Tread Lightly Interpreting Polygenic Tests of Selection

Quantile-specific confounding: correction for subtle population stratification via quantile regression

Subtle population structure remains a significant concern in genome-wide association studies. Using human height as an example, we show how quantile regression, a natural extension of linear regression, can better correct for subtle population structure due to its inherent ability to adjust for quantile-specific effects of covariates such as principal components. We utilize data from the UK biobank and the SardiNIA/ProgeNIA project for demonstration.

A path integral approach for allele frequency dynamics under polygenic selection

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence (E&R) experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a role in a given allele frequency change (AFC). Predicting AFCs under drift and selection, even for alleles contributing to simple, monogenic traits, has remained a challenging problem. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here, we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. We derive analytic expressions for the transition probability (i.e. the probability that an allele will change in frequency from x to y in time t) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of AFC to test for selection, as well as explore optimal design choices for E&R experiments to uncover the genetic architecture of polygenic traits under selection.

Forensic height estimation using polygenic score in Korean population

Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.

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.

A Path Integral Approach for Allele Frequency Dynamics Under Polygenic Selection

Many phenotypic traits have a polygenic genetic basis, making it challenging to learn their genetic architectures and predict individual phenotypes. One promising avenue to resolve the genetic basis of complex traits is through evolve-and-resequence experiments, in which laboratory populations are exposed to some selective pressure and trait-contributing loci are identified by extreme frequency changes over the course of the experiment. However, small laboratory populations will experience substantial random genetic drift, and it is difficult to determine whether selection played a roll in a given allele frequency change. Predicting how much allele frequencies change under drift and selection had remained an open problem well into the 21st century, even those contributing to simple, monogenic traits. Recently, there have been efforts to apply the path integral, a method borrowed from physics, to solve this problem. So far, this approach has been limited to genic selection, and is therefore inadequate to capture the complexity of quantitative, highly polygenic traits that are commonly studied. Here we extend one of these path integral methods, the perturbation approximation, to selection scenarios that are of interest to quantitative genetics. In particular, we derive analytic expressions for the transition probability (i.e., the probability that an allele will change in frequency from x , to y in time t ) of an allele contributing to a trait subject to stabilizing selection, as well as that of an allele contributing to a trait rapidly adapting to a new phenotypic optimum. We use these expressions to characterize the use of allele frequency change to test for selection, as well as explore optimal design choices for evolve-and-resequence experiments to uncover the genetic architecture of polygenic traits under selection.

Gene expression plasticity followed by genetic change during colonization in a high-elevation environment

Phenotypic plasticity facilitates organismal invasion of novel environments, and the resultant phenotypic change may later be modified by genetic change, so called 'plasticity first.' Herein, we quantify gene expression plasticity and regulatory adaptation in a wild bird (Eurasian Tree Sparrow) from its original lowland (ancestral stage), experimentally implemented hypoxia acclimation (plastic stage), and colonized highland (colonized stage). Using a group of co-expressed genes from the cardiac and flight muscles, respectively, we demonstrate that gene expression plasticity to hypoxia tolerance is more often reversed than reinforced at the colonized stage. By correlating gene expression change with muscle phenotypes, we show that colonized tree sparrows reduce maladaptive plasticity that largely associated with decreased hypoxia tolerance. Conversely, adaptive plasticity that is congruent with increased hypoxia tolerance is often reinforced in the colonized tree sparrows. Genes displaying large levels of reinforcement or reversion plasticity (i.e. 200% of original level) show greater genetic divergence between ancestral and colonized populations. Overall, our work demonstrates that gene expression plasticity at the initial stage of high-elevation colonization can be reversed or reinforced through selection-driven adaptive modification.

Unveiling recent and ongoing adaptive selection in human populations

Genome-wide scans for signals of selection have become a routine part of the analysis of population genomic variation datasets and have resulted in compelling evidence of selection during recent human evolution. This Essay spotlights methodological innovations that have enabled the detection of selection over very recent timescales, even in contemporary human populations. By harnessing large-scale genomic and phenotypic datasets, these new methods use different strategies to uncover connections between genotype, phenotype, and fitness. This Essay outlines the rationale and key findings of each strategy, discusses challenges in interpretation, and describes opportunities to improve detection and understanding of ongoing selection in human populations.

'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.

