Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation

Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.

Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes

Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.

Testing for differences in polygenic scores in the presence of confounding

Polygenic scores have become an important tool in human genetics, enabling the prediction of individuals' phenotypes from their genotypes. Understanding how the pattern of differences in polygenic score predictions across individuals intersects with variation in ancestry can provide insights into the evolutionary forces acting on the trait in question, and is important for understanding health disparities. However, because most polygenic scores are computed using effect estimates from population samples, they are susceptible to confounding by both genetic and environmental effects that are correlated with ancestry. The extent to which this confounding drives patterns in the distribution of polygenic scores depends on patterns of population structure in both the original estimation panel and in the prediction/test panel. Here, we use theory from population and statistical genetics, together with simulations, to study the procedure of testing for an association between polygenic scores and axes of ancestry variation in the presence of confounding. We use a general model of genetic relatedness to describe how confounding in the estimation panel biases the distribution of polygenic scores in a way that depends on the degree of overlap in population structure between panels. We then show how this confounding can bias tests for associations between polygenic scores and important axes of ancestry variation in the test panel. Specifically, for any given test, there exists a single axis of population structure in the GWAS panel that needs to be controlled for in order to protect the test. Based on this result, we propose a new approach for directly estimating this axis of population structure in the GWAS panel. We then use simulations to compare the performance of this approach to the standard approach in which the principal components of the GWAS panel genotypes are used to control for stratification.

Predicting the direction of phenotypic difference

Predicting phenotypes from genomic data is a key goal in genetics, but for most complex phenotypes, predictions are hampered by incomplete genotype-to-phenotype mapping. Here, we describe a more attainable approach than quantitative predictions, which is aimed at qualitatively predicting phenotypic differences. Despite incomplete genotype-to-phenotype mapping, we show that it is relatively easy to determine which of two individuals has a greater phenotypic value. This question is central in many scenarios, e.g., comparing disease risk between individuals, the yield of crop strains, or the anatomy of extinct vs extant species. To evaluate prediction accuracy, i.e., the probability that the individual with the greater predicted phenotype indeed has a greater phenotypic value, we developed an estimator of the ratio between known and unknown effects on the phenotype. We evaluated prediction accuracy using human data from tens of thousands of individuals from either the same family or the same population, as well as data from different species. We found that, in many cases, even when only a small fraction of the loci affecting a phenotype is known, the individual with the greater phenotypic value can be identified with over 90% accuracy. Our approach also circumvents some of the limitations in transferring genetic association results across populations. Overall, we introduce an approach that enables accurate predictions of key information on phenotypes - the direction of phenotypic difference - and suggest that more phenotypic information can be extracted from genomic data than previously appreciated.

Heritability within groups is uninformative about differences among groups: Cases from behavioral, evolutionary, and statistical genetics

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give-directly or indirectly-some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the "between-group heritability" sometimes invoked in behavior genetics, the statistic [Formula: see text] used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

Heritability within groups is uninformative about differences among groups: cases from behavioral, evolutionary, and statistical genetics

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give-directly or indirectly-some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the "between-group heritability" sometimes invoked in behavior genetics, the statistic P S T used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

Uncovering the architecture of selection in two Bos taurus cattle breeds

Directional selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animal's generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (<10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed-specific selection objectives, such as fertility and maternal ability in Red Angus, and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (<50 kb) annotated genes. Using haplotype-based and composite selective sweep statistics, we identify hundreds of putative selective sweeps that likely occurred earlier in the evolution of these breeds; however, these sweeps have little overlap with recent polygenic selection. This makes GPSM a complementary approach to sweep detection methods when temporal genotype data are available. The selected loci that we identify across methods demonstrate the complex architecture of selection in domesticated cattle.

Spousal Concordance of Hypertension Among Middle-Aged and Older Heterosexual Couples Around the World: Evidence From Studies of Aging in the United States, England, China, and India

BACKGROUND

Health concordance within couples presents a promising opportunity to design interventions for disease management, including hypertension. We compared the concordance of prevalent hypertension within middle-aged and older heterosexual couples in the United States, England, China, and India.

METHODS AND RESULTS

Cross-sectional dyadic data on heterosexual couples were used from contemporaneous waves of the HRS (US Health and Retirement Study, 2016/17, n=3989 couples), ELSA (English Longitudinal Study on Aging, 2016/17, n=1086), CHARLS (China Health and Retirement Longitudinal Study, 2015/16, n=6514), and LASI (Longitudinal Aging Study in India, 2017/19, n=22 389). Concordant hypertension was defined as both husband and wife in a couple having hypertension. The prevalence of concordant hypertension within couples was 37.9% (95% CI, 35.8-40.0) in the United States, 47.1% (95% CI, 43.2-50.9) in England, 20.8% (95% CI, 19.6-21.9) in China, and 19.8% (95% CI, 19.0-20.5) in India. Compared with wives married to husbands without hypertension, wives married to husbands with hypertension were more likely to have hypertension in the United States (prevalence ratio, 1.09 [95% CI, 1.01- 1.17), England (prevalence ratio, 1.09, 95% CI, 0.98-1.21), China (prevalence ratio, 1.26 [95% CI, 1.17-1.35), and India (prevalence ratio, 1.19 [95% CI, 1.15-1.24]). Within each country, similar associations were observed for husbands. Across countries, associations in the United States and England were similar, whereas they were slightly larger in China and India.

