Evidence of directional and stabilizing selection in contemporary humans

Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N ~ 408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N ~ 40,466), where the genetically most variable individuals had increased conventional PGS accuracy (by ~19%) relative to the genetically least variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.

Association of toll like receptors polymorphism (TLR1-rs4833095, TLR1-rs5743611, TLR6-rs5743810, TLR6-rs5743809, TLR4-rs4986790, TLR4-rs4986791, TLR9 rs187084) with clinical outcome of malaria among children in Ibadan, Southwest Nigeria

Different genetic polymorphisms, particularly in the erythrocyte receptors and immune response-related genes, have been implicated in the development of malaria. With the first immune response to Plasmodium falciparum related to the activity of toll-like receptors (TLRs), we investigated the association of TLR polymorphisms with the clinical outcome of malaria among 662 children in Ibadan, Nigeria. The participants were genotyped for TLR1-rs5743611, TLR1-rs4833095, TLR4-rs4986791, TLR4-rs4986790, TLR6-rs5743810, TLR9-rs187084 and TLR9-rs5743809 using TaqMan real-time PCR probes and analyzed using the Sequenom iPLEX platform. Statistical analyses were performed using PLINK 2.0, Haploview 4.2 and SPSS® 20.0. Overall, the TLR genes were consistent with the Hardy-Weinberg equilibrium. The minor allelic frequency (MAF) of TRL1-rs4833095, TLR4-rs4986790, TLR4-rs4986791, TLR9-rs187084, TLR9-rs5743809 was 0.094, 0.089, 0.011, 0.288, and 0.044, respectively. The CT genotype of TLR1-rs4833095 was significantly associated with increased susceptibility to clinical malaria. Similarly, the GA and CT genotypes of TLR4-rs4986790 and TLR4-rs4986791, respectively, were linked to susceptibility to complicated malaria. TLR9-rs187084 CT was associated with the development of uncomplicated malaria, while TLR6-rs5743809 showed no significant association with malaria. Notably, TLR1-rs5743611 and TLR6-rs5743810 were monomorphic in the population. This study, pioneering in its exploration of TLR polymorphisms among Yorubas', underscores the need for expansive, large-scale investigations involving diverse TLR polymorphisms across multiple malaria-endemic populations.

Socio-economic status is a social construct with heritable components and genetic consequences

In civilizations, individuals are born into or sorted into different levels of socio-economic status (SES). SES clusters in families and geographically, and is robustly associated with genetic effects. Here we first review the history of scientific research on the relationship between SES and heredity. We then discuss recent findings in genomics research in light of the hypothesis that SES is a dynamic social construct that involves genetically influenced traits that help in achieving or retaining a socio-economic position, and can affect the distribution of genes associated with such traits. Social stratification results in people with differing traits being sorted into strata with different environmental exposures, which can result in evolutionary selection pressures through differences in mortality, reproduction and non-random mating. Genomics research is revealing previously concealed genetic consequences of the way society is organized, yielding insights that should be approached with caution in pursuit of a fair and functional society.

The maintenance of genetic polymorphism underlying sexually antagonistic traits

Selection often favors different trait values in males and females, leading to genetic conflicts between the sexes when traits have a shared genetic basis. Such sexual antagonism has been proposed to maintain genetic polymorphism. However, this notion is based on insights from population genetic models of single loci with fixed fitness effects. It is thus unclear how readily polymorphism emerges from sex-specific selection acting on continuous traits, where fitness effects arise from the genotype-phenotype map and the fitness landscape. Here, we model the evolution of a continuous trait that has a shared genetic basis but different optima in males and females, considering a wide variety of genetic architectures and fitness landscapes. For autosomal loci, the long-term maintenance of polymorphism requires strong conflict between males and females that generates uncharacteristic sex-specific fitness patterns. Instead, more plausible sex-specific fitness landscapes typically generate stabilizing selection leading to an evolutionarily stable state that consists of a single homozygous genotype. Except for sites tightly linked to the sex-determining region, our results indicate that genetic variation due to sexual antagonism should arise only rarely and often be transient, making these signatures challenging to detect in genomic data.

Archaic introgression and the distribution of shared variation under stabilizing selection

Many phenotypic traits are under stabilizing selection, which maintains a population's mean phenotypic value near some optimum. The dynamics of traits and trait architectures under stabilizing selection have been extensively studied for single populations at steady state. However, natural populations are seldom at steady state and are often structured in some way. Admixture and introgression events may be common, including over human evolutionary history. Because stabilizing selection results in selection against the minor allele at a trait-affecting locus, alleles from the minor parental ancestry will be selected against after admixture. We show that the site-frequency spectrum can be used to model the genetic architecture of such traits, allowing for the study of trait architecture dynamics in complex multi-population settings. We use a simple deterministic two-locus model to predict the reduction of introgressed ancestry around trait-contributing loci. From this and individual-based simulations, we show that introgressed-ancestry is depleted around such loci. When introgression between two diverged populations occurs in both directions, as has been inferred between humans and Neanderthals, the locations of such regions with depleted introgressed ancestry will tend to be shared across populations. We argue that stabilizing selection for shared phenotypic optima may explain recent observations in which regions of depleted human-introgressed ancestry in the Neanderthal genome overlap with Neanderthal-ancestry deserts in humans.

