Negative selection in humans and fruit flies involves synergistic epistasis

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.

Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction

While respiratory diseases such as COPD and asthma share many risk factors, most studies investigate them in insolation and in predominantly European ancestry populations. Here, we conducted the most powerful multi-trait and -ancestry genetic analysis of respiratory diseases and auxiliary traits to date. Our approach improves the power of genetic discovery across traits and ancestries, identifying 44 novel loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross TRait and Ancestry), a multi-trait and -ancestry polygenic risk score approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD, and lung cancer compared to trait- and ancestry-matched PRS in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. PRSxtra identified individuals in the top decile with over four-fold odds of asthma and COPD compared to the first decile. Our results present a new framework for multi-trait and -ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction.

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Confounding Fuels Misinterpretation in Human Genetics

The scientific literature has seen a resurgence of interest in genetic influences on human behavior and socioeconomic outcomes. Such studies face the central difficulty of distinguishing possible causal influences, in particular genetic and non-genetic ones. When confounding between possible influences is not rigorously addressed, it invites over- and misinterpretation of data. We illustrate the breadth of this problem through a discussion of the literature and a reanalysis of two examples. Clark (2023) suggested that patterns of similarity in social status between relatives indicate that social status is largely determined by one's DNA. We show that the paper's conclusions are based on the conflation of genetic and non-genetic transmission, such as wealth, within families. Song & Zhang (2024) posited that genetic variants underlying bisexual behavior are maintained in the population because they also affect risk-taking behavior, thereby conferring an evolutionary fitness advantage through increased sexual promiscuity. In this case, too, we show that possible explanations cannot be distinguished, but only one is chosen and presented as a conclusion. We discuss how issues of confounding apply more broadly to studies that claim to establish genetic underpinnings to human behavior and societal outcomes.

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.

Stem cell transcriptional profiles from mouse subspecies reveal cis -regulatory evolution at translation genes

A key goal of evolutionary genomics is to harness molecular data to draw inferences about selective forces that have acted on genomes. The field progresses in large part through the development of advanced molecular-evolution analysis methods. Here we explored the intersection between classical sequence-based tests for selection and an empirical expression-based approach, using stem cells from Mus musculus subspecies as a model. Using a test of directional, cis -regulatory evolution across genes in pathways, we discovered a unique program of induction of translation genes in stem cells of the Southeast Asian mouse M. m. castaneus relative to its sister taxa. We then mined population-genomic sequences to pursue underlying regulatory mechanisms for this expression divergence, finding robust evidence for alleles unique to M. m. castaneus at the upstream regions of the translation genes. We interpret our data under a model of changes in lineage-specific pressures across Mus musculus in stem cells with high translational capacity. Our findings underscore the rigor of integrating expression and sequence-based methods to generate hypotheses about evolutionary events from long ago.

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.

Linkage equilibrium between rare mutations

Recombination breaks down genetic linkage by reshuffling existing variants onto new genetic backgrounds. These dynamics are traditionally quantified by examining the correlations between alleles, and how they decay as a function of the recombination rate. However, the magnitudes of these correlations are strongly influenced by other evolutionary forces like natural selection and genetic drift, making it difficult to tease out the effects of recombination. Here we introduce a theoretical framework for analyzing an alternative family of statistics that measure the homoplasy produced by recombination. We derive analytical expressions that predict how these statistics depend on the rates of recombination and recurrent mutation, the strength of negative selection and genetic drift, and the present-day frequencies of the mutant alleles. We find that the degree of homoplasy can strongly depend on this frequency scale, which reflects the underlying timescales over which these mutations occurred. We show how these scaling properties can be used to isolate the effects of recombination, and discuss their implications for the rates of horizontal gene transfer in bacteria.

Background Selection From Unlinked Sites Causes Nonindependent Evolution of Deleterious Mutations

Background selection describes the reduction in neutral diversity caused by selection against deleterious alleles at other loci. It is typically assumed that the purging of deleterious alleles affects linked neutral variants, and indeed simulations typically only treat a genomic window. However, background selection at unlinked loci also depresses neutral diversity. In agreement with previous analytical approximations, in our simulations of a human-like genome with a realistically high genome-wide deleterious mutation rate, the effects of unlinked background selection exceed those of linked background selection. Background selection reduces neutral genetic diversity by a factor that is independent of census population size. Outside of genic regions, the strength of background selection increases with the mean selection coefficient, contradicting the linked theory but in agreement with the unlinked theory. Neutral diversity within genic regions is fairly independent of the strength of selection. Deleterious genetic load among haploid individuals is underdispersed, indicating nonindependent evolution of deleterious mutations. Empirical evidence for underdispersion was previously interpreted as evidence for global epistasis, but we recover it from a non-epistatic model.

Highly parameterized polygenic scores tend to overfit to population stratification via random effects

Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.

Validity of Xiphophorus fish as models for human disease

Platyfish and swordtails of the genus Xiphophorus provide a well-established model for melanoma research and have become well known for this feature. Recently, modelling approaches for other human diseases in Xiphophorus have been developed or are emerging. This Review provides a comprehensive summary of these models and discusses how findings from basic biological and molecular studies and their translation to medical research demonstrate that Xiphophorus models have face, construct and predictive validity for studying a broad array of human diseases. These models can thus improve our understanding of disease mechanisms to benefit patients.