CONCLUSIONS

Concordance of hypertension within heterosexual couples was consistently observed across these 4 socially and economically diverse countries. Couple-centered interventions may be an efficient strategy to prevent and manage hypertension in these countries.

Getting it right: Teaching undergraduate biology to undermine racial essentialism

How we teach human genetics matters for social equity. The biology curriculum appears to be a crucial locus of intervention for either reinforcing or undermining students' racial essentialist views. The Mendelian genetic models dominating textbooks, particularly in combination with racially inflected language sometimes used when teaching about monogenic disorders, can increase middle and high school students' racial essentialism and opposition to policies to increase equity. These findings are of particular concern given the increasing spread of racist misinformation online and the misappropriation of human genomics research by white supremacists, who take advantage of low levels of genetics literacy in the general public. Encouragingly, however, teaching updated information about the geographical distribution of human genetic variation and the complex, multifactorial basis of most human traits, reduces students' endorsement of racial essentialism. The genetics curriculum is therefore a key tool in combating misinformation and scientific racism. Here, we describe a framework and example teaching materials for teaching students key concepts in genetics, human evolutionary history, and human phenotypic variation at the undergraduate level. This framework can be flexibly applied in biology and anthropology classes and adjusted based on time availability. Our goal is to provide undergraduate-level instructors with varying levels of expertise with a set of evidence-informed tools for teaching human genetics to combat scientific racism, including an evolving set of instructional resources, as well as learning goals and pedagogical approaches. Resources can be found at https://noto.li/YIlhZ5. Additionally, we hope to generate conversation about integrating modern genetics into the undergraduate curriculum, in light of recent findings about the risks and opportunities associated with teaching genetics.

'Dysgenic fertility' is an ideological, not a scientific, concept. A Comment on: 'Stability and change in male fertility patterns by cognitive ability across 32 birth cohorts' (2023), by Bratsberg & Rogeberg

Recently Bratsberg & Rogeberg (2023) presented an analysis in Biology Letters of how cognitive ability is associated with fertility in Norwegian men. Our concern relates to the theoretical framework of this paper. The analysis is framed around the concept of 'dysgenic fertility', which is treated throughout as a scientific theory, but 'dysgenic fertility' is not science, it is an ideological concept.

Ethical, legal, and social implications of genetic risk prediction for multifactorial disease: a narrative review identifying concerns about interpretation and use of polygenic scores

Advances in genomics have enabled the development of polygenic scores (PGS), sometimes called polygenic risk scores, in the context of multifactorial diseases and disorders such as cancer, cardiovascular disease, and schizophrenia. PGS estimate an individual's genetic predisposition, as compared to other members of a population, for conditions which are influenced by both genetic and environmental factors. There is significant interest in using genetic risk prediction afforded through PGS in public health, clinical care, and research settings, yet many acknowledge the need to thoughtfully consider and address ethical, legal, and social implications (ELSI). To contribute to this effort, this paper reports on a narrative review of the literature, with the aim of identifying and categorizing ELSI relating to genetic risk prediction in the context of multifactorial disease, which have been raised by scholars in the field. Ninety-two articles, spanning from 1977 to 2021, met the inclusion criteria for this study. Identified ELSI included potential benefits, challenges and risks that focused on concerns about interpretation and use, and ethical obligations to maximize benefits, minimize risks, promote justice, and support autonomy. This research will support geneticists, clinicians, genetic counselors, patients, patient advocates, and policymakers in recognizing and addressing ethical concerns associated with PGS; it will also guide future empirical and normative research.

HLA-G 3'UTR haplotype analyses in HCV infection and HCV-derived cirrhosis, hepatocellular carcinoma and fibrosis

Hepatitis C virus (HCV) infection is a major cause of chronic liver disease. Chronic HCV infection is also an important cause of hepatic fibrosis, cirrhosis and hepatocellular carcinoma (HCC). HCV has the capacity to evade immune surveillance by altering the host immune response. Moreover, variations in immune-related genes can lead to differential susceptibility to HCV infection as well as interfere on the susceptibility to the development of hepatic fibrosis, cirrhosis and HCC. The human leucocyte antigen G (HLA-G) gene codes for an immunomodulatory protein known to be expressed in the maternal-foetal interface and in immune-privileged tissues. The HLA-G 3' untranslated region (3'UTR) is important for mRNA stability, and variants in this region are known to impact gene expression. Studies, mainly focusing in a 14 bp insertion/deletion polymorphism, have correlated HLA-G 3'UTR with susceptibility to viral infections, but other polymorphic variants in the HLA-G 3'UTR might also affect HCV infection as they are inherited as haplotypes. The present study evaluated HLA-G 3'UTR polymorphisms and performed linkage disequilibrium test and haplotype assembly in 286 HCV infected patients who have developed fibrosis, cirrhosis or HCC, as well as in 129 healthy control subjects. Haplotypes UTR-1, UTR-2 and UTR-3 were the most observed in HCV+ patients, in the frequencies of 0.276, 0.255 and 0.121, respectively. No statistically significant difference was observed between HCV+ and control subjects, even when patients were grouped according to outcome (HCC, cirrhosis or fibrosis). Despite that, some trends in the results were observed, and therefore, we cannot rule out the possibility that variants associated to high HLA-G expression can be involved in HCV infection susceptibility.