A genome-wide comparison between selected and unselected Valle del Belice sheep reveals differences in population structure and footprints of recent selection

About three decades of breeding and selection in the Valle del Belìce sheep are expected to have left several genomic footprints related to milk production traits. In this study, we have assembled a dataset with 451 individuals of the Valle del Belìce sheep breed: 184 animals that underwent directional selection for milk production and 267 unselected animals, genotyped for 40,660 single-nucleotide polymorphisms (SNPs). Three different statistical approaches, both within (iHS and ROH) and between (Rsb) groups, were used to identify genomic regions potentially under selection. Population structure analyses separated all individuals according to their belonging to the two groups. A total of four genomic regions on two chromosomes were jointly identified by at least two statistical approaches. Several candidate genes for milk production were identified, corroborating the polygenic nature of this trait and which may provide clues to potential new selection targets. We also found candidate genes for growth and reproductive traits. Overall, the identified genes may explain the effect of selection to improve the performances related to milk production traits in the breed. Further studies using high-density array data, would be particularly relevant to refine and validate these results.

Low and differential polygenic score generalizability among African populations due largely to genetic diversity

African populations are vastly underrepresented in genetic studies but have the most genetic variation and face wide-ranging environmental exposures globally. Because systematic evaluations of genetic prediction had not yet been conducted in ancestries that span African diversity, we calculated polygenic risk scores (PRSs) in simulations across Africa and in empirical data from South Africa, Uganda, and the United Kingdom to better understand the generalizability of genetic studies. PRS accuracy improves with ancestry-matched discovery cohorts more than from ancestry-mismatched studies. Within ancestrally and ethnically diverse South African individuals, we find that PRS accuracy is low for all traits but varies across groups. Differences in African ancestries contribute more to variability in PRS accuracy than other large cohort differences considered between individuals in the United Kingdom versus Uganda. We computed PRS in African ancestry populations using existing European-only versus ancestrally diverse genetic studies; the increased diversity produced the largest accuracy gains for hemoglobin concentration and white blood cell count, reflecting large-effect ancestry-enriched variants in genes known to influence sickle cell anemia and the allergic response, respectively. Differences in PRS accuracy across African ancestries originating from diverse regions are as large as across out-of-Africa continental ancestries, requiring commensurate nuance.

On the origin and structure of haplotype blocks

The term "haplotype block" is commonly used in the developing field of haplotype-based inference methods. We argue that the term should be defined based on the structure of the Ancestral Recombination Graph (ARG), which contains complete information on the ancestry of a sample. We use simulated examples to demonstrate key features of the relationship between haplotype blocks and ancestral structure, emphasizing the stochasticity of the processes that generate them. Even the simplest cases of neutrality or of a "hard" selective sweep produce a rich structure, often missed by commonly used statistics. We highlight a number of novel methods for inferring haplotype structure, based on the full ARG, or on a sequence of trees, and illustrate how they can be used to define haplotype blocks using an empirical data set. While the advent of new, computationally efficient methods makes it possible to apply these concepts broadly, they (and additional new methods) could benefit from adding features to explore haplotype blocks, as we define them. Understanding and applying the concept of the haplotype block will be essential to fully exploit long and linked-read sequencing technologies.

15 years of GWAS discovery: Realizing the promise

It has been 15 years since the advent of the genome-wide association study (GWAS) era. Here, we review how this experimental design has realized its promise by facilitating an impressive range of discoveries with remarkable impact on multiple fields, including population genetics, complex trait genetics, epidemiology, social science, and medicine. We predict that the emergence of large-scale biobanks will continue to expand to more diverse populations and capture more of the allele frequency spectrum through whole-genome sequencing, which will further improve our ability to investigate the causes and consequences of human genetic variation for complex traits and diseases.

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.

Mosaic patterns of selection in genomic regions associated with diverse human traits

Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer's disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome.

Polygenic scores in biomedical research

Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.

Uncovering signals of positive selection in Peruvian populations from three ecological regions

Perú hosts extremely diverse ecosystems which can be broadly classified into three major ecoregions: the Pacific desert coast, the Andean highlands, and the Amazon rainforest. Since its initial peopling approximately 12,000 years ago, the populations inhabiting such ecoregions might have differentially adapted to their contrasting environmental pressures. Previous studies have described several candidate genes underlying adaptation to hypobaric hypoxia among Andean highlanders. However, the adaptive genetic diversity of coastal and rainforest populations has been less studied. Here, we gathered genome-wide SNP-array data from 286 Peruvians living across the three ecoregions and analysed signals of recent positive selection through population differentiation and haplotype-based selection scans. Among highland populations, we identify candidate genes related to cardiovascular function (TLL1, DUSP27, TBX5, PLXNA4, SGCD), to the Hypoxia-Inducible Factor pathway (TGFA, APIP), to skin pigmentation (MITF), as well as to glucose (GLIS3) and glycogen metabolism (PPP1R3C, GANC). In contrast, most signatures of adaptation in coastal and rainforest populations comprise candidate genes related to the immune system (including SIGLEC8, TRIM21, CD44 and ICAM1 in the coast; CBLB and PRDM1 in rainforest and the BRD2- HLA-DOA- HLA-DPA1 region in both), possibly as a result of strong pathogen-driven selection. This study identifies candidate genes related to human adaptation to the diverse environments of South America.