Pervasive selective sweeps across human gut microbiomes

The human gut microbiome is composed of a highly diverse consortia of species which are continually evolving within and across hosts. The ability to identify adaptations common to many host gut microbiomes would not only reveal shared selection pressures across hosts, but also key drivers of functional differentiation of the microbiome that may affect community structure and host traits. However, to date there has not been a systematic scan for adaptations that have spread across host microbiomes. Here, we develop a novel selection scan statistic, named the integrated linkage disequilibrium score (iLDS), that can detect the spread of adaptive haplotypes across host microbiomes via migration and horizontal gene transfer. Specifically, iLDS leverages signals of hitchhiking of deleterious variants with the beneficial variant, a common feature of adaptive evolution. We find that iLDS is capable of detecting simulated and known cases of selection, and moreover is robust to potential confounders that can also elevate LD. Application of the statistic to ~20 common commensal gut species from a large cohort of healthy, Western adults reveals pervasive spread of selected alleles across human microbiomes mediated by horizontal gene transfer. Among the candidate selective sweeps recovered by iLDS is an enrichment for genes involved in the metabolism of maltodextrin, a synthetic starch that has recently become a widespread component of Western diets. In summary, we demonstrate that selective sweeps across host microbiomes are a common feature of the evolution of the human gut microbiome.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC produced robust estimates in simulations across various genetic architectures. We applied LDSPEC to 70 diseases and complex traits from the UK Biobank (average N=306K), meta-analyzing results across diseases/traits. We detected significantly nonzero effect correlations for proximal SNP pairs (e.g., -0.37±0.09 for low-frequency positive-LD 0-100bp SNP pairs) that decayed with distance (e.g., -0.07±0.01 for low-frequency positive-LD 1-10kb), varied with allele frequency (e.g., -0.15±0.04 for common positive-LD 0-100bp), and varied with LD between SNPs (e.g., +0.12±0.05 for common negative-LD 0-100bp) (because we consider derived alleles, positive-LD and negative-LD SNP pairs may yield very different results). We further determined that SNP pairs with shared functions had stronger effect correlations that spanned longer genomic distances, e.g., -0.37±0.08 for low-frequency positive-LD same-gene promoter SNP pairs (average genomic distance of 47kb (due to alternative splicing)) and -0.32±0.04 for low-frequency positive-LD H3K27ac 0-1kb SNP pairs. Consequently, SNP-heritability estimates were substantially smaller than estimates of the sum of causal effect size variances across all SNPs (ratio of 0.87±0.02 across diseases/traits), particularly for certain functional annotations (e.g., 0.78±0.01 for common Super enhancer SNPs)-even though these quantities are widely assumed to be equal. We recapitulated our findings via forward simulations with an evolutionary model involving stabilizing selection, implicating the action of linkage masking, whereby haplotypes containing linked SNPs with opposite effects on disease have reduced effects on fitness and escape negative selection.

Causal interpretations of family GWAS in the presence of heterogeneous effects

Family-based genome-wide association studies (GWAS) have emerged as a gold standard for assessing causal effects of alleles and polygenic scores. Notably, family studies are often claimed to provide an unbiased estimate of the average causal effect (or average treatment effect; ATE) of an allele, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. Here, we show that this interpretation does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. Consequently, if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in LD patterns, family studies provide a biased estimate of the average effect in the sample. At a single locus, family-based association studies can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores, however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate for any subset or weighted average of families. Instead, family-based studies can be reinterpreted as enabling an unbiased estimate of the extent to which Mendelian segregation at loci in the PGS contributes to the population-level variance in the trait. Because this estimate does not include the between-family variance, however, this interpretation applies to only (roughly) half of the sample PGS variance. In practice, the potential biases of a family-based GWAS are likely smaller than those arising from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, the causal interpretation of family-based GWAS estimates is less straightforward than has been widely appreciated.

Mendelian randomization analyses clarify the effects of height on cardiovascular diseases

An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested causal association. However, the extent to which the effect estimated by MR can be explained by established cardiovascular risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized a well-powered set of genetic instruments for human stature, comprising >1,800 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 0.02). However, multivariable analyses demonstrated independent effects of height on other cardiovascular traits beyond CAD, consistent with epidemiologic associations and univariable MR experiments. In contrast with published reports, we observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest the impact of height on CAD risk beyond previously established cardiovascular risk factors is minimal and not explained by lung function measures.

Interchromosomal linkage disequilibrium and linked fitness cost loci associated with selection for herbicide resistance

The adaptation of weeds to herbicide is both a significant problem in agriculture and a model of rapid adaptation. However, significant gaps remain in our knowledge of resistance controlled by many loci and the evolutionary factors that influence the maintenance of resistance. Here, using herbicide-resistant populations of the common morning glory (Ipomoea purpurea), we perform a multilevel analysis of the genome and transcriptome to uncover putative loci involved in nontarget-site herbicide resistance (NTSR) and to examine evolutionary forces underlying the maintenance of resistance in natural populations. We found loci involved in herbicide detoxification and stress sensing to be under selection and confirmed that detoxification is responsible for glyphosate (RoundUp) resistance using a functional assay. We identified interchromosomal linkage disequilibrium (ILD) among loci under selection reflecting either historical processes or additive effects leading to the resistance phenotype. We further identified potential fitness cost loci that were strongly linked to resistance alleles, indicating the role of genetic hitchhiking in maintaining the cost. Overall, our work suggests that NTSR glyphosate resistance in I. purpurea is conferred by multiple genes which are potentially maintained through generations via ILD, and that the fitness cost associated with resistance in this species is likely a by-product of genetic hitchhiking.