Decreased accuracy of forensic DNA mixture analysis for groups with lower genetic diversity

Forensic investigation of DNA samples from multiple contributors has become commonplace. These complex analyses use statistical frameworks accounting for multiple levels of uncertainty in allelic contributions from different individuals, particularly for samples containing few molecules of DNA. These methods have been thoroughly tested along some axes of variation, but less attention has been paid to accuracy across human genetic variation. Here, we quantify the accuracy of DNA mixture analysis over 244 human groups. We find higher false inclusion rates for mixtures with more contributors, and for groups with lower genetic diversity. Even for two-contributor mixtures where one contributor is known and the reference group is correctly specified, false inclusion rates are 1e-5 or higher for 56 out of 244 groups. This means that, depending on multiple testing, some false inclusions may be expected. These false positives could be lessened with more selective and conservative use of DNA mixture analysis.

Genome-wide meta-analysis identifies new candidate genes for sickle cell disease nephropathy

Sickle cell disease nephropathy (SCDN), a common SCD complication, is strongly associated with mortality. Polygenic risk scores calculated from recent transethnic meta-analyses of urinary albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR) trended toward association with proteinuria and eGFR in SCD but the model fit was poor (R2 < 0.01), suggesting that there are likely unique genetic risk factors for SCDN. Therefore, we performed genome-wide association studies (GWAS) for 2 critical manifestations of SCDN, proteinuria and decreased eGFR, in 2 well-characterized adult SCD cohorts, representing, to the best of our knowledge, the largest SCDN sample to date. Meta-analysis identified 6 genome-wide significant associations (false discovery rate, q ≤ 0.05): 3 for proteinuria (CRYL1, VWF, and ADAMTS7) and 3 for eGFR (LRP1B, linc02288, and FPGT-TNNI3K/TNNI3K). These associations are independent of APOL1 risk and represent novel SCDN loci, many with evidence for regulatory function. Moreover, GWAS SNPs in CRYL1, VWF, ADAMTS7, and linc02288 are associated with gene expression in kidney and pathways important to both renal function and SCD biology, supporting the hypothesis that SCDN pathophysiology is distinct from other forms of kidney disease. Together, these findings provide new targets for functional follow-up that could be tested prospectively and potentially used to identify patients with SCD who are at risk, before onset of kidney dysfunction.

Polygenic scores for social science: Clarification, consensus, and controversy

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.

Genetic polymorphisms in the serotonin, dopamine and opioid pathways influence social attention in rhesus macaques (Macaca mulatta)

Behaviour has a significant heritable component; however, unpicking the variants of interest in the neural circuits and molecular pathways that underpin these has proven difficult. Here, we present a comprehensive analysis of the relationship between known and new candidate genes from identified pathways and key behaviours for survival in 109 adult rhesus macaques (Macaca mulatta). Eight genes involved in emotion were analysed for variation at a total of nine loci. Genetic data were then correlated with cognitive and observational measures of behaviour associated with wellbeing and survival using MCMC-based Bayesian GLMM in R, to account for relatedness within the macaque population. For four loci the variants genotyped were length polymorphisms (SLC6A4 5-hydroxytryptamine transporter length-polymorphic repeat (5-HTTLPR), SLC6A4 STin polymorphism, Tryptophan 5-hydroxylase 2 (TPH2) and Monoamine oxidase A (MAOA)) whilst for the other five (5-hydroxytryptamine receptor 2A (HTR2A), Dopamine Receptor D4 (DRD4), Oxytocin receptor (OXTR), Arginine vasopressin receptor 1A (AVPR1a), Opioid receptor mu(μ) 1 (OPRM1)) SNPs were analysed. STin genotype, DRD4 haplotype and OXTR haplotype were significantly associated with the cognitive and observational measures of behaviour associated with wellbeing and survival. Genotype for 5-HTTLPR, STin and AVPR1a, and haplotype for HTR2A, DRD4 and OXTR were significantly associated with the duration of behaviours including fear and anxiety. Understanding the biological underpinnings of individual variation in negative emotion (e.g., fear and anxiety), together with their impact on social behaviour (e.g., social attention including vigilance for threat) has application for managing primate populations in the wild and captivity, as well as potential translational application for understanding of the genetic basis of emotions in humans.

Sex differences in the polygenic architecture of hearing problems in adults

BACKGROUND

Hearing problems (HP) in adults are common and are associated with several comorbid conditions. Its prevalence increases with age, reflecting the cumulative effect of environmental factors and genetic predisposition. Although several risk loci have been already identified, HP biology and epidemiology are still insufficiently investigated by large-scale genetic studies.

METHODS

Leveraging the UK Biobank, the Nurses' Health Studies (I and II), the Health Professionals Follow-up Study, and the Million Veteran Program, we conducted a comprehensive genome-wide investigation of HP in 748,668 adult participants (discovery N = 501,825; replication N = 226,043; cross-ancestry replication N = 20,800). We leveraged the GWAS findings to characterize HP polygenic architecture, exploring sex differences, polygenic risk across ancestries, tissue-specific transcriptomic regulation, cause-effect relationships with genetically correlated traits, and gene interactions with HP environmental risk factors.

RESULTS

We identified 54 risk loci and demonstrated that HP polygenic risk is shared across ancestry groups. Our transcriptomic regulation analysis highlighted the potential role of the central nervous system in HP pathogenesis. The sex-stratified analyses showed several additional associations related to peripheral hormonally regulated tissues reflecting a potential role of estrogen in hearing function. This evidence was supported by the multivariate interaction analysis that showed how genes involved in brain development interact with sex, noise pollution, and tobacco smoking in relation to their HP associations. Additionally, the genetically informed causal inference analysis showed that HP is linked to many physical and mental health outcomes.