The background and legacy of Lewontin's apportionment of human genetic diversity

Lewontin's 1972 article 'The apportionment of human diversity' described a key feature of human genetic diversity that would have profound impacts on conversations regarding genetics and race: the typical genetic locus varies much less between classical human race groupings than one might infer from inspecting the features historically used to define those races, like skin pigmentation. From this, Lewontin concluded: 'Human racial classification … is now seen to be of virtually no genetic or taxonomic significance' (p. 397). Here, 50 years after the paper's publication, the goal is to understand the origins and legacy of the paper. Aided by insights from published papers and interviews with several of Lewontin's contemporaries, I review the 1972 paper, asking about the intellectual background that led to the publication of the paper, the development of its impact, the critiques of the work and the work's application and limitations today. The hope is that by gaining a clearer understanding of the origin and reasoning of the paper, we might dispel various confusions about the result and sharpen an understanding of the enduring value and insight the result provides. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

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'.

Human genetic admixture through the lens of population genomics

Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, 'The Apportionment of Human Diversity'. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture. This article is part of the theme issue 'Celebrating 50 years since Lewontin's apportionment of human diversity'.

Challenging the utility of polygenic scores for social science: Environmental confounding, downward causation, and unknown biology

The sociogenomics revolution is upon us, we are told. Whether revolutionary or not, sociogenomics is poised to flourish given the ease of incorporating polygenic scores (or PGSs) as "genetic propensities" for complex traits into social science research. Pointing to evidence of ubiquitous heritability and the accessibility of genetic data, scholars have argued that social scientists not only have an opportunity but a duty to add PGSs to social science research. Social science research that ignores genetics is, some proponents argue, at best partial and likely scientifically flawed, misleading, and wasteful. Here, I challenge arguments about the value of genetics for social science and with it the claimed necessity of incorporating PGSs into social science models as measures of genetic influences. In so doing, I discuss the impracticability of distinguishing genetic influences from environmental influences because of non-causal gene-environment correlations, especially population stratification, familial confounding, and downward causation. I explain how environmental effects masquerade as genetic influences in PGSs, which undermines their raison d'être as measures of genetic propensity, especially for complex socially contingent behaviors that are the subject of sociogenomics. Additionally, I draw attention to the partial, unknown biology, while highlighting the persistence of an implicit, unavoidable reductionist genes versus environments approach. Leaving sociopolitical and ethical concerns aside, I argue that the potential scientific rewards of adding PGSs to social science are few and greatly overstated and the scientific costs, which include obscuring structural disadvantages and cultural influences, outweigh these meager benefits for most social science applications.

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.

Evidence for continent-wide convergent evolution and stasis throughout 150 y of a biological invasion

Adaptive evolution can help species to persist and spread in new environments, but it is unclear how the rate and duration of adaptive evolution vary throughout species ranges and on the decadal timescales most relevant to managing biodiversity for the 21st century. Using herbarium records, we reconstruct 150 y of evolution in an invasive plant as it spread across North America. Flowering phenology evolves to adapt to local growing seasons throughout the range but stalls after about a century. This punctuated, convergent evolution recapitulates long-term dynamics in the fossil record, implicating limits to evolutionary rates that are not evident for the first century of spread.

The extent to which evolution can rescue a species from extinction, or facilitate range expansion, depends critically on the rate, duration, and geographical extent of the evolutionary response to natural selection. Adaptive evolution can occur quickly, but the duration and geographical extent of contemporary evolution in natural systems remain poorly studied. This is particularly true for species with large geographical ranges and for timescales that lie between “long-term” field experiments and the fossil record. Here, we introduce the Virtual Common Garden (VCG) to investigate phenotypic evolution in natural history collections while controlling for phenotypic plasticity in response to local growing conditions. Reconstructing 150 y of evolution in Lythrum salicaria (purple loosestrife) as it invaded North America, we analyze phenology measurements of 3,429 herbarium records, reconstruct growing conditions from more than 12 million local temperature records, and validate predictions across three common gardens spanning 10° of latitude. We find that phenological clines have evolved repeatedly throughout the range, during the first century of evolution. Thereafter, the rate of microevolution stalls, recapitulating macroevolutionary stasis observed in the fossil record. Our study demonstrates that preserved specimens are a critical resource for investigating limits to evolution in natural populations. Our results show how natural selection and trade-offs measured in field studies predict adaptive divergence observable in herbarium specimens over 15 decades at a continental scale.

