Evolutionary processes acting on candidate cis-regulatory regions in humans inferred from patterns of polymorphism and divergence

The influence of gene flow on population viability in an isolated urban caracal population

Wildlife populations are becoming increasingly fragmented by anthropogenic development. Small and isolated populations often face an elevated risk of extinction, in part due to inbreeding depression. Here, we examine the genomic consequences of urbanization in a caracal (Caracal caracal) population that has become isolated in the Cape Peninsula region of the City of Cape Town, South Africa, and is thought to number ~50 individuals. We document low levels of migration into the population over the past ~75 years, with an estimated rate of 1.3 effective migrants per generation. As a consequence of this isolation and small population size, levels of inbreeding are elevated in the contemporary Cape Peninsula population (mean FROH = 0.20). Inbreeding primarily manifests as long runs of homozygosity >10 Mb, consistent with the effects of isolation due to the rapid recent growth of Cape Town. To explore how reduced migration and elevated inbreeding may impact future population dynamics, we parameterized an eco-evolutionary simulation model. We find that if migration rates do not change in the future, the population is expected to decline, though with a low projected risk of extinction. However, if migration rates decline or anthropogenic mortality rates increase, the potential risk of extinction is greatly elevated. To avert a population decline, we suggest that translocating migrants into the Cape Peninsula to initiate a genetic rescue may be warranted in the near future. Our analysis highlights the utility of genomic datasets coupled with computational simulation models for investigating the influence of gene flow on population viability.

Selection on the promoter regions plays an important role in complex traits during duck domestication

BACKGROUND

Identifying the key factors that underlie complex traits during domestication is a great challenge for evolutionary and biological studies. In addition to the protein-coding region differences caused by variants, a large number of variants are located in the noncoding regions containing multiple types of regulatory elements. However, the roles of accumulated variants in gene regulatory elements during duck domestication and economic trait improvement are poorly understood.

RESULTS

We constructed a genomics, transcriptomics, and epigenomics map of the duck genome and assessed the evolutionary forces that have been in play across the whole genome during domestication. In total, 304 (42.94%) gene promoters have been specifically selected in Pekin duck among all selected genes. Joint multi-omics analysis reveals that 218 genes (72.01%) with selected promoters are located in open and active chromatin, and 267 genes (87.83%) with selected promoters were highly and differentially expressed in domestic trait-related tissues. One important candidate gene ELOVL3, with a strong signature of differentiation on the core promoter region, is known to regulate fatty acid elongation. Functional experiments showed that the nearly fixed variants in the top selected ELOVL3 promoter in Pekin duck decreased binding ability with HLF and increased gene expression, with the overexpression of ELOVL3 able to increase lipid deposition and unsaturated fatty acid enrichment.

CONCLUSIONS

This study presents genome resequencing, RNA-Seq, Hi-C, and ATAC-Seq data of mallard and Pekin duck, showing that selection of the gene promoter region plays an important role in gene expression and phenotypic changes during domestication and highlights that the variants of the ELOVL3 promoter may have multiple effects on fat and long-chain fatty acid content in ducks.

Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations

AbstractDeleterious genetic variation is abundant in wild populations, and understanding the ecological and conservation implications of such variation is an area of active research. Genomic methods are increasingly used to quantify the impacts of deleterious variation in natural populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of mutations. Computational simulations of deleterious variation offer a complementary tool that can help overcome these limitations, although such approaches have yet to be widely employed. In this perspective article, we aim to encourage ecological and conservation genomics researchers to adopt greater use of computational simulations to aid in deepening our understanding of deleterious variation in natural populations. We first provide an overview of the components of a simulation of deleterious variation, describing the key parameters involved in such models. Next, we discuss several approaches for validating simulation models. Finally, we compare and validate several recently proposed deleterious mutation models, demonstrating that models based on estimates of selection parameters from experimental systems are biased toward highly deleterious mutations. We describe a new model that is supported by multiple orthogonal lines of evidence and provide example scripts for implementing this model (https://github.com/ckyriazis/simulations_review).

Quantifying adaptive evolution and the effects of natural selection across the Norway spruce genome

Detecting natural selection is one of the major goals of evolutionary genomics. Here, we sequenced the whole genome of 25 Picea abies individuals and quantified the amount of selection across the genome. Using an estimate of the distribution of fitness effects, we showed that both negative selection and the rate of positively selected substitutions are very limited in coding regions. We found a positive correlation between the rate of adaptive substitutions and recombination rate and a negative correlation between the rate of adaptive substitutions and gene density, suggesting a widespread influence from Hill-Robertson interference on the efficiency of protein adaptation in P. abies. Finally, the distinct population statistics between genomic regions under either positive or balancing selection with that under neutral regions indicated the impact of natural selection on the genomic architecture of Norway spruce. Further gene ontology enrichment analysis for genes located in regions identified as undergoing either positive or long-term balancing selection also highlighted the specific molecular functions and biological processes that appear to be targets of selection in Norway spruce.

A genome-wide genetic screen uncovers determinants of human pigmentation

Skin color, one of the most diverse human traits, is determined by the quantity, type, and distribution of melanin. In this study, we leveraged the light-scattering properties of melanin to conduct a genome-wide screen for regulators of melanogenesis. We identified 169 functionally diverse genes that converge on melanosome biogenesis, endosomal transport, and gene regulation, of which 135 represented previously unknown associations with pigmentation. In agreement with their melanin-promoting function, the majority of screen hits were up-regulated in melanocytes from darkly pigmented individuals. We further unraveled functions of KLF6 as a transcription factor that regulates melanosome maturation and pigmentation in vivo, and of the endosomal trafficking protein COMMD3 in modulating melanosomal pH. Our study reveals a plethora of melanin-promoting genes, with broad implications for human variation, cell biology, and medicine.