CONCLUSIONS

The results provide many novel insights into the biology and epidemiology of HP in adults. Our sex-specific analyses and transcriptomic associations highlighted molecular pathways that may be targeted for drug development or repurposing. Additionally, the potential causal relationships identified may support novel preventive screening programs to identify individuals at risk.

Embedded racism: Inequitable niche construction as a neglected evolutionary process affecting health

Racial health disparities are a pervasive feature of modern experience and structural racism is increasingly recognized as a public health crisis. Yet evolutionary medicine has not adequately addressed the racialization of health and disease, particularly the systematic embedding of social biases in biological processes leading to disparate health outcomes delineated by socially defined race. In contrast to the sheer dominance of medical publications which still assume genetic 'race' and omit mention of its social construction, we present an alternative biological framework of racialized health. We explore the unifying evolutionary-ecological principle of niche construction as it offers critical insights on internal and external biological and behavioral feedback processes environments at every level of the organization. We Integrate insights of niche construction theory in the context of human evolutionary and social history and phenotype-genotype modification, exposing the extent to which racism is an evolutionary mismatch underlying inequitable disparities in disease. We then apply ecological models of niche exclusion and exploitation to institutional and interpersonal racial constructions of population and individual health and demonstrate how discriminatory processes of health and harm apply to evolutionarily relevant disease classes and life-history processes in which socially defined race is poorly understood and evaluated. Ultimately, we call for evolutionary and biomedical scholars to recognize the salience of racism as a pathogenic process biasing health outcomes studied across disciplines and to redress the neglect of focus on research and application related to this crucial issue.

Addressing the challenges of polygenic scores in human genetic research

The genotyping of millions of human samples has made it possible to evaluate variants across the human genome for their possible association with risks for numerous diseases and other traits by using genome-wide association studies (GWASs). The associations between phenotype and genotype found in GWASs make possible the construction of polygenic scores (PGSs), which aim to predict a trait or disease outcome in an individual on the basis of their genotype (in the disease case, the term polygenic risk score [PRS] is often used). PGSs have shown promise for studying the biology of complex traits and as a tool for evaluating individual disease risks in clinical settings. Although the quantity and quality of data to compute PGSs are increasing, challenges remain in the technical aspects of developing PGSs and in the ethical and social issues that might arise from their use. This ASHG Guidance emphasizes three major themes for researchers working with or interested in the application of PGSs in their own research: (1) developing diverse research cohorts; (2) fostering robustness in the development, application, and interpretation of PGSs; and (3) improving the communication of PGS results and their implications to broad audiences.

Heritability is a poor, if not unhelpful, measure of complex human behavioral processes

Heritability is not a measure of the relative contribution of nature vis-à-vis nurture, nor is it the phenotypic variance explained by or because of genetic variance. Heritability is a correlative value. The evolutionary and developmental processes associated with human culture challenge the use of "heritability" for understanding human behavior.

1,000 ancient genomes uncover 10,000 years of natural selection in Europe

Ancient DNA has revolutionized our understanding of human population history. However, its potential to examine how rapid cultural evolution to new lifestyles may have driven biological adaptation has not been met, largely due to limited sample sizes. We assembled genome-wide data from 1,291 individuals from Europe over 10,000 years, providing a dataset that is large enough to resolve the timing of selection into the Neolithic, Bronze Age, and Historical periods. We identified 25 genetic loci with rapid changes in frequency during these periods, a majority of which were previously undetected. Signals specific to the Neolithic transition are associated with body weight, diet, and lipid metabolism-related phenotypes. They also include immune phenotypes, most notably a locus that confers immunity to Salmonella infection at a time when ancient Salmonella genomes have been shown to adapt to human hosts, thus providing a possible example of human-pathogen co-evolution. In the Bronze Age, selection signals are enriched near genes involved in pigmentation and immune-related traits, including at a key human protein interactor of SARS-CoV-2. Only in the Historical period do the selection candidates we detect largely mirror previously-reported signals, highlighting how the statistical power of previous studies was limited to the last few millennia. The Historical period also has multiple signals associated with vitamin D binding, providing evidence that lactase persistence may have been part of an oligogenic adaptation for efficient calcium uptake and challenging the theory that its adaptive value lies only in facilitating caloric supplementation during times of scarcity. Finally, we detect selection on complex traits in all three periods, including selection favoring variants that reduce body weight in the Neolithic. In the Historical period, we detect selection favoring variants that increase risk for cardiovascular disease plausibly reflecting selection for a more active inflammatory response that would have been adaptive in the face of increased infectious disease exposure. Our results provide an evolutionary rationale for the high prevalence of these deadly diseases in modern societies today and highlight the unique power of ancient DNA in elucidating biological change that accompanied the profound cultural transformations of recent human history.