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.

Specificity, length, and luck: How genes are prioritized by rare and common variant association studies

Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance - how much a gene quantitatively affects a trait; and 2) trait specificity - a gene's importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants, while burden tests prioritize trait-specific genes. Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage.

A novel expectation-maximization approach to infer general diploid selection from time-series genetic data

Detecting and quantifying the strength of selection is a main objective in population genetics. Since selection acts over multiple generations, many approaches have been developed to detect and quantify selection using genetic data sampled at multiple points in time. Such time series genetic data is commonly analyzed using Hidden Markov Models, but in most cases, under the assumption of additive selection. However, many examples of genetic variation exhibiting non-additive mechanisms exist, making it critical to develop methods that can characterize selection in more general scenarios. Thus, we extend a previously introduced expectation-maximization algorithm for the inference of additive selection coefficients to the case of general diploid selection, in which the heterozygote and homozygote fitness are parameterized independently. We furthermore introduce a framework to identify bespoke modes of diploid selection from given data, as well as a procedure for aggregating data across linked loci to increase power and robustness. Using extensive simulation studies, we find that our method accurately and efficiently estimates selection coefficients for different modes of diploid selection across a wide range of scenarios; however, power to classify the mode of selection is low unless selection is very strong. We apply our method to ancient DNA samples from Great Britain in the last 4,450 years, and detect evidence for selection in six genomic regions, including the well-characterized LCT locus. Our work is the first genome-wide scan characterizing signals of general diploid selection.

Striking Departures from Polygenic Architecture in the Tails of Complex Traits

Understanding the genetic architecture of human traits is of key biological, medical and evolutionary importance[1]. Despite much progress, little is known about how genetic architecture varies across the trait continuum and, in particular, if it differs in the tails of complex traits, where disease often occurs. Here, applying a novel approach based on polygenic scores, we reveal striking departures from polygenic architecture across 148 quantitative trait tails, consistent with distinct concentrations of high-impact rare alleles in one or both tails of most of the traits. We demonstrate replication of these results across ancestries, cohorts, repeat measures, and using an orthogonal family-based approach[2]. Furthermore, trait tails with inferred enrichment of rare alleles are associated with more exome study hits, reduced fecundity, advanced paternal age, and lower predictive accuracy of polygenic scores. Finally, we find evidence of ongoing selection consistent with the observed departures in polygenicity and demonstrate, via simulation, that traits under stabilising selection are expected to have tails enriched for rare, large-effect alleles. Overall, our findings suggest that while common variants of small effect likely account for most of the heritability in complex traits[3], rare variants of large effect are often more important in the trait tails, particularly among individuals at highest risk of disease. Our study has implications for rare variant discovery, the utility of polygenic scores, the study of selection in humans, and for the relative importance of common and rare variants to complex traits and diseases.

Population Genomic Scans for Natural Selection and Demography

Uncovering the fundamental processes that shape genomic variation in natural populations is a primary objective of population genetics. These processes include demographic effects such as past changes in effective population size or gene flow between structured populations. Furthermore, genomic variation is affected by selection on nonneutral genetic variants, for example, through the adaptation of beneficial alleles or balancing selection that maintains genetic variation. In this article, we discuss the characterization of these processes using population genetic models, and we review methods developed on the basis of these models to unravel the underlying processes from modern population genomic data sets. We briefly discuss the conditions in which these approaches can be used to infer demography or identify specific nonneutral genetic variants and cases in which caution is warranted. Moreover, we summarize the challenges of jointly inferring demography and selective processes that affect neutral variation genome-wide.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these 2 fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we lay out a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., genome-wide association studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur analytically and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study, we re-examine an analysis testing for coevolution of expression levels between genes across a fungal phylogeny and show that including eigenvectors of the covariance matrix as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

The importance of family-based sampling for biobanks

Biobanks aim to improve our understanding of health and disease by collecting and analysing diverse biological and phenotypic information in large samples. So far, biobanks have largely pursued a population-based sampling strategy, where the individual is the unit of sampling, and familial relatedness occurs sporadically and by chance. This strategy has been remarkably efficient and successful, leading to thousands of scientific discoveries across multiple research domains, and plans for the next wave of biobanks are underway. In this Perspective, we discuss the strengths and limitations of a complementary sampling strategy for future biobanks based on oversampling of close genetic relatives. Such family-based samples facilitate research that clarifies causal relationships between putative risk factors and outcomes, particularly in estimates of genetic effects, because they enable analyses that reduce or eliminate confounding due to familial and demographic factors. Family-based biobank samples would also shed new light on fundamental questions across multiple fields that are often difficult to explore in population-based samples. Despite the potential for higher costs and greater analytical complexity, the many advantages of family-based samples should often outweigh their potential challenges.