MATS: a novel multi-ancestry transcriptome-wide association study to account for heterogeneity in the effects of cis-regulated gene expression on complex traits

The Transcriptome-Wide Association Study (TWAS) is a widely used approach which integrates gene expression and Genome Wide Association Study (GWAS) data to study the role of cis-regulated gene expression (GEx) in complex traits. However, the genetic architecture of GEx varies across populations, and recent findings point to possible ancestral heterogeneity in the effects of GEx on complex traits, which may be amplified in TWAS by modeling GEx as a function of cis-eQTLs. Here, we present a novel extension to TWAS to account for heterogeneity in the effects of cis-regulated GEx which are correlated with ancestry. Our proposed Multi-Ancestry TwaS (MATS) framework jointly analyzes samples from multiple populations and distinguishes between shared, ancestry-specific and/or subject-specific expression-trait associations. As such, MATS amplifies power to detect shared GEx associations over ancestry-stratified TWAS through increased sample sizes, and facilitates the detection of genes with subgroup-specific associations which may be masked by standard TWAS. Our simulations highlight the improved Type-I error conservation and power of MATS compared with competing approaches. Our real data applications to Alzheimer's disease (AD) case-control genotypes from the Alzheimer's Disease Sequencing Project (ADSP) and continuous phenotypes from the UK Biobank (UKBB) identify a number of unique gene-trait associations which were not discovered through standard and/or ancestry-stratified TWAS. Ultimately, these findings promote MATS as a powerful method for detecting and estimating significant gene expression effects on complex traits within multi-ancestry cohorts and corroborates the mounting evidence for inter-population heterogeneity in gene-trait associations.

Genomic Underpinnings of Population Persistence in Isle Royale Moose

Island ecosystems provide natural laboratories to assess the impacts of isolation on population persistence. However, most studies of persistence have focused on a single species, without comparisons to other organisms they interact with in the ecosystem. The case study of moose and gray wolves on Isle Royale allows for a direct contrast of genetic variation in isolated populations that have experienced dramatically differing population trajectories over the past decade. Whereas the Isle Royale wolf population recently declined nearly to extinction due to severe inbreeding depression, the moose population has thrived and continues to persist, despite having low genetic diversity and being isolated for ∼120 years. Here, we examine the patterns of genomic variation underlying the continued persistence of the Isle Royale moose population. We document high levels of inbreeding in the population, roughly as high as the wolf population at the time of its decline. However, inbreeding in the moose population manifests in the form of intermediate-length runs of homozygosity suggestive of historical inbreeding and purging, contrasting with the long runs of homozygosity observed in the smaller wolf population. Using simulations, we confirm that substantial purging has likely occurred in the moose population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the use of genomic datasets and computational simulation tools for understanding the factors enabling persistence in isolated populations.

Do Noncoding and Coding Sites in Angiosperm Chloroplast DNA Have Different Mutation Processes?

Fourfold degenerate sites within coding regions and intergenic sites have both been used as estimates of neutral evolution. In chloroplast DNA, the pattern of substitution at intergenic sites is strongly dependent on the composition of the surrounding hexanucleotide composed of the three base pairs on each side, which suggests that the mutation process is highly context-dependent in this genome. This study examines the context-dependency of substitutions at fourfold degenerate sites in protein-coding regions and compares the pattern to what has been observed at intergenic sites. Overall, there is strong similarity between the two types of sites, but there are some intriguing differences. One of these is that substitutions of G and C are significantly higher at fourfold degenerate sites across a range of contexts. In fact, A → T and T → A substitutions are the only substitution types that occur at a lower rate at fourfold degenerate sites. The data are not consistent with selective constraints being responsible for the difference in substitution patterns between intergenic and fourfold degenerate sites. Rather, it is suggested that the difference may be a result of different epigenetic modifications that result in slightly different mutation patterns in coding and intergenic DNA.

Signature selection forces and evolutionary divergence of immune-survival genes compared between two important shrimp species

In recent years, shrimp aquaculture industry had grown significantly to become the major source of global shrimp production. Despite that, shrimp aquaculture production was impeded by various shrimp diseases over the past decades. Interestingly, different shrimp species demonstrated variable levels of immune strength and survival (immune-survival) ability towards different diseases, especially the much stronger immune-survival ability shown by the ancient shrimp species, Macrobrachium rosenbergii compared to other shrimp species. In this study, two important shrimp species, M. rosenbergii and Penaeus monodon (disease tolerant strain) (uninfected control and VpAHPND-infected) were compared to uncover the potential underlying genetic factors. The shrimp species were sampled, followed by RNA extraction and cDNA conversion. Five important immune-survival genes (C-type Lectin, HMGB, STAT, ALF3, and ATPase 8/6) were selected for PCR, sequencing, and subsequent genetics analysis. The overall genetic analyses conducted, including Analysis of Molecular Variance (AMOVA) and population differentiation, showed significant genetic differentiation (p<0.05) between different genes of M. rosenbergii and P. monodon. There was greater genetic divergence identified between HMGB subgroups of P. monodon (uninfected control and VpAHPND-infected) compared to other genes. Besides that, based on neutrality tests conducted, purifying selection was determined to be the main evolutionary driving force of M. rosenbergii and P. monodon with stronger purifying selection exhibited in M. rosenbergii genes. Potential balancing selection was identified for VpAHPND-infected HMGB subgroup whereas directional selection was detected for HMGB (both species) and ATPase 8/6 (only P. monodon) genes. The divergence times between M. rosenbergii and P. monodon genes were estimated through Bayesian molecular clock analysis, which were 438.6 mya (C-type Lectin), 1885.4 mya (HMGB), 432.6 mya (STAT), 448.1 mya (ALF3), and 426.4 mya (ATPase 8/6) respectively. In conclusion, important selection forces and evolutionary divergence information of immune-survival genes between M. rosenbergii and P. monodon were successfully identified.