Evolution in small steps and giant leaps

The first Editor of Evolution was Ernst Mayr. His foreword to the first issue of Evolution published in 1947 framed evolution as a "problem of interaction" that was just beginning to be studied in this broad context. First, I explore progress and prospects on understanding the subsidiary interactions identified by Mayr, including interactions between parts of organisms, between individuals and populations, between species, and between the organism and its abiotic environment. Mayr's overall "problem of interaction" framework is examined in the context of coevolution within and among levels of biological organization. This leads to a comparison in the relative roles of biotic versus abiotic agents of selection and fluctuating versus directional selection, followed by stabilizing selection in shaping the genomic architecture of adaptation. Oligogenic architectures may be typical for traits shaped more by fluctuating selection and biotic selection. Conversely, polygenic architectures may be typical for traits shaped more by directional followed by stabilizing selection and abiotic selection. The distribution of effect sizes and turnover dynamics of adaptive alleles in these scenarios deserves further study. Second, I review two case studies on the evolution of acquired toxicity in animals, one involving cardiac glycosides obtained from plants and one involving bacterial virulence factors horizontally transferred to animals. The approaches used in these studies and the results gained directly flow from Mayr's vision of an evolutionary biology that revolves around the "problem of interaction."

Using singleton densities to detect recent selection in Bos taurus

Many quantitative traits are subject to polygenic selection, where several genomic regions undergo small, simultaneous changes in allele frequency that collectively alter a phenotype. The widespread availability of genome data, along with novel statistical techniques, has made it easier to detect these changes. We apply one such method, the "Singleton Density Score" (SDS), to the Holstein breed of Bos taurus to detect recent selection (arising up to around 740 years ago). We identify several genes as candidates for targets of recent selection, including some relating to cell regulation, catabolic processes, neural-cell adhesion and immunity. We do not find strong evidence that three traits that are important to humans-milk protein content, milk fat content, and stature-have been subject to directional selection. Simulations demonstrate that because B. taurus recently experienced a population bottleneck, singletons are depleted so the power of SDS methods is reduced. These results inform on which genes underlie recent genetic change in B. taurus, while providing information on how polygenic selection can be best investigated in future studies.

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.

The genetic architecture and population genomic signatures of glyphosate resistance in Amaranthus tuberculatus

Much of what we know about the genetic basis of herbicide resistance has come from detailed investigations of monogenic adaptation at known target-sites, despite the increasingly recognized importance of polygenic resistance. Little work has been done to characterize the broader genomic basis of herbicide resistance, including the number and distribution of genes involved, their effect sizes, allele frequencies and signatures of selection. In this work, we implemented genome-wide association (GWA) and population genomic approaches to examine the genetic architecture of glyphosate (Round-up) resistance in the problematic agricultural weed Amaranthus tuberculatus. A GWA was able to correctly identify the known target-gene but statistically controlling for two causal target-site mechanisms revealed an additional 250 genes across all 16 chromosomes associated with non-target-site resistance (NTSR). The encoded proteins had functions that have been linked to NTSR, the most significant of which is response to chemicals, but also showed pleiotropic roles in reproduction and growth. Compared to an empirical null that accounts for complex population structure, the architecture of NTSR was enriched for large effect sizes and low allele frequencies, suggesting the role of pleiotropic constraints on its evolution. The enrichment of rare alleles also suggested that the genetic architecture of NTSR may be population-specific and heterogeneous across the range. Despite their rarity, we found signals of recent positive selection on NTSR-alleles by both window- and haplotype-based statistics, and an enrichment of amino acid changing variants. In our samples, genome-wide single nucleotide polymorphisms explain a comparable amount of the total variation in glyphosate resistance to monogenic mechanisms, even in a collection of individuals where 80% of resistant individuals have large-effect TSR mutations, indicating an underappreciated polygenic contribution to the evolution of herbicide resistance in weed populations.

Genetic Risk Prediction of COVID-19 Susceptibility and Severity in the Indian Population

Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al. determining the genetic susceptibility to severe COVID-19 infection, to compute population-wise polygenic risk scores (PRS) for populations represented in the Indian Genome Variation Consortium (IGVC) database. Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10⁻¹⁶) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.