The battle of the sexes in humans is highly polygenic

Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation. Using data from 300K individuals in the UK Biobank, we estimate the strength of SDS throughout the genome. While only a handful of loci under SDS are individually significant, we uncover highly polygenic signals of genome-wide SDS for both viability and fecundity. Selection coefficients of [Formula: see text] may be typical. Despite its ubiquity, SDS may impose a mortality load of less than 1%. An interesting life-history tradeoff emerges: Alleles that increase viability more strongly in females than males tend to increase fecundity more strongly in males than in females. Finally, we find marginal evidence of SDS on fecundity acting on alleles affecting arm fat-free mass. Taken together, our findings connect the long-standing evidence of SDS acting on human phenotypes with its impact on the genome.

Pervasive findings of directional selection realize the promise of ancient DNA to elucidate human adaptation

We present a method for detecting evidence of natural selection in ancient DNA time-series data that leverages an opportunity not utilized in previous scans: testing for a consistent trend in allele frequency change over time. By applying this to 8433 West Eurasians who lived over the past 14000 years and 6510 contemporary people, we find an order of magnitude more genome-wide significant signals than previous studies: 347 independent loci with >99% probability of selection. Previous work showed that classic hard sweeps driving advantageous mutations to fixation have been rare over the broad span of human evolution, but in the last ten millennia, many hundreds of alleles have been affected by strong directional selection. Discoveries include an increase from ~0% to ~20% in 4000 years for the major risk factor for celiac disease at HLA-DQB1; a rise from ~0% to ~8% in 6000 years of blood type B; and fluctuating selection at the TYK2 tuberculosis risk allele rising from ~2% to ~9% from ~5500 to ~3000 years ago before dropping to ~3%. We identify instances of coordinated selection on alleles affecting the same trait, with the polygenic score today predictive of body fat percentage decreasing by around a standard deviation over ten millennia, consistent with the "Thrifty Gene" hypothesis that a genetic predisposition to store energy during food scarcity became disadvantageous after farming. We also identify selection for combinations of alleles that are today associated with lighter skin color, lower risk for schizophrenia and bipolar disease, slower health decline, and increased measures related to cognitive performance (scores on intelligence tests, household income, and years of schooling). These traits are measured in modern industrialized societies, so what phenotypes were adaptive in the past is unclear. We estimate selection coefficients at 9.9 million variants, enabling study of how Darwinian forces couple to allelic effects and shape the genetic architecture of complex traits.

An estimate of fitness reduction from mutation accumulation in a mammal allows assessment of the consequences of relaxed selection

Each generation, spontaneous mutations introduce heritable changes that tend to reduce fitness in populations of highly adapted living organisms. This erosion of fitness is countered by natural selection, which keeps deleterious mutations at low frequencies and ultimately removes most of them from the population. The classical way of studying the impact of spontaneous mutations is via mutation accumulation (MA) experiments, where lines of small effective population size are bred for many generations in conditions where natural selection is largely removed. Such experiments in microbes, invertebrates, and plants have generally demonstrated that fitness decays as a result of MA. However, the phenotypic consequences of MA in vertebrates are largely unknown, because no replicated MA experiment has previously been carried out. This gap in our knowledge is relevant for human populations, where societal changes have reduced the strength of natural selection, potentially allowing deleterious mutations to accumulate. Here, we study the impact of spontaneous MA on the mean and genetic variation for quantitative and fitness-related traits in the house mouse using the MA experimental design, with a cryopreserved control to account for environmental influences. We show that variation for morphological and life history traits accumulates at a sufficiently high rate to maintain genetic variation and selection response. Weight and tail length measures decrease significantly between 0.04% and 0.3% per generation with narrow confidence intervals. Fitness proxy measures (litter size and surviving offspring) decrease on average by about 0.2% per generation, but with confidence intervals overlapping zero. When extrapolated to humans, our results imply that the rate of fitness loss should not be of concern in the foreseeable future.

On the resolution of sexual conflict over shared traits

Anisogamy, different-sized male and female gametes, sits at the heart of sexual selection and conflict between the sexes. Sperm producers (males) and egg producers (females) of the same species generally share most, if not all, of the same genome, but selection frequently favours different trait values in each sex for traits common to both. The extent to which this conflict might be resolved, and the potential mechanisms by which this can occur, have been widely debated. Here, we summarize recent findings and emphasize that once the sexes evolve, sexual selection is ongoing, and therefore new conflict is always possible. In addition, sexual conflict is largely a multivariate problem, involving trait combinations underpinned by networks of interconnected genes. Although these complexities can hinder conflict resolution, they also provide multiple possible routes to decouple male and female phenotypes and permit sex-specific evolution. Finally, we highlight difficulty in the study of sexual conflict over shared traits and promising directions for future research.

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.

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Regularized regression can improve estimates of multivariate selection in the face of multicollinearity and limited data

The breeder's equation, Δ z ¯ = G β   , allows us to understand how genetics (the genetic covariance matrix, G) and the vector of linear selection gradients β interact to generate evolutionary trajectories. Estimation of β using multiple regression of trait values on relative fitness revolutionized the way we study selection in laboratory and wild populations. However, multicollinearity, or correlation of predictors, can lead to very high variances of and covariances between elements of β, posing a challenge for the interpretation of the parameter estimates. This is particularly relevant in the era of big data, where the number of predictors may approach or exceed the number of observations. A common approach to multicollinear predictors is to discard some of them, thereby losing any information that might be gained from those traits. Using simulations, we show how, on the one hand, multicollinearity can result in inaccurate estimates of selection, and, on the other, how the removal of correlated phenotypes from the analyses can provide a misguided view of the targets of selection. We show that regularized regression, which places data-validated constraints on the magnitudes of individual elements of β, can produce more accurate estimates of the total strength and direction of multivariate selection in the presence of multicollinearity and limited data, and often has little cost when multicollinearity is low. We also compare standard and regularized regression estimates of selection in a reanalysis of three published case studies, showing that regularized regression can improve fitness predictions in independent data. Our results suggest that regularized regression is a valuable tool that can be used as an important complement to traditional least-squares estimates of selection. In some cases, its use can lead to improved predictions of individual fitness, and improved estimates of the total strength and direction of multivariate selection.

