intelligence had been taken into account. The marginal cognitive benefits depended on the educational duration but did not reach a plateau until 17 years. Further, intelligence test score at age 12 was found to modify the association, suggesting that individuals with low intelligence in childhood derive the largest benefit from education. Comparing the strength of the association observed among participants and non-participants in our midlife study, we showed that selection due to loss to follow-up might bias the investigated association towards the null. This might explain previous studies' findings of relatively small cognitive benefits. In conclusion, education seems to constitute a promising method for raising intelligence, especially among the least advantaged individuals.
Much time and effort, as well as funding, is being devoted to Genome Wide Association Studies (GWAS) for identifying genetic causes of variation (single nucleotide polymorphisms or SNPs) in human cognitive abilities and educational attainments (CA and EA). After years of finding only very weak associations, usually failing to replicate, attention has turned to aggregates of otherwise non-significant SNPs (called polygenic scores, or PGS) and some associations with traits are now being reported. Here we show how, in the context of CA and EA as approximation measures, spurious correlations in GWAS/PGS can arise in a number of ways, particularly from genetic population structure. We review recent studies suggesting that attempts to control for such confounds have been quite inadequate, and also criticize the underlying statistical assumptions and genetic model.
Efforts to link variation in the human genome to phenotypes have progressed at a tremendous pace in recent decades. Most human traits have been shown to be affected by a large number of genetic variants across the genome. To interpret these associations and to use them reliably—in particular for phenotypic prediction—a better understanding of the many sources of genotype-phenotype associations is necessary. We summarize the progress that has been made in this direction in humans, notably in decomposing direct and indirect genetic effects as well as population structure confounding. We discuss the natural next steps in data collection and methodology development, with a focus on what can be gained by analyzing genotype and phenotype data from close relatives.
Environmental enrichment (EE), comprising positive physical (exercise) and cognitive stimuli, influences neuronal structure and usually improves brain function. The promise of EE as a preventative strategy against neuropsychiatric disease is especially high during early postnatal development when the brain is still amenable to reorganization. Despite the fact that male and female brains differ in terms of connectivity and function that may reflect early life experiences, knowledge of the neural substrates and mechanisms by which such changes arise remains limited. This study compared the impact of EE combined with physical activity on neuroplasticity and its functional consequences in adult male and female rats; EE was provided during the first 3 months of life and our analysis focused on the hippocampus, an area implicated in cognitive behavior as well as the neuroendocrine response to stress. Both male and female rats reared in EE displayed better object recognition memory than their control counterparts. Interestingly, sex differences were revealed in the effects of EE on time spent exploring the objects during this test. Independently of sex, EE increased hippocampal turnover rates of dopamine and serotonin and reduced expression of 5-HT1A receptors; in addition, EE upregulated expression of synaptophysin, a presynaptic protein, in the hippocampus. As compared to their respective controls, EE-exposed males exhibited parallel increases in phosphorylated Tau and the GluN2B receptor, whereas females responded to EE with reduced hippocampal levels of glutamate and GluN2B. Together, these observations provide further evidence on the differential effects of EE on markers of hippocampal neuroplasticity in males and females.
Hardly a month goes by without a media report proclaiming that researchers have discovered the gene for some complex human behavior or trait—intelligence, dyslexia, shyness, homosexuality. The practical implications of genetic research can bring great good—relieving parents of self-blame for a child's schizophrenia or autism and possibly treating genetic diseases in the future. Other findings—or pernicious interpretations of them—can cause great harm, for example, by establishing flawed connections between genetics, race, and educational attainment.
Wrestling with Behavioral Genetics brings together an interdisciplinary group of contributors—human geneticists, humanists, social scientists, lawyers, and journalists—to discuss the ethical and social implications of behavioral genetics research. The essays give readers the necessary tools to critically analyze the findings of behavioral geneticists, explore competing interpretations of the ethical and social implications of those findings, and engage in a productive public conversation about them.
This volume provides an accessible introduction to a fascinating and controversial science and the societal and individual implications of its continuing development.
The theory of multiple intelligences (MI) seeks to describe and encompass the range of human cognitive capacities. In challenging the concept of general intelligence, we can apply an MI perspective that may provide a more useful approach to cognitive differences within and across species.
The vast majority of genome-wide association studies (GWASs) are performed in Europeans, and their transferability to other populations is dependent on many factors (e.g., linkage disequilibrium, allele frequencies, genetic architecture). As medical genomics studies become increasingly large and diverse, gaining insights into population history and consequently the transferability of disease risk measurement is critical. Here, we disentangle recent population history in the widely used 1000 Genomes Project reference panel, with an emphasis on populations underrepresented in medical studies. To examine the transferability of single-ancestry GWASs, we used published summary statistics to calculate polygenic risk scores for eight well-studied phenotypes. We identify directional inconsistencies in all scores; for example, height is predicted to decrease with genetic distance from Europeans, despite robust anthropological evidence that West Africans are as tall as Europeans on average. To gain deeper quantitative insights into GWAS transferability, we developed a complex trait coalescent-based simulation framework considering effects of polygenicity, causal allele frequency divergence, and heritability. As expected, correlations between true and inferred risk are typically highest in the population from which summary statistics were derived. We demonstrate that scores inferred from European GWASs are biased by genetic drift in other populations even when choosing the same causal variants and that biases in any direction are possible and unpredictable. This work cautions that summarizing findings from large-scale GWASs may have limited portability to other populations using standard approaches and highlights the need for generalized risk prediction methods and the inclusion of more diverse individuals in medical genomics.