Deleterious mutation load in the admixed mice population

Deleterious mutation loads are known to correlate negatively with effective population size (Ne). Due to this reason, previous studies observed a higher proportion of harmful mutations in small populations than that in large populations. However, the mutational load in an admixed population that derived from introgression between individuals from two populations with vastly different Ne is not known. We investigated this using the whole genome data from two subspecies of the mouse (Mus musculus castaneus and Mus musculus musculus) with significantly different Ne. We used the ratio of diversities at nonsynonymous and synonymous sites (dN/dS) to measure the harmful mutation load. Our results showed that this ratio observed for the admixed population was intermediate between those of the parental populations. The dN/dS ratio of the hybrid population was significantly higher than that of M. m. castaneus but lower than that of M. m. musculus. Our analysis revealed a significant positive correlation between the proportion of M. m. musculus ancestry in admixed individuals and their dN/dS ratio. This suggests that the admixed individuals with high proportions of M. m. musculus ancestry have large dN/dS ratios. We also used the proportion of deleterious nonsynonymous SNVs as a proxy for deleterious mutation load, which also produced similar results. The observed results were in concordance with those expected by theory. We also show a shift in the distribution of fitness effects of nonsynonymous SNVs in the admixed genomes compared to the parental populations. These findings suggest that the deleterious mutation load of the admixed population is determined by the proportion of the ancestries of the subspecies. Therefore, it is important to consider the status and the level of genetic admixture of the populations whilst estimating the mutation loads.

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.

Detection of Selection Signatures in Anqing Six-End-White Pigs Based on Resequencing Data

As a distinguished Chinese indigenous pig breed that exhibits disease resistance and high meat quality, the Anqing six-end-white (AQ) pig represents a valuable germplasm resource for improving the quality of the pig breeding industry. In this study, 24 AQ pigs that were distantly blood-related and 6 Asian Wild Boar (AWB) were selected for 10× deep-genome resequencing. The signatures of the selection were analyzed to explore the genetic basis of their germplasm characteristics and to identify excellent germplasm-related functional genes based on NGS data. A total of 49,289,052 SNPs and 6,186,123 indels were detected across the genome in 30 pigs. Most of the genetic variations were synonym mutations and existed in the intergenic region. We identified 275 selected regions (top 1%) harboring 85 genes by applying a crossover approach based on genetic differentiation (F_ST) and polymorphism levels (π ratio). Some genes were found to be positively selected in AQ pigs' breeding. The SMPD4 and DDX18 genes were involved in the immune response to pseudorabies virus (PRV) and porcine reproductive and respiratory syndrome virus (PRRSV). The BCL6 and P2RX6 genes were involved in biological regulation of immune T cells and phagocytes. The SLC7A4 and SPACA4 genes were related to reproductive performance. The MSTN and HIF1A genes were related to fat deposition and muscle development. Moreover, 138 overlapping regions were detected in selected regions and ROH islands of AQ pigs. Additionally, we found that the QTLs with the most overlapping regions were related to back fat thickness, meat color, pH value, fatty acid content, immune cells, parasitic immunity, and bacterial immunity. Based on functional enrichment analysis and QTLs mapping, we conducted further research on the molecular genetic basis of germplasm traits (disease resistance and excellent meat quality). These results are a reliable resource for conserving germplasm resources and exploiting molecular markers of AQ pigs.

Octopaminergic/tyraminergic Tdc2 neurons regulate biased sperm usage in female Drosophila melanogaster

In polyandrous internally fertilizing species, a multiply-mated female can use stored sperm from different males in a biased manner to fertilize her eggs. The female's ability to assess sperm quality and compatibility is essential for her reproductive success, and represents an important aspect of postcopulatory sexual selection. In Drosophila melanogaster, previous studies demonstrated that the female nervous system plays an active role in influencing progeny paternity proportion, and suggested a role for octopaminergic/tyraminergic Tdc2 neurons in this process. Here, we report that inhibiting Tdc2 neuronal activity causes females to produce a higher-than-normal proportion of first-male progeny. This difference is not due to differences in sperm storage or release, but instead is attributable to the suppression of second-male sperm usage bias that normally occurs in control females. We further show that a subset of Tdc2 neurons innervating the female reproductive tract is largely responsible for the progeny proportion phenotype that is observed when Tdc2 neurons are inhibited globally. On the contrary, overactivation of Tdc2 neurons does not further affect sperm storage and release or progeny proportion. These results suggest that octopaminergic/tyraminergic signaling allows a multiply-mated female to bias sperm usage, and identify a new role for the female nervous system in postcopulatory sexual selection.

Complex fitness landscape shapes variation in a hyperpolymorphic species

It is natural to assume that patterns of genetic variation in hyperpolymorphic species can reveal large-scale properties of the fitness landscape that are hard to detect by studying species with ordinary levels of genetic variation. Here, we study such patterns in a fungus Schizophyllum commune, the most polymorphic species known. Throughout the genome, short-range linkage disequilibrium (LD) caused by attraction of minor alleles is higher between pairs of nonsynonymous than of synonymous variants. This effect is especially pronounced for pairs of sites that are located within the same gene, especially if a large fraction of the gene is covered by haploblocks, genome segments where the gene pool consists of two highly divergent haplotypes, which is a signature of balancing selection. Haploblocks are usually shorter than 1000 nucleotides, and collectively cover about 10% of the S. commune genome. LD tends to be substantially higher for pairs of nonsynonymous variants encoding amino acids that interact within the protein. There is a substantial correlation between LDs at the same pairs of nonsynonymous mutations in the USA and the Russian populations. These patterns indicate that selection in S. commune involves positive epistasis due to compensatory interactions between nonsynonymous alleles. When less polymorphic species are studied, analogous patterns can be detected only through interspecific comparisons.