Survival costs and benefits of reproduction: A register-based study in 20th century Estonia

Patterns of individual variation in lifespan and senescence depend on the associations between parental survival and reproductive rates. We studied the associations between parity and survival among 579,271 Estonians born between 1905 and 1945 and in a cohort with a completed lifespan born in 1905-1927. For this cohort, selection for increased lifespan operated on both sexes, but it was stronger in men than in women. However, the median lifespan increased between the subsequent cohorts in women but stagnated in men. Selection for longer lifespan was caused by the below-average lifespan of individuals with no or single offspring. Despite a general positive selection for lifespan, survival costs of reproduction were also detected among a relatively small proportion of individuals with high parities, as mothers of two and fathers of two and three children had the highest median lifespans. Fathers of more than six children had better survival than fathers of few children in their reproductive age, but this association reversed after age 70. The reversal of this association between survival and parity at old age indicates that relative mortality risks between those with lower versus higher parities change across ages, as predicted by the antagonistic pleiotropy theory of aging.

Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N~408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N~40,466), where genetically less variable individuals (low vPGS) had increased conventional PGS accuracy (by ~19%) than genetically more variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.

Interpreting population- and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding and can also absorb the "indirect" genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect-size estimates are used in polygenic scores (PGSs). We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding.

Unifying approaches from statistical genetics and phylogenetics for mapping phenotypes in structured populations

In both statistical genetics and phylogenetics, a major goal is to identify correlations between genetic loci or other aspects of the phenotype or environment and a focal trait. In these two fields, there are sophisticated but disparate statistical traditions aimed at these tasks. The disconnect between their respective approaches is becoming untenable as questions in medicine, conservation biology, and evolutionary biology increasingly rely on integrating data from within and among species, and once-clear conceptual divisions are becoming increasingly blurred. To help bridge this divide, we derive a general model describing the covariance between the genetic contributions to the quantitative phenotypes of different individuals. Taking this approach shows that standard models in both statistical genetics (e.g., Genome-Wide Association Studies; GWAS) and phylogenetic comparative biology (e.g., phylogenetic regression) can be interpreted as special cases of this more general quantitative-genetic model. The fact that these models share the same core architecture means that we can build a unified understanding of the strengths and limitations of different methods for controlling for genetic structure when testing for associations. We develop intuition for why and when spurious correlations may occur using analytical theory and conduct population-genetic and phylogenetic simulations of quantitative traits. The structural similarity of problems in statistical genetics and phylogenetics enables us to take methodological advances from one field and apply them in the other. We demonstrate this by showing how a standard GWAS technique-including both the genetic relatedness matrix (GRM) as well as its leading eigenvectors, corresponding to the principal components of the genotype matrix, in a regression model-can mitigate spurious correlations in phylogenetic analyses. As a case study of this, we re-examine an analysis testing for co-evolution of expression levels between genes across a fungal phylogeny, and show that including covariance matrix eigenvectors as covariates decreases the false positive rate while simultaneously increasing the true positive rate. More generally, this work provides a foundation for more integrative approaches for understanding the genetic architecture of phenotypes and how evolutionary processes shape it.

2023 ASHG Scientific Achievement Award

This article is based on the address given by the author at the 2023 meeting of The American Society of Human Genetics (ASHG) in Washington, D.C. A video of the original address can be found at the ASHG website.

Polygenic response of sex chromosomes to sexual antagonism

Sexual antagonism occurs when males and females differ in their phenotypic fitness optima but are constrained in their evolution to these optima because of their shared genome. The sex chromosomes, which have distinct evolutionary "interests" relative to the autosomes, are theorized to play an important role in sexually antagonistic conflict. However, the evolutionary responses of sex chromosomes and autosomes have usually been considered independently, that is, via contrasting the response of a gene located on either an X chromosome or an autosome. Here, we study the coevolutionary response of the X chromosome and autosomes to sexually antagonistic selection acting on a polygenic phenotype. We model a phenotype initially under stabilizing selection around a single optimum, followed by a sudden divergence of the male and female optima. We find that, in the absence of dosage compensation, the X chromosome promotes evolution toward the female optimum, inducing coevolutionary male-biased responses on the autosomes. Dosage compensation obscures the female-biased interests of the X, causing it to contribute equally to male and female phenotypic change. We further demonstrate that fluctuations in an adaptive landscape can generate prolonged intragenomic conflict and accentuate the differential responses of the X and autosomes to this conflict.

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.