Population differentiation of polygenic score predictions under stabilizing selection

Given the many small-effect loci uncovered by genome-wide association studies (GWAS), polygenic scores have become central to genomic medicine, and have found application in diverse settings including evolutionary studies of adaptation. Despite their promise, polygenic scores have been found to suffer from limited portability across human populations. This at first seems in conflict with the observation that most common genetic variation is shared among populations. We investigate one potential cause of this discrepancy: stabilizing selection on complex traits. Counterintuitively, while stabilizing selection constrains phenotypic evolution, it accelerates the loss and fixation of alleles underlying trait variation within populations (GWAS loci). Thus even when populations share an optimum phenotype, stabilizing selection erodes the variance contributed by their shared GWAS loci, such that predictions from GWAS in one population explain less of the phenotypic variation in another. We develop theory to quantify how stabilizing selection is expected to reduce the prediction accuracy of polygenic scores in populations not represented in GWAS samples. In addition, we find that polygenic scores can substantially overstate average genetic differences of phenotypes among populations. We emphasize stabilizing selection around a common optimum as a useful null model to connect patterns of allele frequency and polygenic score differentiation. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

Polygenic risk, population structure and ongoing difficulties with race in human genetics

‘The Apportionment of Human Diversity’ stands as a noteworthy intervention, both for the field of human population genetics as well as in the annals of public communication of science. Despite the widespread uptake of Lewontin's conclusion that racial classification is of ‘virtually no genetic or taxonomic significance’, the biomedical research community continues to grapple with whether and how best to account for race in its work. Nowhere is this struggle more apparent than in the latest attempts to translate genetic associations with complex disease risk to clinical use in the form of polygenic risk scores, or PRS. In this perspective piece, we trace current challenges surrounding the appropriate development and clinical application of PRS in diverse patient cohorts to ongoing difficulties deciding which facets of population structure matter, and for what reasons, to human health. Despite numerous analytical innovations, there are reasons that emerge from Lewontin's work to remain sceptical that accounting for population structure in the context of polygenic risk estimation will allow us to more effectively identify and intervene on the significant health disparities which plague marginalized populations around the world.

This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.

Half a century later and we're back where we started: How the problem of locality turned in to the problem of portability

In the 1970s, Lewontin sparked a debate about a problem of locality, by making the case that any given heritability estimate is local to the original population and environment studied, and could not be generalized to other populations and environments. Nearly 50 years later, a new problem of portability has emerged: the predictive accuracy of polygenic scores diminishes when applied to populations whose characteristics are different from the original population sample. This paper briefly reviews the nature of each problem and analyzes their similarities and differences in three areas: 1) conceptual underpinnings, 2) causal explanations, and 3) practical, social, and political implications. Although conceptually and methodologically different from the problem of locality in important respects, the problem of portability facing contemporary genomics today should come as no surprise, as it is an inevitable outcome of the kinds of problematic inferences detailed by Lewontin nearly half a century ago.

Ending genetic essentialism through genetics education

Genetic concepts are regularly used in arguments about racial inequality. This review summarizes research about the relationship between genetics education and a particular form of racial prejudice known as genetic essentialism. Genetic essentialism is a cognitive form of prejudice that is used to rationalize inequality. Studies suggest that belief in genetic essentialism among genetics students can be increased or decreased based on what students learn about human genetics and why they learn it. Research suggests that genetics education does little to prevent the development of genetic essentialism, and it may even exacerbate belief in it. However, some forms of genetics education can avert this problem. In particular, if instructors teach genetics to help students understand the flaws in genetic essentialist arguments, then it is possible to reduce belief in genetic essentialism among biology students. This review outlines our knowledge about how to accomplish this goal and the research that needs to be done to end genetic essentialism through genetics education.

Revisiting the malaria hypothesis: accounting for polygenicity and pleiotropy

The malaria hypothesis predicts local, balancing selection of deleterious alleles that confer strong protection from malaria. Three protective variants, recently discovered in red cell genes, are indeed more common in African than European populations. Still, up to 89% of the heritability of severe malaria is attributed to many genome-wide loci with individually small effects. Recent analyses of hundreds of genome-wide association studies (GWAS) in humans suggest that most functional, polygenic variation is pleiotropic for multiple traits. Interestingly, GWAS alleles and red cell traits associated with small reductions in malaria risk are not enriched in African populations. We propose that other selective and neutral forces, in addition to malaria prevalence, explain the global distribution of most genetic variation impacting malaria risk.

Genotype imputation and polygenic score estimation in northwestern Russian population

Numerous studies demonstrated the lack of transferability of polygenic score (PGS) models across populations and the problem arising from unequal presentation of ancestries across genetic studies. However, even within European ancestry there are ethnic groups that are rarely presented in genetic studies. For instance, Russians, being one of the largest, diverse, and yet understudied group in Europe. In this study, we evaluated the reliability of genotype imputation for the Russian cohort by testing several commonly used imputation reference panels (e.g. HRC, 1000G, HGDP). HRC, in comparison with two other panels, showed the most accurate results based on both imputation accuracy and allele frequency concordance between masked and imputed genotypes. We built polygenic score models based on GWAS results from the UK biobank, measured the explained phenotypic variance in the Russian cohort attributed to polygenic scores for 11 phenotypes, collected in the clinic for each participant, and finally explored the role of allele frequency discordance between the UK biobank and the study cohort in the resulting PGS performance.

Populations, Traits, and Their Spatial Structure in Humans

The spatial distribution of genetic variants is jointly determined by geography, past demographic processes, natural selection, and its interplay with environmental variation. A fraction of these genetic variants are "causal alleles" that affect the manifestation of a complex trait. The effect exerted by these causal alleles on complex traits can be independent or dependent on the environment. Understanding the evolutionary processes that shape the spatial structure of causal alleles is key to comprehend the spatial distribution of complex traits. Natural selection, past population size changes, range expansions, consanguinity, assortative mating, archaic introgression, admixture, and the environment can alter the frequencies, effect sizes, and heterozygosities of causal alleles. This provides a genetic axis along which complex traits can vary. However, complex traits also vary along biogeographical and sociocultural axes which are often correlated with genetic axes in complex ways. The purpose of this review is to consider these genetic and environmental axes in concert and examine the ways they can help us decipher the variation in complex traits that is visible in humans today. This initiative necessarily implies a discussion of populations, traits, the ability to infer and interpret "genetic" components of complex traits, and how these have been impacted by adaptive events. In this review, we provide a history-aware discussion on these topics using both the recent and more distant past of our academic discipline and its relevant contexts.