Extreme purifying selection against point mutations in the human genome

Large-scale genome sequencing has enabled the measurement of strong purifying selection in protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring such selection in noncoding as well as coding regions of the human genome. ExtRaINSIGHT estimates the prevalence of “ultraselection” by the fractional depletion of rare single-nucleotide variants, after controlling for variation in mutation rates. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find abundant ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. By contrast, we find much less ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest levels in ultraconserved elements. We estimate that ~0.4–0.7% of the human genome is ultraselected, implying ~ 0.26–0.51 strongly deleterious mutations per generation. Overall, our study sheds new light on the genome-wide distribution of fitness effects by combining deep sequencing data and classical theory from population genetics.

Previous work has investigated selection in the coding genome, but it is not as well characterized in the non-coding genome. By analyzing rare variants in 70k genome sequences from gnomAD, the authors detect very strong purifying selection ("ultraselection”) across the human genome, finding it in some microRNAs and coding sequences but generally rare in regulatory sequences.

Haplotype-based inference of the distribution of fitness effects

Recent genome sequencing studies with large sample sizes in humans have discovered a vast quantity of low-frequency variants, providing an important source of information to analyze how selection is acting on human genetic variation. In order to estimate the strength of natural selection acting on low-frequency variants, we have developed a likelihood-based method that uses the lengths of pairwise identity-by-state between haplotypes carrying low-frequency variants. We show that in some non-equilibrium populations (such as those that have had recent population expansions) it is possible to distinguish between positive or negative selection acting on a set of variants. With our new framework, one can infer a fixed selection intensity acting on a set of variants at a particular frequency, or a distribution of selection coefficients for standing variants and new mutations. We show an application of our method to the UK10K phased haplotype dataset of individuals.

Both Binding Strength and Evolutionary Accessibility Affect the Population Frequency of Transcription Factor Binding Sequences in Arabidopsis thaliana

Mutations in DNA sequences that bind transcription factors and thus modulate gene expression are a source of adaptive variation in gene expression. To understand how transcription factor binding sequences evolve in natural populations of the thale cress Arabidopsis thaliana, we integrated genomic polymorphism data for loci bound by transcription factors with in vitro data on binding affinity for these transcription factors. Specifically, we studied 19 different transcription factors, and the allele frequencies of 8,333 genomic loci bound in vivo by these transcription factors in 1,135 A. thaliana accessions. We find that transcription factor binding sequences show very low genetic diversity, suggesting that they are subject to purifying selection. High frequency alleles of such binding sequences tend to bind transcription factors strongly. Conversely, alleles that are absent from the population tend to bind them weakly. In addition, alleles with high frequencies also tend to be the endpoints of many accessible evolutionary paths leading to these alleles. We show that both high affinity and high evolutionary accessibility contribute to high allele frequency for at least some transcription factors. Although binding sequences with stronger affinity are more frequent, we did not find them to be associated with higher gene expression levels. Epistatic interactions among individual mutations that alter binding affinity are pervasive and can help explain variation in accessibility among binding sequences. In summary, combining in vitro binding affinity data with in vivo binding sequence data can help understand the forces that affect the evolution of transcription factor binding sequences in natural populations.

Decreased recent adaptation at human mendelian disease genes as a possible consequence of interference between advantageous and deleterious variants

Advances in genome sequencing have improved our understanding of the genetic basis of human diseases, and thousands of human genes have been associated with different diseases. Recent genomic adaptation at disease genes has not been well characterized. Here, we compare the rate of strong recent adaptation in the form of selective sweeps between mendelian, non-infectious disease genes and non-disease genes across distinct human populations from the 1000 Genomes Project. We find that mendelian disease genes have experienced far less selective sweeps compared to non-disease genes especially in Africa. Investigating further the possible causes of the sweep deficit at disease genes, we find that this deficit is very strong at disease genes with both low recombination rates and with high numbers of associated disease variants, but is almost non-existent at disease genes with higher recombination rates or lower numbers of associated disease variants. Because segregating recessive deleterious variants have the ability to interfere with adaptive ones, these observations strongly suggest that adaptation has been slowed down by the presence of interfering recessive deleterious variants at disease genes. These results suggest that disease genes suffer from a transient inability to adapt as fast as the rest of the genome.

Impact of Genetic Variation in Gene Regulatory Sequences: A Population Genomics Perspective

The unprecedented rise of high-throughput sequencing and assay technologies has provided a detailed insight into the non-coding sequences and their potential role as gene expression regulators. These regulatory non-coding sequences are also referred to as cis-regulatory elements (CREs). Genetic variants occurring within CREs have been shown to be associated with altered gene expression and phenotypic changes. Such variants are known to occur spontaneously and ultimately get fixed, due to selection and genetic drift, in natural populations and, in some cases, pave the way for speciation. Hence, the study of genetic variation at CREs has improved our overall understanding of the processes of local adaptation and evolution. Recent advances in high-throughput sequencing and better annotations of CREs have enabled the evaluation of the impact of such variation on gene expression, phenotypic alteration and fitness. Here, we review recent research on the evolution of CREs and concentrate on studies that have investigated genetic variation occurring in these regulatory sequences within the context of population genetics.