Changes to DNA known as mutations may alter how the proteins and other components of a cell work, and thus play an important role in allowing living things to evolve new traits and abilities over many generations. Whether a mutation is beneficial or harmful may differ depending on the genetic background of the individual – that is, depending on other mutations present in other positions within the same gene – due to a phenomenon called epistasis.

Epistasis is known to affect how various species accumulate differences in their DNA compared to each other over time. For example, a mutation that is rare in humans and known to cause disease may be widespread in other primates because its negative effect is canceled out by another mutation that is standard for these species but absent in humans. However, it remains unclear whether epistasis plays a significant part in shaping genetic differences between individuals of the same species.

A type of fungus known as Schizophyllum commune lives on rotting wood and is found across the world. It is one of the most genetically diverse species currently known, so there is a higher chance of pairs of compensatory mutations occurring and persisting for a long time in S. commune than in most other species, providing a unique opportunity to study epistasis.

Here, Stolyarova et al. studied two distinct populations of S. commune, one from the USA and one from Russia. The team found that – unlike in humans, flies and other less genetically diverse species – epistasis maintains combinations of mutations in S. commune that individually would be harmful to the fungus but together compensate for each other. For example, pairs of mutations affecting specific molecules known as amino acids – the building blocks of proteins – that physically interact with each other tended to be found together in the same individuals.

One potential downside of having pairs of compensatory mutations in the genome is that when the organism reproduces, the process of making sex cells may split up these pairs so that harmful mutations are inherited without their partner mutations. Thus, epistasis may have helped shape the way S. commune and other genetically diverse species have evolved.

Synergistic epistasis of the deleterious effects of transposable elements

The replicative nature and generally deleterious effects of transposable elements (TEs) raise an outstanding question about how TE copy number is stably contained in host populations. Classic theoretical analyses predict that, when the decline in fitness due to each additional TE insertion is greater than linear, or when there is synergistic epistasis, selection against TEs can result in a stable equilibrium of TE copy number. While several mechanisms are predicted to yield synergistic deleterious effects of TEs, we lack empirical investigations of the presence of such epistatic interactions. Purifying selection with synergistic epistasis generates repulsion linkage between deleterious alleles. We investigated this population genetic signal in the likely ancestral Drosophila melanogaster population and found evidence supporting the presence of synergistic epistasis among TE insertions, especially TEs expected to exert large fitness impacts. Even though synergistic epistasis of TEs has been predicted to arise through ectopic recombination and TE-mediated epigenetic silencing mechanisms, we only found mixed support for the associated predictions. We observed signals of synergistic epistasis for a large number of TE families, which is consistent with the expectation that such epistatic interaction mainly happens among copies of the same family. Curiously, significant repulsion linkage was also found among TE insertions from different families, suggesting the possibility that synergism of TEs' deleterious fitness effects could arise above the family level and through mechanisms similar to those of simple mutations. Our findings set the stage for investigating the prevalence and importance of epistatic interactions in the evolutionary dynamics of TEs.

Linkage disequilibrium between rare mutations

The statistical associations between mutations, collectively known as linkage disequilibrium, encode important information about the evolutionary forces acting within a population. Yet in contrast to single-site analogues like the site frequency spectrum, our theoretical understanding of linkage disequilibrium remains limited. In particular, little is currently known about how mutations with different ages and fitness costs contribute to expected patterns of linkage disequilibrium, even in simple settings where recombination and genetic drift are the major evolutionary forces. Here, I introduce a forward-time framework for predicting linkage disequilibrium between pairs of neutral and deleterious mutations as a function of their present-day frequencies. I show that the dynamics of linkage disequilibrium become much simpler in the limit that mutations are rare, where they admit a simple heuristic picture based on the trajectories of the underlying lineages. I use this approach to derive analytical expressions for a family of frequency-weighted linkage disequilibrium statistics as a function of the recombination rate, the frequency scale, and the additive and epistatic fitness costs of the mutations. I find that the frequency scale can have a dramatic impact on the shapes of the resulting linkage disequilibrium curves, reflecting the broad range of time scales over which these correlations arise. I also show that the differences between neutral and deleterious linkage disequilibrium are not purely driven by differences in their mutation frequencies and can instead display qualitative features that are reminiscent of epistasis. I conclude by discussing the implications of these results for recent linkage disequilibrium measurements in bacteria. This forward-time approach may provide a useful framework for predicting linkage disequilibrium across a range of evolutionary scenarios.

Negative linkage disequilibrium between amino acid changing variants reveals interference among deleterious mutations in the human genome

Evolutionary forces like Hill-Robertson interference and negative epistasis can lead to deleterious mutations being found on distinct haplotypes. However, the extent to which these forces depend on the selection and dominance coefficients of deleterious mutations and shape genome-wide patterns of linkage disequilibrium (LD) in natural populations with complex demographic histories has not been tested. In this study, we first used forward-in-time simulations to predict how negative selection impacts LD. Under models where deleterious mutations have additive effects on fitness, deleterious variants less than 10 kb apart tend to be carried on different haplotypes relative to pairs of synonymous SNPs. In contrast, for recessive mutations, there is no consistent ordering of how selection coefficients affect LD decay, due to the complex interplay of different evolutionary effects. We then examined empirical data of modern humans from the 1000 Genomes Project. LD between derived alleles at nonsynonymous SNPs is lower compared to pairs of derived synonymous variants, suggesting that nonsynonymous derived alleles tend to occur on different haplotypes more than synonymous variants. This result holds when controlling for potential confounding factors by matching SNPs for frequency in the sample (allele count), physical distance, magnitude of background selection, and genetic distance between pairs of variants. Lastly, we introduce a new statistic HR(j) which allows us to detect interference using unphased genotypes. Application of this approach to high-coverage human genome sequences confirms our finding that nonsynonymous derived alleles tend to be located on different haplotypes more often than are synonymous derived alleles. Our findings suggest that interference may play a pervasive role in shaping patterns of LD between deleterious variants in the human genome, and consequently influences genome-wide patterns of LD.