Allele frequency differentiation at height-associated SNPs among continental human populations

Methods to detect polygenic adaptation have recently been shown to be sensitive to uncorrected stratification in GWAS, thereby casting doubts on whether polygenic adaptation is prevalent among humans. Consistent with a signal of adaptation at human height loci, the mean F_ST among African, East Asian, and European populations was shown to be significantly higher at height-associated SNPs than that at non-associated SNPs. This conclusion was reached, however, using height-associated SNPs ascertained from a GWAS design impacted by residual confounding due to uncorrected stratification. Specifically, we show here that the estimated effect sizes are significantly correlated with population structure across continents, potentially explaining the elevated differentiation previously reported. We alleviated these concerns of confounding by ascertaining height-associated SNPs from two biobank GWAS (UK Biobank, UKB, and Biobank Japan, BBJ), where measures to control for confounding in GWAS are more effective. Consistent with a global signature of polygenic adaptation, we found that compared to non-associated SNPs, frequencies of height-associated SNPs are indeed significantly more differentiated among continental populations from both the 1000 Genomes Project (p = 0.0012 for UKB and p = 0.0265 for BBJ), and the Human Genome Diversity Project (p = 0.0225 for UKB and p = 0.0032 for BBJ). However, we found no significant difference among continental populations in polygenic height scores. Through simulations, we found that polygenic score-based statistics could lose power in detecting polygenic adaptation in presence of independent converging selections, thereby potentially explaining the inconsistent results based on F_ST and polygenic scores.

The omnigenic model and polygenic prediction of complex traits

The omnigenic model was proposed as a framework to understand the highly polygenic architecture of complex traits revealed by genome-wide association studies (GWASs). I argue that this model also explains recent observations about cross-population genetic effects, specifically the low transferability of polygenic scores and the lack of clear evidence for polygenic selection. In particular, the omnigenic model explains why the effects of most GWAS variants vary between populations. This interpretation has several consequences for the evolutionary interpretation and practical use of GWAS summary statistics and polygenic scores. First, some polygenic scores may be applicable only in populations of the same ancestry and environment as the discovery population. Second, most GWAS associations will have differing effects between populations and are unlikely to be robust clinical targets. Finally, it may not always be possible to detect polygenic selection from population genetic data. These considerations make it difficult to interpret the clinical and evolutionary meanings of polygenic scores without an explicit model of genetic architecture.

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.

The Phenotypic Consequences of Genetic Divergence between Admixed Latin American Populations: Antioquia and Chocó, Colombia

Genome-wide association studies have uncovered thousands of genetic variants that are associated with a wide variety of human traits. Knowledge of how trait-associated variants are distributed within and between populations can provide insight into the genetic basis of group-specific phenotypic differences, particularly for health-related traits. We analyzed the genetic divergence levels for 1) individual trait-associated variants and 2) collections of variants that function together to encode polygenic traits, between two neighboring populations in Colombia that have distinct demographic profiles: Antioquia (Mestizo) and Chocó (Afro-Colombian). Genetic ancestry analysis showed 62% European, 32% Native American, and 6% African ancestry for Antioquia compared with 76% African, 10% European, and 14% Native American ancestry for Chocó, consistent with demography and previous results. Ancestry differences can confound cross-population comparison of polygenic risk scores (PRS); however, we did not find any systematic bias in PRS distributions for the two populations studied here, and population-specific differences in PRS were, for the most part, small and symmetrically distributed around zero. Both genetic differentiation at individual trait-associated single nucleotide polymorphisms and population-specific PRS differences between Antioquia and Chocó largely reflected anthropometric phenotypic differences that can be readily observed between the populations along with reported disease prevalence differences. Cases where population-specific differences in genetic risk did not align with observed trait (disease) prevalence point to the importance of environmental contributions to phenotypic variance, for both infectious and complex, common disease. The results reported here are distributed via a web-based platform for searching trait-associated variants and PRS divergence levels at http://map.chocogen.com (last accessed August 12, 2020).

How White nationalists mobilize genetics: From genetic ancestry and human biodiversity to counterscience and metapolitics

OBJECTIVES

Our aim in this study was to understand how genetics ideas are appropriated and mobilized online toward the political projects of White nationalism and the alt right. Studying three different online venues, we investigated how genetics is used to support racial realism, hereditarianism, and racial hierarchy. We analyzed how these ideas are connected to political and metapolitical projects. In addition, we examined the strategies used to build authority for these interpretations.

METHODS

We analyze three online venues in which genetics has been mobilized to advance racial realism and hereditarian explanations of racial differences. These were (a) the use of genetic ancestry tests in online nationalist discussions, (b) blogs and other venues in which the human biodiversity ideas are articulated, (c) activities surrounding the OpenPsych collection of online journals. Ethnographic and interpretive methods were applied to investigate scientific and political meanings of efforts to mobilize genetic ideas.

RESULTS

We found that White nationalists use genetic ancestry tests to align White identity with ideas of racial purity and diversity, educating each other about genetics, and debating the boundaries of Whiteness. "Human biodiversity" has been mobilized as a movement to catalog and create hereditarian ideas about racial differences and to distribute them as "red pills" to transform online discourse. The OpenPsych journals have allowed amateur hereditarian psychologists to publish papers, coordinate activity, and legitimate their project at the academic margins.