Polygenic Adaptation: Integrating Population Genetics and Gene Regulatory Networks

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

Network and Evolutionary Analysis of Human Epigenetic Regulators to Unravel Disease Associations

We carried out a system-level analysis of epigenetic regulators (ERs) and detailed the protein–protein interaction (PPI) network characteristics of disease-associated ERs. We found that most diseases associated with ERs can be clustered into two large groups, cancer diseases and developmental diseases. ER genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate with one another. We used the disease module detection (DIAMOnD) algorithm to expand the PPI subnetworks into a comprehensive cancer disease ER network (CDEN) and developmental disease ER network (DDEN). Using the transcriptome from early mouse developmental stages, we identified the gene co-expression modules significantly enriched for the CDEN and DDEN gene sets, which indicated the stage-dependent roles of ER-related disease genes during early embryonic development. The evolutionary rate and phylogenetic age distribution analysis indicated that the evolution of CDEN and DDEN genes was mostly constrained, and these genes exhibited older evolutionary age. Our analysis of human polymorphism data revealed that genes belonging to DDEN and Seed-DDEN were more likely to show signs of recent positive selection in human history. This finding suggests a potential association between positive selection of ERs and risk of developmental diseases through the mechanism of antagonistic pleiotropy.

Greater strength of selection and higher proportion of beneficial amino acid changing mutations in humans compared with mice and Drosophila melanogaster

Quantifying and comparing the amount of adaptive evolution among different species is key to understanding how evolution works. Previous studies have shown differences in adaptive evolution across species; however, their specific causes remain elusive. Here, we use improved modeling of weakly deleterious mutations and the demographic history of the outgroup species and ancestral population and estimate that at least 20% of nonsynonymous substitutions between humans and an outgroup species were fixed by positive selection. This estimate is much higher than previous estimates, which did not correct for the sizes of the outgroup species and ancestral population. Next, we jointly estimate the proportion and selection coefficient (p⁺ and s⁺, respectively) of newly arising beneficial nonsynonymous mutations in humans, mice, and Drosophila melanogaster by examining patterns of polymorphism and divergence. We develop a novel composite likelihood framework to test whether these parameters differ across species. Overall, we reject a model with the same p⁺ and s⁺ of beneficial mutations across species and estimate that humans have a higher p⁺s⁺ compared with that of D. melanogaster and mice. We show that this result cannot be caused by biased gene conversion or hypermutable CpG sites. We discuss possible biological explanations that could generate the observed differences in the amount of adaptive evolution across species.

Selection against archaic hominin genetic variation in regulatory regions

Traces of Neandertal and Denisovan DNA persist in the modern human gene pool, but have been systematically purged by natural selection from genes and other functionally important regions. This implies that many archaic alleles harmed the fitness of hybrid individuals, but the nature of this harm is poorly understood. Here, we show that enhancers contain less Neandertal and Denisovan variation than expected given the background selection they experience, suggesting that selection acted to purge these regions of archaic alleles that disrupted their gene regulatory functions. We infer that selection acted mainly on young archaic variation that arose in Neandertals or Denisovans shortly before their contact with humans; enhancers are not depleted of older variants found in both archaic species. Some types of enhancer appear to have tolerated introgression better than others; compared with tissue-specific enhancers, pleiotropic enhancers show stronger depletion of archaic single-nucleotide polymorphisms. To some extent, evolutionary constraint is predictive of introgression depletion, but certain tissues' enhancers are more depleted of Neandertal and Denisovan alleles than expected given their comparative tolerance to new mutations. Foetal brain and muscle are the tissues whose enhancers show the strongest depletion of archaic alleles, but only brain enhancers show evidence of unusually stringent purifying selection. We conclude that epistatic incompatibilities between human and archaic alleles are needed to explain the degree of archaic variant depletion from foetal muscle enhancers, perhaps due to divergent selection for higher muscle mass in archaic hominins compared with humans.

Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution

Comparative genomic approaches have been used to identify sites where mutations are under purifying selection and of functional consequence by searching for sequences that are conserved across distantly related species. However, the performance of these approaches has not been rigorously evaluated under population genetic models. Further, short-lived functional elements may not leave a footprint of sequence conservation across many species. We use simulations to study how one measure of conservation, the Genomic Evolutionary Rate Profiling (GERP) score, relates to the strength of selection (N_es). We show that the GERP score is related to the strength of purifying selection. However, changes in selection coefficients or functional elements over time (i.e. functional turnover) can strongly affect the GERP distribution, leading to unexpected relationships between GERP and N_es. Further, we show that for functional elements that have a high turnover rate, adding more species to the analysis does not necessarily increase statistical power. Finally, we use the distribution of GERP scores across the human genome to compare models with and without turnover of sites where mutations are under purifying selection. We show that mutations in 4.51% of the noncoding human genome are under purifying selection and that most of this sequence has likely experienced changes in selection coefficients throughout mammalian evolution. Our work reveals limitations to using comparative genomic approaches to identify deleterious mutations. Commonly used GERP score thresholds miss over half of the noncoding sites in the human genome where mutations are under purifying selection.

One of the most significant and challenging tasks in modern genomics is to assess the functional consequences of a particular nucleotide change in a genome. A common approach to address this challenge prioritizes sequences that share similar nucleotides across distantly related species, with the rationale that mutations at such positions were deleterious and removed from the population by purifying natural selection. Our manuscript shows that one popular measure of sequence conservation, the GERP score, performs well at identifying selected mutations if mutations at a site were under selection across all of mammalian evolution. Changes in selection at a given site dramatically reduces the power of GERP to detect selected mutations in humans. We also combine population genetic models with the distribution of GERP scores at noncoding sites across the human genome to show that the degree of selection at individual sites has changed throughout mammalian evolution. Importantly, we demonstrate that at least 80 Mb of noncoding sequence under purifying selection in humans will not have extreme GERP scores and will likely be missed by modern comparative genomic approaches. Our work argues that new approaches, potentially based on genetic variation within species, will be required to identify deleterious mutations.