Problems with studying directional natural selection in humans

The review describes the main methods for assessing directional selection in human populations. These include bioinformatic analysis of DNA sequences via detection of linkage disequilibrium and of deviations from the random distribution of frequencies of genetic variants, demographic and anthropometric studies based on a search for a correlation between fertility and phenotypic traits, genome-wide association studies on fertility along with genetic loci and polygenic risk scores, and a comparison of allele frequencies between generations (in modern samples and in those obtained from burials). Each approach has its limitations and is applicable to different periods in the evolution of Homo sapiens. The main source of error in such studies is thought to be sample stratification, the small number of studies on nonwhite populations, the impossibility of a complete comparison of the associations found and functionally significant causative variants, and the difficulty with taking into account all nongenetic determinants of fertility in contemporary populations. The results obtained by various methods indicate that the direction of human adaptation to new food products has not changed during evolution since the Neolithic; many variants of immunity genes associated with inflammatory and autoimmune diseases in modern populations have undergone positive selection over the past 2-3 thousand years owing to the spread of bacterial and viral infections. For some genetic variants and polygenic traits, an alteration of the direction of natural selection in Europe has been documented, e. g., for those associated with an immune response and cognitive abilities. Examination of the correlation between fertility and educational attainment yields conflicting results. In modern populations, to a greater extent than previously, there is selection for variants of genes responsible for social adaptation and behavioral phenotypes. In particular, several articles have shown a positive correlation of fertility with polygenic risk scores of attention deficit/hyperactivity disorder.

Community-wide genome sequencing reveals 30 years of Darwin's finch evolution

A fundamental goal in evolutionary biology is to understand the genetic architecture of adaptive traits. Using whole-genome data of 3955 of Darwin's finches on the Galápagos Island of Daphne Major, we identified six loci of large effect that explain 45% of the variation in the highly heritable beak size of Geospiza fortis, a key ecological trait. The major locus is a supergene comprising four genes. Abrupt changes in allele frequencies at the loci accompanied a strong change in beak size caused by natural selection during a drought. A gradual change in Geospiza scandens occurred across 30 years as a result of introgressive hybridization with G. fortis. This study shows how a few loci with large effect on a fitness-related trait contribute to the genetic potential for rapid adaptive radiation.

Rethinking (again) Hardy-Weinberg and genetic drift in undergraduate biology

Designing effective curricula is challenging. Content decisions can impact both learning outcomes and student engagement. As an example consider the place of Hardy-Weinberg equilibria (HWE) and genetic drift calculations in introductory biology courses, as discussed by Masel (2012). Given that population genetics, "a fairly arcane speciality", can be difficult to grasp, there is little justification for introducing introductory students to HWE calculations. It is more useful to introduce them to the behavior of alleles in terms of basic features of biological systems, and that in the absence of selection recessive alleles are no "weaker" or preferentially lost from a population than are dominant alleles. On the other hand, stochastic behaviors, such as genetic drift, are ubiquitous in biological systems and often play functionally significant roles; they can be introduced to introductory students in mechanistic and probabilistic terms. Specifically, genetic drift emerges from the stochastic processes involved in meiotic chromosome segregation and recombination. A focus on stochastic processes may help counteract naive bio-deterministic thinking and can reinforce, for students, the value of thinking quantitatively about biological processes.

Variation in ontogenetic trajectories of limb dimensions in humans is attributable to both climatic effects and neutral evolution

Previous studies showed that there is variation in ontogenetic trajectories of human limb dimensions and proportions. However, little is known about the evolutionary significance of this variation. This study used a global sample of modern human immature long bone measurements and a multivariate linear mixed-effects model to study 1) whether the variation in ontogenetic trajectories of limb dimensions is consistent with ecogeographic predictions and 2) the effects of different evolutionary forces on the variation in ontogenetic trajectories. We found that genetic relatedness arising from neutral (nonselective) evolution, allometric variation associated with the change in size, and directional effects from climate all contributed to the variation in ontogenetic trajectories of all major long bone dimensions in modern humans. After accounting for the effects of neutral evolution and holding other effects considered in the current study constant, extreme temperatures have weak, positive associations with diaphyseal length and breadth measurements, while mean temperature shows negative associations with diaphyseal dimensions. The association with extreme temperatures fits the expectations of ecogeographic rules, while the association with mean temperature may explain the observed among-group variation in intralimb indices. The association with climate is present throughout ontogeny, suggesting an explanation of adaptation by natural selection as the most likely cause. On the other hand, genetic relatedness among groups, as structured by neutral evolutionary factors, is an important consideration when interpreting skeletal morphology, even for nonadult individuals.