Patterns and Causes of Signed Linkage Disequilibria in Flies and Plants

Most empirical studies of linkage disequilibrium (LD) study its magnitude, ignoring its sign. Here, we examine patterns of signed LD in two population genomic data sets, one from Capsella grandiflora and one from Drosophila melanogaster. We consider how processes such as drift, admixture, Hill–Robertson interference, and epistasis may contribute to these patterns. We report that most types of mutations exhibit positive LD, particularly, if they are predicted to be less deleterious. We show with simulations that this pattern arises easily in a model of admixture or distance-biased mating, and that genome-wide differences across site types are generally expected due to differences in the strength of purifying selection even in the absence of epistasis. We further explore how signed LD decays on a finer scale, showing that loss of function mutations exhibit particularly positive LD across short distances, a pattern consistent with intragenic antagonistic epistasis. Controlling for genomic distance, signed LD in C. grandiflora decays faster within genes, compared with between genes, likely a by-product of frequent recombination in gene promoters known to occur in plant genomes. Finally, we use information from published biological networks to explore whether there is evidence for negative synergistic epistasis between interacting radical missense mutations. In D. melanogaster networks, we find a modest but significant enrichment of negative LD, consistent with the possibility of intranetwork negative synergistic epistasis.

The inflated significance of neutral genetic diversity in conservation genetics

The current rate of species extinction is rapidly approaching unprecedented highs, and life on Earth presently faces a sixth mass extinction event driven by anthropogenic activity, climate change, and ecological collapse. The field of conservation genetics aims at preserving species by using their levels of genetic diversity, usually measured as neutral genome-wide diversity, as a barometer for evaluating population health and extinction risk. A fundamental assumption is that higher levels of genetic diversity lead to an increase in fitness and long-term survival of a species. Here, we argue against the perceived importance of neutral genetic diversity for the conservation of wild populations and species. We demonstrate that no simple general relationship exists between neutral genetic diversity and the risk of species extinction. Instead, a better understanding of the properties of functional genetic diversity, demographic history, and ecological relationships is necessary for developing and implementing effective conservation genetic strategies.

Emergence and propagation of epistasis in metabolic networks

Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.

Episodic evolution of coadapted sets of amino acid sites in mitochondrial proteins

The rate of evolution differs between protein sites and changes with time. However, the link between these two phenomena remains poorly understood. Here, we design a phylogenetic approach for distinguishing pairs of amino acid sites that evolve concordantly, i.e., such that substitutions at one site trigger subsequent substitutions at the other; and also pairs of sites that evolve discordantly, so that substitutions at one site impede subsequent substitutions at the other. We distinguish groups of amino acid sites that undergo coordinated evolution and evolve discordantly from other such groups. In mitochondrion-encoded proteins of metazoans and fungi, we show that concordantly evolving sites are clustered in protein structures. By analysing the phylogenetic patterns of substitutions at concordantly and discordantly evolving site pairs, we find that concordant evolution has two distinct causes: epistatic interactions between amino acid substitutions and episodes of selection independently affecting substitutions at different sites. The rate of substitutions at concordantly evolving groups of protein sites changes in the course of evolution, indicating episodes of selection limited to some of the lineages. The phylogenetic positions of these changes are consistent between proteins, suggesting common selective forces underlying them.

The mode and rate of evolution of a protein site depends on the effect of its mutations on protein fitness. The fitness effect of a mutation itself can change in the course of evolution for at least two reasons. First, it can be modulated by substitutions occurring at other sites, a phenomenon called epistasis. Second, changes in selection can be non-epistatic, affecting sites independently of one another. Here, we analyse substitutions accumulated by the evolving lineages of the five proteins encoded by the mitochondrial genomes of thousands of species of metazoans and fungi. We show that substitutions at different amino acid sites occur in a coordinated fashion, and this coordination is caused both by epistasis and by episodes of selection affecting groups of sites. We partition each protein into several groups of concordantly evolving sites such that evolution of sites from different groups is discordant, and show that the proteins encoded by the mitochondrial genome consist of coevolving structural blocks. Some of these blocks have a clear functional specialization, e.g. are associated with interfaces between proteins composing respiratory complexes. Together, our results reveal a previously unrecognized complexity in the causes of variation in evolutionary rates between protein sites.

Epistatic evidence for gender-dependant slow neurotransmission signalling in substance use disorders: PPP1R12B versus PPP1R1B

BACKGROUND

Slow neurotransmission including DARPP-32 signalling is implicated in substance use disorders (SUDs) by experimental systems but not yet in the human aetiology. PPP1R12B, encoding another protein in the DARPP-32 family, hasn't been studied in the brain.

METHODS

Brain-regional gene activity was assessed in three different animal models of SUDs for mRNA level alterations. Genetic associations were assessed by meta-analysis of pre-existing dbGaP GWAS datasets for main effects and epistasis with known genetic risks, followed by cell type-specific pathway delineation. Parkinson's disease (PD) was included as a dopamine-related disease control for SUDs.