How Natural Genetic Variation Shapes Behavior

Nervous systems allow animals to acutely respond and behaviorally adapt to changes and recurring patterns in their environment at multiple timescales-from milliseconds to years. Behavior is further shaped at intergenerational timescales by genetic variation, drift, and selection. This sophistication and flexibility of behavior makes it challenging to measure behavior consistently in individual subjects and to compare it across individuals. In spite of these challenges, careful behavioral observations in nature and controlled measurements in the laboratory, combined with modern technologies and powerful genetic approaches, have led to important discoveries about the way genetic variation shapes behavior. A critical mass of genes whose variation is known to modulate behavior in nature is finally accumulating, allowing us to recognize emerging patterns. In this review, we first discuss genetic mapping approaches useful for studying behavior. We then survey how variation acts at different levels-in environmental sensation, in internal neuronal circuits, and outside the nervous system altogether-and then discuss the sources and types of molecular variation linked to behavior and the mechanisms that shape such variation. We end by discussing remaining questions in the field.

The Temporal Dynamics of Background Selection in Nonequilibrium Populations

Neutral genetic diversity across the genome is determined by the complex interplay of mutation, demographic history, and natural selection. While the direct action of natural selection is limited to functional loci across the genome, its impact can have effects on nearby neutral loci due to genetic linkage. These effects of selection at linked sites, referred to as genetic hitchhiking and background selection (BGS), are pervasive across natural populations. However, only recently has there been a focus on the joint consequences of demography and selection at linked sites, and some empirical studies have come to apparently contradictory conclusions as to their combined effects. To understand the relationship between demography and selection at linked sites, we conducted an extensive forward simulation study of BGS under a range of demographic models. We found that the relative levels of diversity in BGS and neutral regions vary over time and that the initial dynamics after a population size change are often in the opposite direction of the long-term expected trajectory. Our detailed observations of the temporal dynamics of neutral diversity in the context of selection at linked sites in nonequilibrium populations provide new intuition about why patterns of diversity under BGS vary through time in natural populations and help reconcile previously contradictory observations. Most notably, our results highlight that classical models of BGS are poorly suited for predicting diversity in nonequilibrium populations.

Evolutionary perspectives on polygenic selection, missing heritability, and GWAS

Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.

Population genetic simulation study of power in association testing across genetic architectures and study designs

While it is well established that genetics can be a major contributor to population variation of complex traits, the relative contributions of rare and common variants to phenotypic variation remains a matter of considerable debate. Here, we simulate genetic and phenotypic data across different case/control panel sampling strategies, sequencing methods, and genetic architecture models based on evolutionary forces to determine the statistical performance of rare variant association tests (RVATs) widely in use. We find that the highest statistical power of RVATs is achieved by sampling case/control individuals from the extremes of an underlying quantitative trait distribution. We also demonstrate that the use of genotyping arrays, in conjunction with imputation from a whole-genome sequenced (WGS) reference panel, recovers the vast majority (90%) of the power that could be achieved by sequencing the case/control panel using current tools. Finally, we show that for dichotomous traits, the statistical performance of RVATs decreases as rare variants become more important in the trait architecture. Our results extend previous work to show that RVATs are insufficiently powered to make generalizable conclusions about the role of rare variants in dichotomous complex traits.

Ultrarare variants drive substantial cis heritability of human gene expression

The vast majority of human mutations have minor allele frequencies under 1%, with the plurality observed only once (that is, 'singletons'). While Mendelian diseases are predominantly caused by rare alleles, their cumulative contribution to complex phenotypes is largely unknown. We develop and rigorously validate an approach to jointly estimate the contribution of all alleles, including singletons, to phenotypic variation. We apply our approach to transcriptional regulation, an intermediate between genetic variation and complex disease. Using whole-genome DNA and lymphoblastoid cell line RNA sequencing data from 360 European individuals, we conservatively estimate that singletons contribute approximately 25% of cis heritability across genes (dwarfing the contributions of other frequencies). The majority (approximately 76%) of singleton heritability derives from ultrarare variants absent from thousands of additional samples. We develop an inference procedure to demonstrate that our results are consistent with pervasive purifying selection shaping the regulatory architecture of most human genes.

The Neutral Theory in Light of Natural Selection

In this perspective, we evaluate the explanatory power of the neutral theory of molecular evolution, 50 years after its introduction by Kimura. We argue that the neutral theory was supported by unreliable theoretical and empirical evidence from the beginning, and that in light of modern, genome-scale data, we can firmly reject its universality. The ubiquity of adaptive variation both within and between species means that a more comprehensive theory of molecular evolution must be sought.

Gene regulation underlies environmental adaptation in house mice

Changes in cis-regulatory regions are thought to play a major role in the genetic basis of adaptation. However, few studies have linked cis-regulatory variation with adaptation in natural populations. Here, using a combination of exome and RNA-seq data, we performed expression quantitative trait locus (eQTL) mapping and allele-specific expression analyses to study the genetic architecture of regulatory variation in wild house mice (Mus musculus domesticus) using individuals from five populations collected along a latitudinal cline in eastern North America. Mice in this transect showed clinal patterns of variation in several traits, including body mass. Mice were larger in more northern latitudes, in accordance with Bergmann's rule. We identified 17 genes where cis-eQTLs were clinal outliers and for which expression level was correlated with latitude. Among these clinal outliers, we identified two genes (Adam17 and Bcat2) with cis-eQTLs that were associated with adaptive body mass variation and for which expression is correlated with body mass both within and between populations. Finally, we performed a weighted gene co-expression network analysis (WGCNA) to identify expression modules associated with measures of body size variation in these mice. These findings demonstrate the power of combining gene expression data with scans for selection to identify genes involved in adaptive phenotypic evolution, and also provide strong evidence for cis-regulatory elements as essential loci of environmental adaptation in natural populations.