Interpreting population and family-based genome-wide association studies in the presence of confounding

A central aim of genome-wide association studies (GWASs) is to estimate direct genetic effects: the causal effects on an individual's phenotype of the alleles that they carry. However, estimates of direct effects can be subject to genetic and environmental confounding, and can also absorb the 'indirect' genetic effects of relatives' genotypes. Recently, an important development in controlling for these confounds has been the use of within-family GWASs, which, because of the randomness of Mendelian segregation within pedigrees, are often interpreted as producing unbiased estimates of direct effects. Here, we present a general theoretical analysis of the influence of confounding in standard population-based and within-family GWASs. We show that, contrary to common interpretation, family-based estimates of direct effects can be biased by genetic confounding. In humans, such biases will often be small per-locus, but can be compounded when effect size estimates are used in polygenic scores. We illustrate the influence of genetic confounding on population- and family-based estimates of direct effects using models of assortative mating, population stratification, and stabilizing selection on GWAS traits. We further show how family-based estimates of indirect genetic effects, based on comparisons of parentally transmitted and untransmitted alleles, can suffer substantial genetic confounding. In addition to known biases that can arise in family-based GWASs when interactions between family members are ignored, we show that biases can also arise from gene-by-environment (G×E) interactions when parental genotypes are not distributed identically across interacting environmental and genetic backgrounds. We conclude that, while family-based studies have placed GWAS estimation on a more rigorous footing, they carry subtle issues of interpretation that arise from confounding and interactions.

Phenotypic evolution as an Ornstein-Uhlenbeck process: The effect of environmental variation and phenotypic plasticity

Here we investigate phenotypic evolution from the perspective of the Ornstein-Uhlenbeck (OU) process. Evolutionarily speaking, the model assumes the existence of stabilizing selection toward a phenotypic optimum. The standard (OU) model is modified to include environmental variation by taking a moving phenotypic optimum and endowing organisms with phenotypic plasticity. These two processes lead to an effective fitness landscape, which deforms the original. We observe that the simultaneous occurrence of environmental variation and phenotypic plasticity leads to skewed phenotypic distributions. The skewness of the resulting phenotypic distributions strongly depends on the rate of environmental variation and strength of selection. When generalized to more than one trait, the phenotypic distributions are not only affected by the magnitude of the rate of environmental variation but also by its direction. A remarkable feature of our predictions is the existence of an upper bound for the critical rate of environmental variation to allow population persistence, even if there is no cost associated with phenotypic plasticity.

Polygenic adaptation dynamics in large, finite populations

Although many phenotypic traits are determined by a large number of genetic variants, how a polygenic trait adapts in response to a change in the environment is not completely understood. In the framework of diffusion theory, we study the steady state and the adaptation dynamics of a large but finite population evolving under stabilizing selection and symmetric mutations when selection and mutation are moderately large. We find that in the stationary state, the allele frequency distribution at a locus is unimodal if its effect size is below a threshold effect and bimodal otherwise; these results are the stochastic analog of the deterministic ones where the stable allele frequency becomes bistable when the effect size exceeds a threshold. It is known that following a sudden shift in the phenotypic optimum, in an infinitely large population, selective sweeps at a large-effect locus are prevented and adaptation proceeds exclusively via subtle changes in the allele frequency; in contrast, we find that the chance of sweep is substantially enhanced in large, finite populations and the allele frequency at a large-effect locus can reach a high frequency at short times even for small shifts in the phenotypic optimum.

mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ² statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

Direct and Indirect Roles of Men in Determining Women Decision to Use Laser Procedures for Skin Care

Laser procedures for skin care is becoming increasingly popular and used by the global community and affect many aspects of human life. However, there is very little research on the role of men in women's decision to follow this dermatological procedure. This study aimed to identify the role of men in motivating women to choose laser-based procedure. A systematic literature review research design was applied in this study. A total of 27 papers were obtained by searching PubMed Central for the 2013-2022 editions. The extraction of samples and the motivation of patients or research respondents in choosing laser procedures for cosmetic treatments were conducted to obtain clues about the role of men in making these decisions. Only six studies explicitly reveal the role of men as motivators of women to perform laser-based dermatological procedures. Four of these six studies were conducted in Asia in countries with a high degree of collectivism. The remaining two studies reveal a minor role for men in women's motivation to choose dermatological procedures. Other studies only indirectly implicate the role of men through intrasexual competition, increased self-esteem, and the needs of the world of work. Limitations of the study lies in the non-specificity of the study sample in the female population, laser-based treatments, and the role of men in motivating women. The role of men in motivating women to perform laser-based skin care procedures is stated explicitly in collectivist cultures while only implicitly in individualist cultures. These findings indicate that the strategy of utilizing the added value of men to laser treatment procedures should be directed at the relationship between men and women in real terms in collective cultured countries.

Polygenic signals of sex differences in selection in humans from the UK Biobank

Sex differences in the fitness effects of genetic variants can influence the rate of adaptation and the maintenance of genetic variation. For example, "sexually antagonistic" (SA) variants, which are beneficial for one sex and harmful for the other, can both constrain adaptation and increase genetic variability for fitness components such as survival, fertility, and disease susceptibility. However, detecting variants with sex-differential fitness effects is difficult, requiring genome sequences and fitness measurements from large numbers of individuals. Here, we develop new theory for studying sex-differential selection across a complete life cycle and test our models with genotypic and reproductive success data from approximately 250,000 UK Biobank individuals. We uncover polygenic signals of sex-differential selection affecting survival, reproductive success, and overall fitness, with signals of sex-differential reproductive selection reflecting a combination of SA polymorphisms and sexually concordant polymorphisms in which the strength of selection differs between the sexes. Moreover, these signals hold up to rigorous controls that minimise the contributions of potential confounders, including sequence mapping errors, population structure, and ascertainment bias. Functional analyses reveal that sex-differentiated sites are enriched in phenotype-altering genomic regions, including coding regions and loci affecting a range of quantitative traits. Population genetic analyses show that sex-differentiated sites exhibit evolutionary histories dominated by genetic drift and/or transient balancing selection, but not long-term balancing selection, which is consistent with theoretical predictions of effectively weak SA balancing selection in historically small populations. Overall, our results are consistent with polygenic sex-differential-including SA-selection in humans. Evidence for sex-differential selection is particularly strong for variants affecting reproductive success, in which the potential contributions of nonrandom sampling to signals of sex differentiation can be excluded.