On cross-ancestry cancer polygenic risk scores

Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.

The translation of results from genome-wide association studies (GWAS) into polygenic risk scores (PRS) to predict disease risk or outcomes is a major aspiration in the field of statistical genetics. While there has been significant progress in this area for many complex diseases, the lack of diversity in GWAS is transferred to PRS research. Discovery of genetic risk factors, especially for cancer traits, are almost exclusively based on individuals with European-ancestry, and it remains unclear if these results can be utilized for PRS applications across non-European ancestries.

Here, we used external European-ancestry based GWAS results to construct breast and prostate cancer PRS and showcase their utility as predictors across African, East Asian, European, and South Asian ancestry groups using data from the UK Biobank. We observed ancestry-specific PRS distributions, that when scaled within each group, could identify individuals at higher risk of prostate and breast cancer in each group. Our study highlights an opportunity to use results from large European GWAS for the construction of PRS in diverse ancestry groups. To realize the full potential of PRS in early detection and prevention of cancer across ethnic groups, we need rapid expanded recruitment of diverse participants in the field of GWAS.

Polygenic risk scores predict diabetes complications and their response to intensive blood pressure and glucose control

AIMS/HYPOTHESIS

Type 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction could lead to timely intervention and better outcomes. Genetic information can be used to enable early detection of risk.

METHODS

We developed a multi-polygenic risk score (multiPRS) that combines ten weighted PRSs (10 wPRS) composed of 598 SNPs associated with main risk factors and outcomes of type 2 diabetes, derived from summary statistics data of genome-wide association studies. The 10 wPRS, first principal component of ethnicity, sex, age at onset and diabetes duration were included into one logistic regression model to predict micro- and macrovascular outcomes in 4098 participants in the ADVANCE study and 17,604 individuals with type 2 diabetes in the UK Biobank study.

RESULTS

The model showed a similar predictive performance for cardiovascular and renal complications in different cohorts. It identified the top 30% of ADVANCE participants with a mean of 3.1-fold increased risk of major micro- and macrovascular events (p = 6.3 × 10^-21 and p = 9.6 × 10^-31, respectively) and a 4.4-fold (p = 6.8 × 10^-33) higher risk of cardiovascular death. While in ADVANCE overall, combined intensive blood pressure and glucose control decreased cardiovascular death by 24%, the model identified a high-risk group in whom it decreased the mortality rate by 47%, and a low-risk group in whom it had no discernible effect. High-risk individuals had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years.

CONCLUSIONS/INTERPRETATION

This novel multiPRS model stratified individuals with type 2 diabetes according to risk of complications and helped to target earlier those who would receive greater benefit from intensive therapy.

Quantitative Human Paleogenetics: What can Ancient DNA Tell us About Complex Trait Evolution?

Genetic association data from national biobanks and large-scale association studies have provided new prospects for understanding the genetic evolution of complex traits and diseases in humans. In turn, genomes from ancient human archaeological remains are now easier than ever to obtain, and provide a direct window into changes in frequencies of trait-associated alleles in the past. This has generated a new wave of studies aiming to analyse the genetic component of traits in historic and prehistoric times using ancient DNA, and to determine whether any such traits were subject to natural selection. In humans, however, issues about the portability and robustness of complex trait inference across different populations are particularly concerning when predictions are extended to individuals that died thousands of years ago, and for which little, if any, phenotypic validation is possible. In this review, we discuss the advantages of incorporating ancient genomes into studies of trait-associated variants, the need for models that can better accommodate ancient genomes into quantitative genetic frameworks, and the existing limits to inferences about complex trait evolution, particularly with respect to past populations.

A polygenic score for acute vaso-occlusive pain in pediatric sickle cell disease

Individuals with monogenic disorders can experience variable phenotypes that are influenced by genetic variation. To investigate this in sickle cell disease (SCD), we performed whole-genome sequencing (WGS) of 722 individuals with hemoglobin HbSS or HbSβ0-thalassemia from Baylor College of Medicine and from the St. Jude Children's Research Hospital Sickle Cell Clinical Research and Intervention Program (SCCRIP) longitudinal cohort study. We developed pipelines to identify genetic variants that modulate sickle hemoglobin polymerization in red blood cells and combined these with pain-associated variants to build a polygenic score (PGS) for acute vaso-occlusive pain (VOP). Overall, we interrogated the α-thalassemia deletion -α3.7 and 133 candidate single-nucleotide polymorphisms (SNPs) across 66 genes for associations with VOP in 327 SCCRIP participants followed longitudinally over 6 years. Twenty-one SNPs in 9 loci were associated with VOP, including 3 (BCL11A, MYB, and the β-like globin gene cluster) that regulate erythrocyte fetal hemoglobin (HbF) levels and 6 (COMT, TBC1D1, KCNJ6, FAAH, NR3C1, and IL1A) that were associated previously with various pain syndromes. An unweighted PGS integrating all 21 SNPs was associated with the VOP event rate (estimate, 0.35; standard error, 0.04; P = 5.9 × 10-14) and VOP event occurrence (estimate, 0.42; standard error, 0.06; P = 4.1 × 10-13). These associations were stronger than those of any single locus. Our findings provide insights into the genetic modulation of VOP in children with SCD. More generally, we demonstrate the utility of WGS for investigating genetic contributions to the variable expression of SCD-associated morbidities.

Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

Functional biology in its natural context: A search for emergent simplicity

The immeasurable complexity at every level of biological organization creates a daunting task for understanding biological function. Here, we highlight the risks of stripping it away at the outset and discuss a possible path toward arriving at emergent simplicity of understanding while still embracing the ever-changing complexity of biotic interactions that we see in nature.

Admixed Populations Improve Power for Variant Discovery and Portability in Genome-Wide Association Studies

Genome-wide association studies (GWAS) are primarily conducted in single-ancestry settings. The low transferability of results has limited our understanding of human genetic architecture across a range of complex traits. In contrast to homogeneous populations, admixed populations provide an opportunity to capture genetic architecture contributed from multiple source populations and thus improve statistical power. Here, we provide a mechanistic simulation framework to investigate the statistical power and transferability of GWAS under directional polygenic selection or varying divergence. We focus on a two-way admixed population and show that GWAS in admixed populations can be enriched for power in discovery by up to 2-fold compared to the ancestral populations under similar sample size. Moreover, higher accuracy of cross-population polygenic score estimates is also observed if variants and weights are trained in the admixed group rather than in the ancestral groups. Common variant associations are also more likely to replicate if first discovered in the admixed group and then transferred to an ancestral population, than the other way around (across 50 iterations with 1,000 causal SNPs, training on 10,000 individuals, testing on 1,000 in each population, p = 3.78e-6, 6.19e-101, ∼0 for F_ST = 0.2, 0.5, 0.8, respectively). While some of these F_ST values may appear extreme, we demonstrate that they are found across the entire phenome in the GWAS catalog. This framework demonstrates that investigation of admixed populations harbors significant advantages over GWAS in single-ancestry cohorts for uncovering the genetic architecture of traits and will improve downstream applications such as personalized medicine across diverse populations.

Cultural Evolution of Genetic Heritability

Behavioral genetics and cultural evolution have both revolutionized our understanding of human behavior-largely independent of each other. Here we reconcile these two fields under a dual inheritance framework, offering a more nuanced understanding of the interaction between genes and culture. Going beyond typical analyses of gene-environment interactions, we describe the cultural dynamics that shape these interactions by shaping the environment and population structure. A cultural evolutionary approach can explain, for example, how factors such as rates of innovation and diffusion, density of cultural sub-groups, and tolerance for behavioral diversity impact heritability estimates, thus yielding predictions for different social contexts. Moreover, when cumulative culture functionally overlaps with genes, genetic effects become masked, unmasked, or even reversed, and the causal effects of an identified gene become confounded with features of the cultural environment. The manner of confounding is specific to a particular society at a particular time, but a WEIRD (Western, educated, industrialized, rich, democratic) sampling problem obscures this boundedness. Cultural evolutionary dynamics are typically missing from models of gene-to-phenotype causality, hindering generalizability of genetic effects across societies and across time. We lay out a reconciled framework and use it to predict the ways in which heritability should differ between societies, between socioeconomic levels and other groupings within some societies but not others, and over the life course. An integrated cultural evolutionary behavioral genetic approach cuts through the nature-nurture debate and helps resolve controversies in topics such as IQ.

Polygenic Adaptation: Integrating Population Genetics and Gene Regulatory Networks

The adaptation of populations to local environments often relies on the selection of optimal values for polygenic traits. Here, we first summarize the results obtained from different quantitative genetics and population genetics models, about the genetic architecture of polygenic traits and their response to directional selection. We then highlight the contribution of systems biology to the understanding of the molecular bases of polygenic traits and the evolution of gene regulatory networks involved in these traits. Finally, we discuss the need for a unifying framework merging the fields of population genetics, quantitative genetics and systems biology to better understand the molecular bases of polygenic traits adaptation.

The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation

We present the Polygenic Score (PGS) Catalog (https://www.PGSCatalog.org ), an open resource of published scores (including variants, alleles and weights) and consistently curated metadata required for reproducibility and independent applications. The PGS Catalog has capabilities for user deposition, expert curation and programmatic access, thus providing the community with a platform for PGS dissemination, research and translation.

Negative selection on complex traits limits phenotype prediction accuracy between populations

Phenotype prediction is a key goal for medical genetics. Unfortunately, most genome-wide association studies are done in European populations, which reduces the accuracy of predictions via polygenic scores in non-European populations. Here, we use population genetic models to show that human demographic history and negative selection on complex traits can result in population-specific genetic architectures. For traits where alleles with the largest effect on the trait are under the strongest negative selection, approximately half of the heritability can be accounted for by variants in Europe that are absent from Africa, leading to poor performance in phenotype prediction across these populations. Further, under such a model, individuals in the tails of the genetic risk distribution may not be identified via polygenic scores generated in another population. We empirically test these predictions by building a model to stratify heritability between European-specific and shared variants and applied it to 37 traits and diseases in the UK Biobank. Across these phenotypes, ∼30% of the heritability comes from European-specific variants. We conclude that genetic association studies need to include more diverse populations to enable the utility of phenotype prediction in all populations.