FINDINGS

In animal models of SUDs, environmentally-altered PPP1R12B expression sex-dependently involves motivation-related brain regions. In humans with polysubstance abuse, meta-analysis of pre-existing datasets revealed that PPP1R12B and PPP1R1B, although expressed in dopamine vs. dopamine-recipient neurons, exerted similar interactions with known genetic risks such as ACTR1B and DRD2 in men but with ADH1B, HGFAC and DRD3 in women. These interactions reached genome-wide significances (P_meta<10^-20) for SUDs but not for PD (disease selectivity: P = 4.8 × 10^-142, OR = 6.7 for PPP1R12B; P = 8.0 × 10^-8, OR = 2.1 for PPP1R1B). CADM2 was the common risk in the molecular signalling regardless of gender and cell type.

INTERPRETATION

Gender-dependant slow neurotransmission may convey both genetic and environmental vulnerabilities selectively to SUDs.

FUNDING

Grants from National Institute on Drug Abuse (NIDA) and National Institute on Alcohol Abuse and Alcoholism (NIAAA) of U.S.A. and National Natural Science Foundation of China (NSFC).

Patterns of Aging Biomarkers, Mortality, and Damaging Mutations Illuminate the Beginning of Aging and Causes of Early-Life Mortality

An increase in the probability of death has been a defining feature of aging, yet human perinatal mortality starts high and decreases with age. Previous evolutionary models suggested that organismal aging begins after the onset of reproduction. However, we find that mortality and incidence of diseases associated with aging follow a U-shaped curve with the minimum before puberty, whereas quantitative biomarkers of aging, including somatic mutations and DNA methylation, do not, revealing that aging starts early but is masked by early-life mortality. Moreover, our genetic analyses point to the contribution of damaging mutations to early mortality. We propose that mortality patterns are governed, in part, by negative selection against damaging mutations in early life, manifesting after the corresponding genes are first expressed. Deconvolution of mortality patterns suggests that deleterious changes rather than mortality are the defining characteristic of aging and that aging begins in very early life.

Differences in local population history at the finest level: the case of the Estonian population

Several recent studies detected fine-scale genetic structure in human populations. Hence, groups conventionally treated as single populations harbour significant variation in terms of allele frequencies and patterns of haplotype sharing. It has been shown that these findings should be considered when performing studies of genetic associations and natural selection, especially when dealing with polygenic phenotypes. However, there is little understanding of the practical effects of such genetic structure on demography reconstructions and selection scans when focusing on recent population history. Here we tested the impact of population structure on such inferences using high-coverage (~30×) genome sequences of 2305 Estonians. We show that different regions of Estonia differ in both effective population size dynamics and signatures of natural selection. By analyzing identity-by-descent segments we also reveal that some Estonian regions exhibit evidence of a bottleneck 10-15 generations ago reflecting sequential episodes of wars, plague and famine, although this signal is virtually undetected when treating Estonia as a single population. Besides that, we provide a framework for relating effective population size estimated from genetic data to actual census size and validate it on the Estonian population. This approach may be widely used both to cross-check estimates based on historical sources as well as to get insight into times and/or regions with no other information available. Our results suggest that the history of human populations within the last few millennia can be highly region specific and cannot be properly studied without taking local genetic structure into account.

Double-edged sword: The evolutionary consequences of the epigenetic silencing of transposable elements

Transposable elements (TEs) are genomic parasites that selfishly replicate at the expense of host fitness. Fifty years of evolutionary studies of TEs have concentrated on the deleterious genetic effects of TEs, such as their effects on disrupting genes and regulatory sequences. However, a flurry of recent work suggests that there is another important source of TEs' harmful effects-epigenetic silencing. Host genomes typically silence TEs by the deposition of repressive epigenetic marks. While this silencing reduces the selfish replication of TEs and should benefit hosts, a picture is emerging that the epigenetic silencing of TEs triggers inadvertent spreading of repressive marks to otherwise expressed neighboring genes, ultimately jeopardizing host fitness. In this Review, we provide a long-overdue overview of the recent genome-wide evidence for the presence and prevalence of TEs' epigenetic effects, highlighting both the similarities and differences across mammals, insects, and plants. We lay out the current understanding of the functional and fitness consequences of TEs' epigenetic effects, and propose possible influences of such effects on the evolution of both hosts and TEs themselves. These unique evolutionary consequences indicate that TEs' epigenetic effect is not only a crucial component of TE biology but could also be a significant contributor to genome function and evolution.

The evolution of zebrafish RAG2 protein is required for adapting to the elevated body temperature of the higher endothermic vertebrates

The recombination activating gene (RAG or RAG1/RAG2 complex)-mediated adaptive immune system is a hallmark of jawed vertebrates. It has been reported that RAG originated in invertebrates. However, whether RAG further evolved once it arose in jawed vertebrates remains largely unknown. Here, we found that zebrafish RAG (zRAG) had a lower activity than mouse RAG (mRAG). Intriguingly, the attenuated stability of zebrafish RAG2 (zRAG2), but not zebrafish RAG1, caused the reduced V(D)J recombination efficiency compared to mRAG at 37 °C which are the body temperature of most endotherms except birds. Importantly, the lower temperature 28 °C, which is the best temperature for zebrafish growth, made the recombination efficiency of zRAG similar to that of mRAG by improving the stability of zRAG2. Consistent with the prementioned observation, the V(D)J recombination of Rag2^KI/KI mice, which zRAG2 was substituted for mRAG2, was also severely impaired. Unexpectedly, Rag2^KI/KI mice developed cachexia syndromes accompanied by premature death. Taken together, our findings illustrate that the evolution of zebrafish RAG2 protein is required for adapting to the elevated body temperature of the higher endothermic vertebrates.