RAiSD detects positive selection based on multiple signatures of a selective sweep and SNP vectors

Selective sweeps leave distinct signatures locally in genomes, enabling the detection of loci that have undergone recent positive selection. Multiple signatures of a selective sweep are known, yet each neutrality test only identifies a single signature. We present RAiSD (Raised Accuracy in Sweep Detection), an open-source software that implements a novel, to our knowledge, and parameter-free detection mechanism that relies on multiple signatures of a selective sweep via the enumeration of SNP vectors. RAiSD achieves higher sensitivity and accuracy than the current state of the art, while the computational complexity is greatly reduced, allowing up to 1000 times faster processing than widely used tools, and negligible memory requirements.

Nikolaos Alachiotis and Pavlos Pavlidis present RAiSD, a computational method for identifying multiple signatures of selective sweeps using single nucleotide polymorphism vectors. They show that RAiSD has higher sensitivity and accuracy with reduced computational complexity than current methods.

Human demographic history has amplified the effects of background selection across the genome

Natural populations often grow, shrink, and migrate over time. Such demographic processes can affect genome-wide levels of genetic diversity. Additionally, genetic variation in functional regions of the genome can be altered by natural selection, which drives adaptive mutations to higher frequencies or purges deleterious ones. Such selective processes affect not only the sites directly under selection but also nearby neutral variation through genetic linkage via processes referred to as genetic hitchhiking in the context of positive selection and background selection (BGS) in the context of purifying selection. While there is extensive literature examining the consequences of selection at linked sites at demographic equilibrium, less is known about how non-equilibrium demographic processes influence the effects of hitchhiking and BGS. Utilizing a global sample of human whole-genome sequences from the Thousand Genomes Project and extensive simulations, we investigate how non-equilibrium demographic processes magnify and dampen the consequences of selection at linked sites across the human genome. When binning the genome by inferred strength of BGS, we observe that, compared to Africans, non-African populations have experienced larger proportional decreases in neutral genetic diversity in strong BGS regions. We replicate these findings in admixed populations by showing that non-African ancestral components of the genome have also been affected more severely in these regions. We attribute these differences to the strong, sustained/recurrent population bottlenecks that non-Africans experienced as they migrated out of Africa and throughout the globe. Furthermore, we observe a strong correlation between FST and the inferred strength of BGS, suggesting a stronger rate of genetic drift. Forward simulations of human demographic history with a model of BGS support these observations. Our results show that non-equilibrium demography significantly alters the consequences of selection at linked sites and support the need for more work investigating the dynamic process of multiple evolutionary forces operating in concert.

Population Genetics and Natural Selection in Rheumatic Disease

Human genetic diversity is the result of population genetic forces. This genetic variation influences disease risk and contributes to health disparities. Natural selection is an important influence on human genetic variation. Because immune and inflammatory function genes are enriched for signals of positive selection, the prevalence of rheumatic disease-risk alleles seen in different populations is partially the result of differing selective pressures (eg, due to pathogens). This review summarizes the genetic regions associated with susceptibility to different rheumatic diseases and concomitant evidence for natural selection, including known agents of selection exerting selective pressure in these regions.

Signatures of negative selection in the genetic architecture of human complex traits

We develop a Bayesian mixed linear model that simultaneously estimates single-nucleotide polymorphism (SNP)-based heritability, polygenicity (proportion of SNPs with nonzero effects), and the relationship between SNP effect size and minor allele frequency for complex traits in conventionally unrelated individuals using genome-wide SNP data. We apply the method to 28 complex traits in the UK Biobank data (N = 126,752) and show that on average, 6% of SNPs have nonzero effects, which in total explain 22% of phenotypic variance. We detect significant (P < 0.05/28) signatures of natural selection in the genetic architecture of 23 traits, including reproductive, cardiovascular, and anthropometric traits, as well as educational attainment. The significant estimates of the relationship between effect size and minor allele frequency in complex traits are consistent with a model of negative (or purifying) selection, as confirmed by forward simulation. We conclude that negative selection acts pervasively on the genetic variants associated with human complex traits.

Dynamic evolution of regulatory element ensembles in primate CD4+ T cells

How evolutionary changes at enhancers affect the transcription of target genes remains an important open question. Previous comparative studies of gene expression have largely measured the abundance of messenger RNA, which is affected by post-transcriptional regulatory processes, hence limiting inferences about the mechanisms underlying expression differences. Here, we directly measured nascent transcription in primate species, allowing us to separate transcription from post-transcriptional regulation. We used precision run-on and sequencing to map RNA polymerases in resting and activated CD4⁺ T cells in multiple human, chimpanzee and rhesus macaque individuals, with rodents as outgroups. We observed general conservation in coding and non-coding transcription, punctuated by numerous differences between species, particularly at distal enhancers and non-coding RNAs. Genes regulated by larger numbers of enhancers are more frequently transcribed at evolutionarily stable levels, despite reduced conservation at individual enhancers. Adaptive nucleotide substitutions are associated with lineage-specific transcription and at one locus, SGPP2, we predict and experimentally validate that multiple substitutions contribute to human-specific transcription. Collectively, our findings suggest a pervasive role for evolutionary compensation across ensembles of enhancers that jointly regulate target genes.