CONCLUSIONS

These various appropriations of genetics aim to further racial realism and hereditarian explanations of racial social and behavioral differences. Beyond these substantive aims, on a "metapolitical" level, they serve to reframe concepts and standards for political and scientific discussion of race, challenge structures of academic legitimacy and expertise, and build a cadre of ideological foot soldiers armed with an argumentative toolkit. As professional anthropologists and geneticists aim to accurately communicate their science and its implications for understanding human differences to the public, they must contend with these substantive claims and metapolitical contexts.

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.

Evolutionary genetics of skin pigmentation in African populations

Skin color is a highly heritable human trait, and global variation in skin pigmentation has been shaped by natural selection, migration and admixture. Ethnically diverse African populations harbor extremely high levels of genetic and phenotypic diversity, and skin pigmentation varies widely across Africa. Recent genome-wide genetic studies of skin pigmentation in African populations have advanced our understanding of pigmentation biology and human evolutionary history. For example, novel roles in skin pigmentation for loci near MFSD12 and DDB1 have recently been identified in African populations. However, due to an underrepresentation of Africans in human genetic studies, there is still much to learn about the evolutionary genetics of skin pigmentation. Here, we summarize recent progress in skin pigmentation genetics in Africans and discuss the importance of including more ethnically diverse African populations in future genetic studies. In addition, we discuss methods for functional validation of adaptive variants related to skin pigmentation.

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.

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.

Genome-Wide Natural Selection Signatures Are Linked to Genetic Risk of Modern Phenotypes in the Japanese Population

Elucidation of natural selection signatures and relationships with phenotype spectra is important to understand adaptive evolution of modern humans. Here, we conducted a genome-wide scan of selection signatures of the Japanese population by estimating locus-specific time to the most recent common ancestor using the ascertained sequentially Markovian coalescent (ASMC), from the biobank-based large-scale genome-wide association study data of 170,882 subjects. We identified 29 genetic loci with selection signatures satisfying the genome-wide significance. The signatures were most evident at the alcohol dehydrogenase (ADH) gene cluster locus at 4q23 (P_ASMC = 2.2 × 10⁻³⁶), followed by relatively strong selection at the FAM96A (15q22), MYOF (10q23), 13q21, GRIA2 (4q32), and ASAP2 (2p25) loci (P_ASMC < 1.0 × 10⁻¹⁰). The additional analysis interrogating extended haplotypes (integrated haplotype score) showed robust concordance of the detected signatures, contributing to fine-mapping of the genes, and provided allelic directional insights into selection pressure (e.g., positive selection for ADH1B-Arg48His and HLA-DPB1*04:01). The phenome-wide selection enrichment analysis with the trait-associated variants identified a variety of the modern human phenotypes involved in the adaptation of Japanese. We observed population-specific evidence of enrichment with the alcohol-related phenotypes, anthropometric and biochemical clinical measurements, and immune-related diseases, differently from the findings in Europeans using the UK Biobank resource. Our study demonstrated population-specific features of the selection signatures in Japanese, highlighting a value of the natural selection study using the nation-wide biobank-scale genome and phenotype data.

Genetic risk scores for cardiometabolic traits in sub-Saharan African populations

BACKGROUND

There is growing support for the use of genetic risk scores (GRS) in routine clinical settings. Due to the limited diversity of current genomic discovery samples, there are concerns that the predictive power of GRS will be limited in non-European ancestry populations. GRS for cardiometabolic traits were evaluated in sub-Saharan Africans in comparison with African Americans and European Americans.

METHODS

We evaluated the predictive utility of GRS for 12 cardiometabolic traits in sub-Saharan Africans (AF; n = 5200), African Americans (AA; n = 9139) and European Americans (EUR; n = 9594). GRS were constructed as weighted sums of the number of risk alleles. Predictive utility was assessed using the additional phenotypic variance explained and the increase in discriminatory ability over traditional risk factors [age, sex and body mass index (BMI)], with adjustment for ancestry-derived principal components.

RESULTS

Across all traits, GRS showed up to a 5-fold and 20-fold greater predictive utility in EUR relative to AA and AF, respectively. Predictive utility was most consistent for lipid traits, with percentage increase in explained variation attributable to GRS ranging from 10.6% to 127.1% among EUR, 26.6% to 65.8% among AA and 2.4% to 37.5% among AF. These differences were recapitulated in the discriminatory power, whereby the predictive utility of GRS was 4-fold greater in EUR relative to AA and up to 44-fold greater in EUR relative to AF. Obesity and blood pressure traits showed a similar pattern of greater predictive utility among EUR.

CONCLUSIONS

This work demonstrates the poorer performance of GRS in AF and highlights the need to improve representation of multiple ethnic populations in genomic studies to ensure equitable clinical translation of GRS.