Fine-Scale Haplotype Structure Reveals Strong Signatures of Positive Selection in a Recombining Bacterial Pathogen

Identifying genetic variation in bacteria that has been shaped by ecological differences remains an important challenge. For recombining bacteria, the sign and strength of linkage provide a unique lens into ongoing selection. We show that derived alleles <300 bp apart in Neisseria gonorrhoeae exhibit more coupling linkage than repulsion linkage, a pattern that cannot be explained by limited recombination or neutrality as these couplings are significantly stronger for nonsynonymous alleles than synonymous alleles. This general pattern is driven by a small fraction of highly diverse genes, many of which exhibit evidence of interspecies horizontal gene transfer and an excess of intermediate frequency alleles. Extensive simulations show that two distinct forms of positive selection can create these patterns of genetic variation: directional selection on horizontally transferred alleles or balancing selection that maintains distinct haplotypes in the presence of recombination. Our results establish a framework for identifying patterns of selection in fine-scale haplotype structure that indicate specific ecological processes in species that recombine with distantly related lineages or possess coexisting adaptive haplotypes.

Accelerated inbreeding depression suggests synergistic epistasis for deleterious mutations in Drosophila melanogaster

Epistasis may have important consequences for a number of issues in quantitative genetics and evolutionary biology. In particular, synergistic epistasis for deleterious alleles is relevant to the mutation load paradox and the evolution of sex and recombination. Some studies have shown evidence of synergistic epistasis for spontaneous or induced deleterious mutations appearing in mutation-accumulation experiments. However, many newly arising mutations may not actually be segregating in natural populations because of the erasing action of natural selection. A demonstration of synergistic epistasis for naturally segregating alleles can be achieved by means of inbreeding depression studies, as deleterious recessive allelic effects are exposed in inbred lines. Nevertheless, evidence of epistasis from these studies is scarce and controversial. In this paper, we report the results of two independent inbreeding experiments carried out with two different populations of Drosophila melanogaster. The results show a consistent accelerated inbreeding depression for fitness, suggesting synergistic epistasis among deleterious alleles. We also performed computer simulations assuming different possible models of epistasis and mutational parameters for fitness, finding some of them to be compatible with the results observed. Our results suggest that synergistic epistasis for deleterious mutations not only occurs among newly arisen spontaneous or induced mutations, but also among segregating alleles in natural populations.

Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

A suite of automated sequence analyses reduces the number of candidate deleterious variants and reveals a difference between probands and unaffected siblings

PURPOSE

Develop an automated exome analysis workflow that can produce a very small number of candidate variants yet still detect different numbers of deleterious variants between probands and unaffected siblings.

METHODS

Ninety-seven outbred nuclear families from the Undiagnosed Diseases Program/Network included single probands and the corresponding unaffected sibling(s). Single-nucleotide polymorphism (SNP) chip and exome analyses were performed on all, with proband and unaffected sibling considered independently as the target. The total burden of candidate genetic variants was summed for probands and siblings over all considered disease models.

RESULTS

Exome analysis workflow include automated programs for ethnicity-matched genotype calling, salvage pathway for Mendelian inconsistency, compound heterozygous recessive detection, BAM file regional curation, population frequency filtering, pedigree-aware BAM file noise evaluation, and exon deletion filtration. This workflow relied heavily on BAM file analysis. A greater average pathogenic variant number was found compared with unaffected siblings. This was significant (p < 0.05) when using published recommended thresholds, and implies that causal variants are retained in many probands' lists.

CONCLUSION

Using Mendelian and non-Mendelian models, this agnostic exome analysis shows a difference between a small group of probands and their unaffected siblings. This workflow produces candidate lists small enough to pursue with laboratory validation.

Directional Selection Rather Than Functional Constraints Can Shape the G Matrix in Rapidly Adapting Asexuals

Genetic covariances represent a combination of pleiotropy and linkage disequilibrium, shaped by the population's history. Observed genetic covariance is most often interpreted in pleiotropic terms. In particular, functional constraints restricting which phenotypes are physically possible can lead to a stable G matrix with high genetic variance in fitness-associated traits, and high pleiotropic negative covariance along the phenotypic curve of constraint. In contrast, population genetic models of relative fitness assume endless adaptation without constraint, through a series of selective sweeps that are well described by recent traveling wave models. We describe the implications of such population genetic models for the G matrix when pleiotropy is excluded by design, such that all covariance comes from linkage disequilibrium. The G matrix is far less stable than has previously been found, fluctuating over the timescale of selective sweeps. However, its orientation is relatively stable, corresponding to high genetic variance in fitness-associated traits and strong negative covariance-the same pattern often interpreted in terms of pleiotropic constraints but caused instead by linkage disequilibrium. We find that different mechanisms drive the instabilities along vs. perpendicular to the fitness gradient. The origin of linkage disequilibrium is not drift, but small amounts of linkage disequilibrium are instead introduced by mutation and then amplified during competing selective sweeps. This illustrates the need to integrate a broader range of population genetic phenomena into quantitative genetics.

Turning Vice into Virtue: Using Batch-Effects to Detect Errors in Large Genomic Data Sets

It is often unavoidable to combine data from different sequencing centers or sequencing platforms when compiling data sets with a large number of individuals. However, the different data are likely to contain specific systematic errors that will appear as SNPs. Here, we devise a method to detect systematic errors in combined data sets. To measure quality differences between individual genomes, we study pairs of variants that reside on different chromosomes and co-occur in individuals. The abundance of these pairs of variants in different genomes is then used to detect systematic errors due to batch effects. Applying our method to the 1000 Genomes data set, we find that coding regions are enriched for errors, where ∼1% of the higher frequency variants are predicted to be erroneous, whereas errors outside of coding regions are much rarer (<0.001%). As expected, predicted errors are found less often than other variants in a data set that was generated with a different sequencing technology, indicating that many of the candidates are indeed errors. However, predicted 1000 Genomes errors are also found in other large data sets; our observation is thus not specific to the 1000 Genomes data set. Our results show that batch effects can be turned into a virtue by using the resulting variation in large scale data sets to detect systematic errors.