This brief review summarizes 60 years of conceptual advances that have demonstrated a role for active changes in neuronal connectivity as a controller of behavior and behavioral change. Seminal studies in the first phase of the six‐decade span of this review firmly established the cellular basis of behavior – a concept that we take for granted now, but which was an open question at the time. Hebbian plasticity, including long‐term potentiation and long‐term depression, was then discovered as being important for local circuit refinement in the context of memory formation and behavioral change and stabilization in the mammalian central nervous system. Direct demonstration of plasticity of neuronal circuit function in vivo , for example, hippocampal neurons forming place cell firing patterns, extended this concept. However, additional neurophysiologic and computational studies demonstrated that circuit development and stabilization additionally relies on non‐Hebbian, homoeostatic, forms of plasticity, such as synaptic scaling and control of membrane intrinsic properties. Activity‐dependent neurodevelopment was found to be associated with cell‐wide adjustments in post‐synaptic receptor density, and found to occur in conjunction with synaptic pruning. Pioneering cellular neurophysiologic studies demonstrated the critical roles of transmembrane signal transduction, NMDA receptor regulation, regulation of neural membrane biophysical properties, and back‐propagating action potential in critical time‐dependent coincidence detection in behavior‐modifying circuits. Concerning the molecular mechanisms underlying these processes, regulation of gene transcription was found to serve as a bridge between experience and behavioral change, closing the ‘nature versus nurture’ divide. Both active DNA (de)methylation and regulation of chromatin structure have been validated as crucial regulators of gene transcription during learning. The discovery of protein synthesis dependence on the acquisition of behavioral change was an influential discovery in the neurochemistry of behavioral modification. Higher order cognitive functions such as decision making and spatial and language learning were also discovered to hinge on neural plasticity mechanisms. The role of disruption of these processes in intellectual disabilities, memory disorders, and drug addiction has recently been clarified based on modern genetic techniques, including in the human.
image The area of neural plasticity and behavior has seen tremendous advances over the last six decades, with many of those advances being specifically in the neurochemistry domain. This review provides an overview of the progress in the area of neuroplasticity and behavior over the life‐span of the Journal of Neurochemistry. To organize the broad literature base, the review collates progress into fifteen broad categories identified as ‘conceptual advances’, as viewed by the author. The fifteen areas are delineated in the figure above.
This article is part of the 60th Anniversary special issue .
The fundamental reason that the genetics of behavior has remained so controversial for so long is that the layer of theory between data and their interpretation is thicker and more opaque than in more established areas of science. The finding that variations in tiny snippets of DNA have small but detectable relations to variation in behavior surprises no one, at least no one who was paying attention to the twin studies. How such snippets of DNA are related to differences in behavior—known as the gene‐to‐behavior pathway—is the great theoretical problem of modern behavioral genetics .
Given that intentional human breeding is a horrific prospect, what kind of technology might we want (or fear) out of human behavioral genetics? One possibility is a technology that could predict important behavioral characteristics of humans based on their genomes alone. A moment's thought suggests significant benefits and risks that might be associated with such a possibility, but for the moment, just consider how convincing it would be if on the day of a baby's birth we could make meaningful predictions about whether he or she would become a concert pianist or an alcoholic. This article will consider where we are right now as regards that possibility, using human height and intelligence as the primary examples .
There is a longstanding debate about genetics research into intelligence. Some scholars question the value of focusing on genetic contributions to intelligence in a society where social and environmental determinants powerfully influence cognitive ability and educational outcomes. Others warn that censoring certain research questions, such as inquiries about genetic differences in intellectual potential, compromises academic freedom. Still others view interest in this subject as a corollary to a long and troublesome history of eugenics research. The dawn of a new era in genome sequencing as a commodity will sustain scientific interest in the genetics of intelligence for the foreseeable future, but deep-rooted challenges threaten the scientific merit of the research. The use of imprecise definitions of study populations, the difficult nature of studying the environment, and the potential of researcher bias are inextricably linked with concerns about the trustworthiness and utility of research in this area. Leadership by the genetics community is essential to ensure the value and trustworthiness of these studies.
In the past, work on racial and ethnic variation in brain and behavior was marginalized within genetics. Against the backdrop of genetics’ eugenic legacy, wide consensus held such research to be both ethically problematic and methodologically controversial. But today it is finding new opportunistic venues in a global, transdisciplinary, data‐rich postgenomic research environment in which such a consensus is increasingly strained. The postgenomic sciences display worrisome deficits in their ability to govern and negotiate standards for making postgenomic claims in the transdisciplinary space between human population variation research, studies of intelligence, neuroscience, and evolutionary biology .
Today some researchers are pursuing the genomics of intelligence on a newly grand scale. They are sequencing large numbers of whole genomes of people considered highly intelligent (by varying empirical and social measures) in the hope of finding gene variants predictive of intelligence. Troubling and at times outlandish futurist claims accompany this research. Scientists involved in this research have openly discussed the possibility of marketing prenatal tests for intelligence, of genetic engineering or selective embryo implantation to increase the likelihood of a high‐IQ child, and of genotyping children to guide their education. In this permissive and contested environment, what would trustworthy research on the genomics of high intelligence look like ?