Inference of the Distribution of Selection Coefficients for New Nonsynonymous Mutations Using Large Samples

The distribution of fitness effects (DFE) has considerable importance in population genetics. To date, estimates of the DFE come from studies using a small number of individuals. Thus, estimates of the proportion of moderately to strongly deleterious new mutations may be unreliable because such variants are unlikely to be segregating in the data. Additionally, the true functional form of the DFE is unknown, and estimates of the DFE differ significantly between studies. Here we present a flexible and computationally tractable method, called Fit∂a∂i, to estimate the DFE of new mutations using the site frequency spectrum from a large number of individuals. We apply our approach to the frequency spectrum of 1300 Europeans from the Exome Sequencing Project ESP6400 data set, 1298 Danes from the LuCamp data set, and 432 Europeans from the 1000 Genomes Project to estimate the DFE of deleterious nonsynonymous mutations. We infer significantly fewer (0.38-0.84 fold) strongly deleterious mutations with selection coefficient |s| > 0.01 and more (1.24-1.43 fold) weakly deleterious mutations with selection coefficient |s| < 0.001 compared to previous estimates. Furthermore, a DFE that is a mixture distribution of a point mass at neutrality plus a gamma distribution fits better than a gamma distribution in two of the three data sets. Our results suggest that nearly neutral forces play a larger role in human evolution than previously thought.

Understanding rare and common diseases in the context of human evolution

The wealth of available genetic information is allowing the reconstruction of human demographic and adaptive history. Demography and purifying selection affect the purge of rare, deleterious mutations from the human population, whereas positive and balancing selection can increase the frequency of advantageous variants, improving survival and reproduction in specific environmental conditions. In this review, I discuss how theoretical and empirical population genetics studies, using both modern and ancient DNA data, are a powerful tool for obtaining new insight into the genetic basis of severe disorders and complex disease phenotypes, rare and common, focusing particularly on infectious disease risk.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.

Which Genetics Variants in DNase-Seq Footprints Are More Likely to Alter Binding?

Large experimental efforts are characterizing the regulatory genome, yet we are still missing a systematic definition of functional and silent genetic variants in non-coding regions. Here, we integrated DNaseI footprinting data with sequence-based transcription factor (TF) motif models to predict the impact of a genetic variant on TF binding across 153 tissues and 1,372 TF motifs. Each annotation we derived is specific for a cell-type condition or assay and is locally motif-driven. We found 5.8 million genetic variants in footprints, 66% of which are predicted by our model to affect TF binding. Comprehensive examination using allele-specific hypersensitivity (ASH) reveals that only the latter group consistently shows evidence for ASH (3,217 SNPs at 20% FDR), suggesting that most (97%) genetic variants in footprinted regulatory regions are indeed silent. Combining this information with GWAS data reveals that our annotation helps in computationally fine-mapping 86 SNPs in GWAS hit regions with at least a 2-fold increase in the posterior odds of picking the causal SNP. The rich meta information provided by the tissue-specificity and the identity of the putative TF binding site being affected also helps in identifying the underlying mechanism supporting the association. As an example, the enrichment for LDL level-associated SNPs is 9.1-fold higher among SNPs predicted to affect HNF4 binding sites than in a background model already including tissue-specific annotation.

Evolutionary Footprints of Short Tandem Repeats in Avian Promoters

Short tandem repeats (STRs) or microsatellites are well-known sequence elements that may change the spacing between transcription factor binding sites (TFBSs) in promoter regions by expansion or contraction of repetitive units. Some of these mutations have the potential to contribute to phenotypic diversity by altering patterns of gene expression. To explore how repetitive sequence motifs within promoters have evolved in avian lineages under mutation-selection balance, more than 400 evolutionary conserved STRs (ecSTRs) were identified in this study by comparing the 2 kb upstream promoter sequences of chicken against those of other birds (turkey, duck, zebra finch, and flycatcher). The rate of conservation was significantly higher in AG dinucleotide repeats than in AC or AT repeats, with the expansion of AG motifs being noticeably constrained in passerines. Analysis of the relative distance between ecSTRs and TFBSs revealed a significantly higher rate of conserved TFBSs in the vicinity of ecSTRs in both chicken-duck and chicken-passerine comparisons. Our comparative study provides a novel insight into which intrinsic factors have influenced the degree of constraint on repeat expansion/contraction during avian promoter evolution.

Genetics of autoimmune diseases: insights from population genetics

Human genetic diversity is the result of population genetic forces. This genetic variation influences disease risk and contributes to health disparities. Autoimmune diseases (ADs) are a family of complex heterogeneous disorders with similar underlying mechanisms characterized by immune responses against self. Collectively, ADs are common, exhibit gender and ethnic disparities, and increasing incidence. As natural selection is an important influence on human genetic variation, and immune function genes are enriched for signals of positive selection, it is thought that the prevalence of AD risk alleles seen in different population is partially the result of differing selective pressures (for example, due to pathogens). With the advent of high-throughput technologies, new analytical methodologies and large-scale projects, evidence for the role of natural selection in contributing to the heritable component of ADs keeps growing. This review summarizes the genetic regions associated with susceptibility to different ADs and concomitant evidence for selection, including known agents of selection exerting selective pressure in these regions. Examples of specific adaptive variants with phenotypic effects are included as an evidence of natural selection increasing AD susceptibility. Many of the complexities of gene effects in different ADs can be explained by population genetics phenomena. Integrating AD susceptibility studies with population genetics to investigate how natural selection has contributed to genetic variation that influences disease risk will help to identify functional variants and elucidate biological mechanisms. As such, the study of population genetics in human population holds untapped potential for elucidating the genetic causes of human disease and more rapidly focusing to personalized medicine.

The Nature of Genetic Variation for Complex Traits Revealed by GWAS and Regional Heritability Mapping Analyses

We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself. We show that GWAS applied to the full sequence increases the number of QTL detected by as much as 50% compared to the number found with SNP chips but only modestly increases the amount of additive genetic variance explained. Even with full sequence data, the total amount of additive variance explained is generally below 50%. Using RHM on the full sequence data, a slightly larger number of QTL are detected than by GWAS if the same probability threshold is assumed, but these QTL explain a slightly smaller amount of genetic variance. Our results also suggest that most of the missing heritability is due to the inability to detect variants of moderate effect (∼0.03-0.3 phenotypic SDs) segregating at substantial frequencies. Very rare variants, which are more difficult to detect by GWAS, are expected to contribute little genetic variation, so their eventual detection is less relevant for resolving the missing heritability problem.