Human Capital Mediates Natural Selection in Contemporary Humans

Natural selection has been documented in contemporary humans, but little is known about the mechanisms behind it. We test for natural selection through the association between 33 polygenic scores and fertility, across two generations, using data from UK Biobank (N = 409,629 British subjects with European ancestry). Consistently over time, polygenic scores that predict higher earnings, education and health also predict lower fertility. Selection effects are concentrated among lower SES groups, younger parents, people with more lifetime sexual partners, and people not living with a partner. The direction of natural selection is reversed among older parents, or after controlling for age at first live birth. These patterns are in line with the economic theory of fertility, in which earnings-increasing human capital may either increase or decrease fertility via income and substitution effects in the labour market. Studying natural selection can help us understand the genetic architecture of health outcomes: we find evidence in modern day Great Britain for multiple natural selection pressures that vary between subgroups in the direction and strength of their effects, that are strongly related to the socio-economic system, and that may contribute to health inequalities across income groups.

Evolutionary genetics: Dissecting a sexually antagonistic polymorphism

Males and females experience divergent selection on many shared traits, which can lead to 'sexual antagonism' - opposing fitness effects of genetic variants in each sex. A new study in the fly Drosophila serrata links sexually antagonistic selection on cuticular hydrocarbons to a single major-effect gene.

Sexually antagonistic selection on educational attainment and body size in Estonian children

Natural selection is a key mechanism of evolution, which results from the differential reproduction of phenotypes. We describe fecundity selection at different parity transitions on 15 anthropometric traits and educational attainment in Estonian children sampled in the middle of 20th century. The direction of selection on educational attainment and bodily traits was sexually antagonistic, and it occurred via different parity transitions in boys and girls. Compared to boys with primary education, obtaining tertiary education was associated with 3.5 times and secondary education two times higher odds of becoming a father. Transition to motherhood was not related to educational attainment, while education above primary was associated with lower odds (OR = 0.5-0.7) to progression to parities above one and two. Selection on anthropometric traits occurred almost exclusively via childlessness in boys, while among the girls, most of the traits that were associated with becoming a mother were additionally associated with a transition from one child to higher parities. Male (but not female) fitness was thus primarily determined by traits related to mating success. Selection favored stronger and larger boys and smaller girls. Selection on girls favored some traits that associate with perceived femininity, while other feminine traits were selected against.

A Double-Track Pathway to Fast Strategy in Humans and Its Personality Correlates

The fast-slow paradigm of life history (LH) focuses on how individuals grow, mate, and reproduce at different paces. This paradigm can contribute substantially to the field of personality and individual differences provided that it is more strictly based on evolutionary biology than it has been so far. Our study tested the existence of a fast-slow continuum underlying indicators of reproductive effort-offspring output, age at first reproduction, number and stability of sexual partners-in 1,043 outpatients with healthy to severely disordered personalities. Two axes emerged reflecting a double-track pathway to fast strategy, based on restricted and unrestricted sociosexual strategies. When rotated, the fast-slow and sociosexuality axes turned out to be independent. Contrary to expectations, neither somatic effort-investment in status, material resources, social capital, and maintenance/survival-was aligned with reproductive effort, nor a clear tradeoff between current and future reproduction was evident. Finally, we examined the association of LH axes with seven high-order personality pathology traits: negative emotionality, impulsivity, antagonism, persistence-compulsivity, subordination, and psychoticism. Persistent and disinhibited subjects appeared as fast-restricted and fast-unrestricted strategists, respectively, whereas asocial subjects were slow strategists. Associations of LH traits with each other and with personality are far more complex than usually assumed in evolutionary psychology.

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'.

[Sex, gender and stature: From biology to culture - and back]

The origin of sexual dimorphism of stature (SSD) in the human species is a subject of debate, likely to have a sociocultural impact. Stature is optimally expressed in good environmental conditions, notably good food, with a strong hereditary determinism. The common academic interpretation, already proposed by Darwin, is that SSD results from sexual selection of stronger males, in most species of mammals, including humans. An alternative hypothesis proposes that it might result from alimentary gender coercion in humans. There is practically no SSD until female growth stops, by ossification of cartilage in the growth plates of long bones, largely under the action of estrogens. The mechanism is the same in males, with a delay due to a lesser and/or later concentration of estrogens. This explanation for SSD has the advantage of being valid for most mammalian species, including those like Pan paniscus where females are dominant. The fitness resulting from high estrogen levels would explain the relatively small stature of women, in spite of obstetric difficulties inversely correlated with height. If patriarchy is involved, it would be by the injunction of fertility rather than by alimentary coercion.