The geometry of partial fitness orders and an efficient method for detecting genetic interactions

We present an efficient computational approach for detecting genetic interactions from fitness comparison data together with a geometric interpretation using polyhedral cones associated to partial orderings. Genetic interactions are defined by linear forms with integer coefficients in the fitness variables assigned to genotypes. These forms generalize several popular approaches to study interactions, including Fourier–Walsh coefficients, interaction coordinates, and circuits. We assume that fitness measurements come with high uncertainty or are even unavailable, as is the case for many empirical studies, and derive interactions only from comparisons of genotypes with respect to their fitness, i.e. from partial fitness orders. We present a characterization of the class of partial fitness orders that imply interactions, using a graph-theoretic approach. Our characterization then yields an efficient algorithm for testing the condition when certain genetic interactions, such as sign epistasis, are implied. This provides an exponential improvement of the best previously known method. We also present a geometric interpretation of our characterization, which provides the basis for statistical analysis of partial fitness orders and genetic interactions.

A survey of functional genomic variation in domesticated chickens

BACKGROUND

Deleterious genetic variation can increase in frequency as a result of mutations, genetic drift, and genetic hitchhiking. Although individual effects are often small, the cumulative effect of deleterious genetic variation can impact population fitness substantially. In this study, we examined the genome of commercial purebred chicken lines for deleterious and functional variations, combining genotype and whole-genome sequence data.

RESULTS

We analysed over 22,000 animals that were genotyped on a 60 K SNP chip from four purebred lines (two white egg and two brown egg layer lines) and two crossbred lines. We identified 79 haplotypes that showed a significant deficit in homozygous carriers. This deficit was assumed to stem from haplotypes that potentially harbour lethal recessive variations. To identify potentially deleterious mutations, a catalogue of over 10 million variants was derived from 250 whole-genome sequenced animals from three purebred white-egg layer lines. Out of 4219 putative deleterious variants, 152 mutations were identified that likely induce embryonic lethality in the homozygous state. Inferred deleterious variation showed evidence of purifying selection and deleterious alleles were generally overrepresented in regions of low recombination. Finally, we found evidence that mutations, which were inferred to be evolutionally intolerant, likely have positive effects in commercial chicken populations.

CONCLUSIONS

We present a comprehensive genomic perspective on deleterious and functional genetic variation in egg layer breeding lines, which are under intensive selection and characterized by a small effective population size. We show that deleterious variation is subject to purifying selection and that there is a positive relationship between recombination rate and purging efficiency. In addition, multiple putative functional coding variants were discovered in selective sweep regions, which are likely under positive selection. Together, this study provides a unique molecular perspective on functional and deleterious variation in commercial egg-laying chickens, which can enhance current genomic breeding practices to lower the frequency of undesirable variants in the population.

Genetic constraints on adaptation: a theoretical primer for the genomics era

Genetic constraints are features of inheritance systems that slow or prohibit adaptation. Several population genetic mechanisms of constraint have received sustained attention within the field since they were first articulated in the early 20th century. This attention is now reflected in a rich, and still growing, theoretical literature on the genetic limits to adaptive change. In turn, empirical research on constraints has seen a rapid expansion over the last two decades in response to changing interests of evolutionary biologists, along with new technologies, expanding data sets, and creative analytical approaches that blend mathematical modeling with genomics. Indeed, one of the most notable and exciting features of recent progress in genetic constraints is the close connection between theoretical and empirical research. In this review, we discuss five major population genetic contexts of genetic constraint: genetic dominance, pleiotropy, fitness trade-offs between types of individuals of a population, sign epistasis, and genetic linkage between loci. For each, we outline historical antecedents of the theory, specific contexts where constraints manifest, and their quantitative consequences for adaptation. From each of these theoretical foundations, we discuss recent empirical approaches for identifying and characterizing genetic constraints, each grounded and motivated by this theory, and outline promising areas for future work.

An Upper Limit on the Functional Fraction of the Human Genome

For the human population to maintain a constant size from generation to generation, an increase in fertility must compensate for the reduction in the mean fitness of the population caused, among others, by deleterious mutations. The required increase in fertility due to this mutational load depends on the number of sites in the genome that are functional, the mutation rate, and the fraction of deleterious mutations among all mutations in functional regions. These dependencies and the fact that there exists a maximum tolerable replacement level fertility can be used to put an upper limit on the fraction of the human genome that can be functional. Mutational load considerations lead to the conclusion that the functional fraction within the human genome cannot exceed 15%.

Slightly deleterious genomic variants and transcriptome perturbations in Down syndrome embryonic selection

The majority of aneuploid fetuses are spontaneously miscarried. Nevertheless, some aneuploid individuals survive despite the strong genetic insult. Here, we investigate if the survival probability of aneuploid fetuses is affected by the genome-wide burden of slightly deleterious variants. We analyzed two cohorts of live-born Down syndrome individuals (388 genotyped samples and 16 fibroblast transcriptomes) and observed a deficit of slightly deleterious variants on Chromosome 21 and decreased transcriptome-wide variation in the expression level of highly constrained genes. We interpret these results as signatures of embryonic selection, and propose a genetic handicap model whereby an individual bearing an extremely severe deleterious variant (such as aneuploidy) could escape embryonic lethality if the genome-wide burden of slightly deleterious variants is sufficiently low. This approach can be used to study the composition and effect of the numerous slightly deleterious variants in humans and model organisms.