Cumulative Genetic Risk and APOE ε4 Are Independently Associated With Dementia Status in a Multiethnic, Population-Based Cohort

Objective

Alzheimer disease (AD) is a common and costly neurodegenerative disorder. A large proportion of AD risk is heritable, and many genetic risk factors have been identified. The objective of this study was to test the hypothesis that cumulative genetic risk of known AD markers contributed to odds of dementia in a population-based sample.

Methods

In the US population-based Health and Retirement Study (waves 1995–2014), we evaluated the role of cumulative genetic risk of AD, with and without the APOE ε4 alleles, on dementia status (dementia, cognitive impairment without dementia, borderline cognitive impairment without dementia, and cognitively normal). We used logistic regression, accounting for demographic covariates and genetic principal components, and analyses were stratified by European and African genetic ancestry.

Results

In the European ancestry sample (n = 8,399), both AD polygenic score excluding the APOE genetic region (odds ratio [OR] = 1.10; 95% confidence interval [CI]: 1.00–1.20) and the presence of any APOE ε4 alleles (OR = 2.42; 95% CI: 1.99–2.95) were associated with the odds of dementia relative to normal cognition in a mutually adjusted model. In the African ancestry sample (n = 1,605), the presence of any APOE ε4 alleles was associated with 1.77 (95% CI: 1.20–2.61) times higher odds of dementia, whereas the AD polygenic score excluding the APOE genetic region was not significantly associated with the odds of dementia relative to normal cognition 1.06 (95% CI: 0.97–1.30).

Conclusions

Cumulative genetic risk of AD and APOE ε4 are both independent predictors of dementia in European ancestry. This study provides important insight into the polygenic nature of dementia and demonstrates the utility of polygenic scores in dementia research.

Complicated legacies: The human genome at 20

Millions of people today have access to their personal genomic information. Direct-to-consumer services and integration with other “big data” increasingly commoditize what was rightly celebrated as a singular achievement in February 2001 when the first draft human genomes were published. But such remarkable technical and scientific progress has not been without its share of missteps and growing pains. Science invited the experts below to help explore how we got here and where we should (or ought not) be going.

No support for the hereditarian hypothesis of the Black-White achievement gap using polygenic scores and tests for divergent selection

OBJECTIVES

Debate about the cause of IQ score gaps between Black and White populations has persisted within genetics, anthropology, and psychology. Recently, authors claimed polygenic scores provide evidence that a significant portion of differences in cognitive performance between Black and White populations are caused by genetic differences due to natural selection, the "hereditarian hypothesis." This study aims to show conceptual and methodological flaws of past studies supporting the hereditarian hypothesis.

MATERIALS AND METHODS

Polygenic scores for educational attainment were constructed for African and European samples of the 1000 Genomes Project. Evidence for selection was evaluated using an excess variance test. Education associated variants were further evaluated for signals of selection by testing for excess genetic differentiation (F_st ). Expected mean difference in IQ for populations was calculated under a neutral evolutionary scenario and contrasted to hereditarian claims.

RESULTS

Tests for selection using polygenic scores failed to find evidence of natural selection when the less biased within-family GWAS effect sizes were used. Tests for selection using F_st values did not find evidence of natural selection. Expected mean difference in IQ was substantially smaller than postulated by hereditarians, even under unrealistic assumptions that overestimate genetic contribution.

CONCLUSION

Given these results, hereditarian claims are not supported in the least. Cognitive performance does not appear to have been under diversifying selection in Europeans and Africans. In the absence of diversifying selection, the best case estimate for genetic contributions to group differences in cognitive performance is substantially smaller than hereditarians claim and is consistent with genetic differences contributing little to the Black-White gap.

The evolution of group differences in changing environments

The selection pressures that have shaped the evolution of complex traits in humans remain largely unknown, and in some contexts highly contentious, perhaps above all where they concern mean trait differences among groups. To date, the discussion has focused on whether such group differences have any genetic basis, and if so, whether they are without fitness consequences and arose via random genetic drift, or whether they were driven by selection for different trait optima in different environments. Here, we highlight a plausible alternative: that many complex traits evolve under stabilizing selection in the face of shifting environmental effects. Under this scenario, there will be rapid evolution at the loci that contribute to trait variation, even when the trait optimum remains the same. These considerations underscore the strong assumptions about environmental effects that are required in ascribing trait differences among groups to genetic differences.

How well can we separate genetics from the environment?

A simulation study demonstrates a better method for separating genetic effects from environmental effects in genome-wide association studies, but there is still some way to go before this becomes a "solved" problem.

Equity in Health: Consideration of Race and Ethnicity in Precision Medicine

The causes for disparities in implementation of precision medicine are complex, due in part to differences in clinical care and a lack of engagement and recruitment of under-represented populations in studies. New tools and large genetic cohorts can change these circumstances and build access to personalized medicine for disadvantaged populations.

Understanding polygenic models, their development and the potential application of polygenic scores in healthcare

The use of genomic information to better understand and prevent common complex diseases has been an ongoing goal of genetic research. Over the past few years, research in this area has proliferated with several proposed methods of generating polygenic scores. This has been driven by the availability of larger data sets, primarily from genome-wide association studies and concomitant developments in statistical methodologies. Here we provide an overview of the methodological aspects of polygenic model construction. In addition, we consider the state of the field and implications for potential applications of polygenic scores for risk estimation within healthcare.