Ancestry-inclusive dog genomics challenges popular breed stereotypes

Behavioral genetics in dogs has focused on modern breeds, which are isolated subgroups with distinctive physical and, purportedly, behavioral characteristics. We interrogated breed stereotypes by surveying owners of 18,385 purebred and mixed-breed dogs and genotyping 2155 dogs. Most behavioral traits are heritable [heritability (h²) > 25%], and admixture patterns in mixed-breed dogs reveal breed propensities. Breed explains just 9% of behavioral variation in individuals. Genome-wide association analyses identify 11 loci that are significantly associated with behavior, and characteristic breed behaviors exhibit genetic complexity. Behavioral loci are not unusually differentiated in breeds, but breed propensities align, albeit weakly, with ancestral function. We propose that behaviors perceived as characteristic of modern breeds derive from thousands of years of polygenic adaptation that predates breed formation, with modern breeds distinguished primarily by aesthetic traits.

Canalization of the Polygenic Risk for Common Diseases and Traits in the UK Biobank Cohort

Since organisms develop and thrive in the face of constant perturbations due to environmental and genetic variation, species may evolve resilient genetic architectures. We sought evidence for this process, known as canalization, through a comparison of the prevalence of phenotypes as a function of the polygenic score (PGS) across environments in the UK Biobank cohort study. Contrasting seven diseases and three categorical phenotypes with respect to 151 exposures in 408,925 people, the deviation between the prevalence-risk curves was observed to increase monotonically with the PGS percentile in one-fifth of the comparisons, suggesting extensive PGS-by-Environment (PGS×E) interaction. After adjustment for the dependency of allelic effect sizes on increased prevalence in the perturbing environment, cases where polygenic influences are greater or lesser than expected are seen to be particularly pervasive for educational attainment, obesity, and metabolic condition type-2 diabetes. Inflammatory bowel disease analysis shows fewer interactions but confirms that smoking and some aspects of diet influence risk. Notably, body mass index has more evidence for decanalization (increased genetic influence at the extremes of polygenic risk), whereas the waist-to-hip ratio shows canalization, reflecting different evolutionary pressures on the architectures of these weight-related traits. An additional 10 % of comparisons showed evidence for an additive shift of prevalence independent of PGS between exposures. These results provide the first widespread evidence for canalization protecting against disease in humans and have implications for personalized medicine as well as understanding the evolution of complex traits. The findings can be explored through an R shiny app at https://canalization-gibsonlab.shinyapps.io/rshiny/.

GWAS on birth year infant mortality rates provides evidence of recent natural selection

Following more than a century of phenotypic measurement of natural selection processes, much recent work explores relationships between molecular genetic measurements and realized fitness in the next generation. We take an innovative approach to the study of contemporary selective pressure by examining which genetic variants are “sustained” in populations as mortality exposure increases. Specifically, we deploy a so-called “regional GWAS” (genome-wide association study) that links the infant mortality rate (IMR) by place and year in the United Kingdom with common genetic variants among birth cohorts in the UK Biobank. These cohorts (born between 1936 and 1970) saw a decline in IMR from above 65 to under 20 deaths per 1,000 live births, with substantial subnational variations and spikes alongside wartime exposures. Our results show several genome-wide significant loci, including LCT and TLR10/1/6, related to area-level cohort IMR exposure during gestation and infancy. Genetic correlations are found across multiple domains, including fertility, cognition, health behaviors, and health outcomes, suggesting an important role for cohort selection in modern populations.

An unbiased test reveals no enrichment of sexually antagonistic polymorphisms on the human X chromosome

Mutations with beneficial effects in one sex can have deleterious effects in the other. Such 'sexually antagonistic' (SA) variants contribute to variation in life-history traits and overall fitness, yet their genomic distribution is poorly resolved. Theory predicts that SA variants could be enriched on the X chromosome or autosomes, yet current empirical tests face two formidable challenges: (i) identifying SA selection in genomic data is difficult; and (ii) metrics of SA variation show persistent biases towards the X, even when SA variants are randomly distributed across the genome. Here, we present an unbiased test of the theory that SA variants are enriched on the X. We first develop models for reproductive FST-a metric for quantifying sex-differential (including SA) effects of genetic variants on lifetime reproductive success-that control for X-linked biases. Comparing data from approximately 250 000 UK Biobank individuals to our models, we find FST elevations consistent with both X-linked and autosomal SA polymorphisms affecting reproductive success in humans. However, the extent of FST elevations does not differ from a model in which SA polymorphisms are randomly distributed across the genome. We argue that the polygenic nature of SA variation, along with sex asymmetries in SA effects, might render X-linked enrichment of SA polymorphisms unlikely.

Is there still evolution in the human population?

It is often claimed that humanity has stopped evolving because modern medicine erased all selection on survival. Even if that would be true, and it is not, there would be other mechanisms of evolution which could still led to changes in allelic frequencies. Here I show, by applying basic evolutionary genetics knowledge, that we expect humanity to evolve. The results from genome sequencing projects have repeatedly affirmed that there are still recent signs of selection in our genomes. I give some examples of such adaptation. Then I briefly discuss what our evolutionary future has in store for us.

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.