The Evolutionary History of Common Genetic Variants Influencing Human Cortical Surface Area

Structural brain changes along the lineage leading to modern Homo sapiens contributed to our distinctive cognitive and social abilities. However, the evolutionarily relevant molecular variants impacting key aspects of neuroanatomy are largely unknown. Here, we integrate evolutionary annotations of the genome at diverse timescales with common variant associations from large-scale neuroimaging genetic screens. We find that alleles with evidence of recent positive polygenic selection over the past 2000-3000 years are associated with increased surface area (SA) of the entire cortex, as well as specific regions, including those involved in spoken language and visual processing. Therefore, polygenic selective pressures impact the structure of specific cortical areas even over relatively recent timescales. Moreover, common sequence variation within human gained enhancers active in the prenatal cortex is associated with postnatal global SA. We show that such variation modulates the function of a regulatory element of the developmentally relevant transcription factor HEY2 in human neural progenitor cells and is associated with structural changes in the inferior frontal cortex. These results indicate that non-coding genomic regions active during prenatal cortical development are involved in the evolution of human brain structure and identify novel regulatory elements and genes impacting modern human brain structure.

Scanning the human genome for "signatures" of positive selection: Transformative opportunities and ethical obligations

The relationship history of evolutionary anthropology and genetics is complex. At best, genetics is a beautifully integrative part of the discipline. Yet this integration has also been fraught, with punctuated, disruptive challenges to dogma, periodic reluctance by some members of the field to embrace results from analyses of genetic data, and occasional over-assertions of genetic definitiveness by geneticists. At worst, evolutionary genetics has been a tool for reinforcing racism and colonialism. While a number of genetics/genomics papers have disproportionately impacted evolutionary anthropology, here we highlight the 2002 presentation of an elegantly powerful approach for identifying "signatures" of past positive selection from haplotype-based patterns of genetic variation. Together with technological advances in genotyping methods, this article transformed our field by facilitating genome-wide "scans" for signatures of past positive selection in human populations. This approach helped researchers test longstanding evolutionary anthropology hypotheses while simultaneously providing opportunities to develop entirely new ones. Genome-wide scans for signatures of positive selection have since been conducted in diverse worldwide populations, with striking findings of local adaptation and convergent evolution. Yet there are ethical considerations with respect to the ubiquity of these studies and the cross-application of the genome-wide scan approach to existing datasets, which we also discuss.

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.

Polygenic adaptation: a unifying framework to understand positive selection

Most adaption processes have a polygenic genetic basis, but even with the recent explosive growth of genomic data we are still lacking a unified framework describing the dynamics of selected alleles. Building on recent theoretical and empirical work we introduce the concept of adaptive architecture, which extends the genetic architecture of an adaptive trait by factors influencing its adaptive potential and population genetic principles. Because adaptation can be typically achieved by many different combinations of adaptive alleles (redundancy), we describe how two characteristics - heterogeneity among loci and non-parallelism between replicated populations - are hallmarks for the characterization of polygenic adaptation in evolving populations. We discuss how this unified framework can be applied to natural and experimental populations.

Polygenic Scores in Developmental Psychology: Invite Genetics In, Leave Biodeterminism Behind

Polygenic scores offer developmental psychologists new methods for integrating genetic information into research on how people change and develop across the life span. Indeed, polygenic scores have correlations with developmental outcomes that rival correlations with traditional developmental psychology variables, such as family income. Yet linking people's genetics with differences between them in socially valued developmental outcomes, such as educational attainment, has historically been used to justify acts of state-sponsored violence. In this review, we emphasize that an interdisciplinary understanding of the environmental and structural determinants of social inequality, in conjunction with a transactional developmental perspective on how people interact with their environments, is critical to interpreting associations between polygenic measures and phenotypes. While there is a risk of misuse, early applications of polygenic scores to developmental psychology have already provided novel findings that identify environmental mechanisms of life course processes that can be used to diagnose inequalities in social opportunity.

Polygenic Scores for Height in Admixed Populations

Polygenic risk scores (PRS) use the results of genome-wide association studies (GWAS) to predict quantitative phenotypes or disease risk at an individual level, and provide a potential route to the use of genetic data in personalized medical care. However, a major barrier to the use of PRS is that the majority of GWAS come from cohorts of European ancestry. The predictive power of PRS constructed from these studies is substantially lower in non-European ancestry cohorts, although the reasons for this are unclear. To address this question, we investigate the performance of PRS for height in cohorts with admixed African and European ancestry, allowing us to evaluate ancestry-related differences in PRS predictive accuracy while controlling for environment and cohort differences. We first show that the predictive accuracy of height PRS increases linearly with European ancestry and is partially explained by European ancestry segments of the admixed genomes. We show that recombination rate, differences in allele frequencies, and differences in marginal effect sizes across ancestries all contribute to the decrease in predictive power, but none of these effects explain the decrease on its own. Finally, we demonstrate that prediction for admixed individuals can be improved by using a linear combination of PRS that includes ancestry-specific effect sizes, although this approach is at present limited by the small size of non-European ancestry discovery cohorts.

Biological anthropology's critical engagement with genomics, evolution, race/racism, and ourselves: Opportunities and challenges to making a difference in the academy and the world

Biological anthropology can, and should, matter in the Anthropocene. Biological anthropologists are interested in human biology and the human experience in a broader ecological, evolutionary, and phylogenetic context. We are interested in the material of the body, the history of the body, and interactions of diverse bodies, communities, ecologies, and evolutionary processes. However, the cultural realities of bodies, histories, communities, livelihoods, perceptions, and experiences are as central to the endeavor and inquiry of biological anthropology as are their material aspects. Biological anthropology is a constant dialectic between the cultural and the biological. In this essay, I argue that Biological Anthropology has much to offer, a history to contend with, and a future that matters. To illustrate this, I highlight theoretical and methodological issues in genomics, evolutionary theory and connect them to the study of Race and Racism to emphasize specific arenas where Biological Anthropology has a great capacity, and a strong obligation, to play a central role. However, Biological Anthropology also has substantive internal issues that hinder our ability to do the best possible science. If we are to live up to our potential and make a difference in the 21st century we need to ameliorate our structural shortcomings and expand our voice, and impact, in academic and public discourse. The goal of this perspective is to offer suggestions for moving us toward this goal.

Theoretical and empirical quantification of the accuracy of polygenic scores in ancestry divergent populations

Polygenic scores (PGS) have been widely used to predict disease risk using variants identified from genome-wide association studies (GWAS). To date, most GWAS have been conducted in populations of European ancestry, which limits the use of GWAS-derived PGS in non-European ancestry populations. Here, we derive a theoretical model of the relative accuracy (RA) of PGS across ancestries. We show through extensive simulations that the RA of PGS based on genome-wide significant SNPs can be predicted accurately from modelling linkage disequilibrium (LD), minor allele frequencies (MAF), cross-population correlations of causal SNP effects and heritability. We find that LD and MAF differences between ancestries can explain between 70 and 80% of the loss of RA of European-based PGS in African ancestry for traits like body mass index and type 2 diabetes. Our results suggest that causal variants underlying common genetic variation identified in European ancestry GWAS are mostly shared across continents.

The importance of including ethnically diverse populations in studies of quantitative trait evolution

For many traits, human variation is less a matter of categorical differences than quantitative variation, such as height, where individuals fall along a continuum from short to tall. Most recent studies utilize large population-based samples with whole-genome sequences to study the evolution of these traits and have made significant progress implementing a broad spectrum of techniques. However, relatively few studies of quantitative trait evolution include ethnically diverse populations, which often harbor the highest levels of genetic and phenotypic diversity. Thus, our ability to draw inferences about quantitative trait adaptation has been limited. Here, we review recent studies examining human quantitative trait adaptation, and argue that including ethnically diverse populations, particularly from Africa, will be especially informative for our understanding of how humans adapt to the world around them.

Distinct Patterns of Selective Sweep and Polygenic Adaptation in Evolve and Resequence Studies

In molecular population genetics, adaptation is typically thought to occur via selective sweeps, where targets of selection have independent effects on the phenotype and rise to fixation, whereas in quantitative genetics, many loci contribute to the phenotype and subtle frequency changes occur at many loci during polygenic adaptation. The sweep model makes specific predictions about frequency changes of beneficial alleles and many test statistics have been developed to detect such selection signatures. Despite polygenic adaptation is probably the prevalent mode of adaptation, because of the traditional focus on the phenotype, we are lacking a solid understanding of the similarities and differences of selection signatures under the two models. Recent theoretical and empirical studies have shown that both selective sweep and polygenic adaptation models could result in a sweep-like genomic signature; therefore, additional criteria are needed to distinguish the two models. With replicated populations and time series data, experimental evolution studies have the potential to identify the underlying model of adaptation. Using the framework of experimental evolution, we performed computer simulations to study the pattern of selected alleles for two models: 1) adaptation of a trait via independent beneficial mutations that are conditioned for fixation, that is, selective sweep model and 2) trait optimum model (polygenic adaptation), that is adaptation of a quantitative trait under stabilizing selection after a sudden shift in trait optimum. We identify several distinct patterns of selective sweep and trait optimum models in populations of different sizes. These features could provide the foundation for development of quantitative approaches to differentiate the two models.