Natural Selection and Functional Potentials of Human Noncoding Elements Revealed by Analysis of Next Generation Sequencing Data

Noncoding DNA sequences (NCS) have attracted much attention recently due to their functional potentials. Here we attempted to reveal the functional roles of noncoding sequences from the point of view of natural selection that typically indicates the functional potentials of certain genomic elements. We analyzed nearly 37 million single nucleotide polymorphisms (SNPs) of Phase I data of the 1000 Genomes Project. We estimated a series of key parameters of population genetics and molecular evolution to characterize sequence variations of the noncoding genome within and between populations, and identified the natural selection footprints in NCS in worldwide human populations. Our results showed that purifying selection is prevalent and there is substantial constraint of variations in NCS, while positive selectionis more likely to be specific to some particular genomic regions and regional populations. Intriguingly, we observed larger fraction of non-conserved NCS variants with lower derived allele frequency in the genome, indicating possible functional gain of non-conserved NCS. Notably, NCS elements are enriched for potentially functional markers such as eQTLs, TF motif, and DNase I footprints in the genome. More interestingly, some NCS variants associated with diseases such as Alzheimer's disease, Type 1 diabetes, and immune-related bowel disorder (IBD) showed signatures of positive selection, although the majority of NCS variants, reported as risk alleles by genome-wide association studies, showed signatures of negative selection. Our analyses provided compelling evidence of natural selection forces on noncoding sequences in the human genome and advanced our understanding of their functional potentials that play important roles in disease etiology and human evolution.

Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviors

It is increasingly apparent that genes and networks that influence complex behavior are evolutionary conserved, which is paradoxical considering that behavior is labile over evolutionary timescales. How does adaptive change in behavior arise if behavior is controlled by conserved, pleiotropic, and likely evolutionary constrained genes? Pleiotropy and connectedness are known to constrain the general rate of protein evolution, prompting some to suggest that the evolution of complex traits, including behavior, is fuelled by regulatory sequence evolution. However, we seldom have data on the strength of selection on mutations in coding and regulatory sequences, and this hinders our ability to study how pleiotropy influences coding and regulatory sequence evolution. Here we use population genomics to estimate the strength of selection on coding and regulatory mutations for a transcriptional regulatory network that influences complex behavior of honey bees. We found that replacement mutations in highly connected transcription factors and target genes experience significantly stronger negative selection relative to weakly connected transcription factors and targets. Adaptively evolving proteins were significantly more likely to reside at the periphery of the regulatory network, while proteins with signs of negative selection were near the core of the network. Interestingly, connectedness and network structure had minimal influence on the strength of selection on putative regulatory sequences for both transcription factors and their targets. Our study indicates that adaptive evolution of complex behavior can arise because of positive selection on protein-coding mutations in peripheral genes, and on regulatory sequence mutations in both transcription factors and their targets throughout the network.

Cis-regulatory elements and human evolution

Modification of gene regulation has long been considered an important force in human evolution, particularly through changes to cis-regulatory elements (CREs) that function in transcriptional regulation. For decades, however, the study of cis-regulatory evolution was severely limited by the available data. New data sets describing the locations of CREs and genetic variation within and between species have now made it possible to study CRE evolution much more directly on a genome-wide scale. Here, we review recent research on the evolution of CREs in humans based on large-scale genomic data sets. We consider inferences based on primate divergence, human polymorphism, and combinations of divergence and polymorphism. We then consider 'new frontiers' in this field stemming from recent research on transcriptional regulation.

Population genetic simulations of complex phenotypes with implications for rare variant association tests

Demographic events and natural selection alter patterns of genetic variation within populations and may play a substantial role in shaping the genetic architecture of complex phenotypes and disease. However, the joint impact of these basic evolutionary forces is often ignored in the assessment of statistical tests of association. Here, we provide a simulation-based framework for generating DNA sequences that incorporates selection and demography with flexible models for simulating phenotypic variation (sfs_coder). This tool also allows the user to perform locus-specific simulations by automatically querying annotated genomic functional elements and genetic maps. We demonstrate the effects of evolutionary forces on patterns of genetic variation by simulating recently inferred models of human selection and demography. We use these simulations to show that the demographic model and locus-specific features, such as the proportion of sites under selection, may have practical implications for estimating the statistical power of sequencing-based rare variant association tests. In particular, for some phenotype models, there may be higher power to detect rare variant associations in African populations compared to non-Africans, but power is considerably reduced in regions of the genome with rampant negative selection. Furthermore, we show that existing methods for simulating large samples based on resampling from a small set of observed haplotypes fail to recapitulate the distribution of rare variants in the presence of rapid population growth (as has been observed in several human populations).

Approximation to the distribution of fitness effects across functional categories in human segregating polymorphisms

Quantifying the proportion of polymorphic mutations that are deleterious or neutral is of fundamental importance to our understanding of evolution, disease genetics and the maintenance of variation genome-wide. Here, we develop an approximation to the distribution of fitness effects (DFE) of segregating single-nucleotide mutations in humans. Unlike previous methods, we do not assume that synonymous mutations are neutral or not strongly selected, and we do not rely on fitting the DFE of all new nonsynonymous mutations to a single probability distribution, which is poorly motivated on a biological level. We rely on a previously developed method that utilizes a variety of published annotations (including conservation scores, protein deleteriousness estimates and regulatory data) to score all mutations in the human genome based on how likely they are to be affected by negative selection, controlling for mutation rate. We map this and other conservation scores to a scale of fitness coefficients via maximum likelihood using diffusion theory and a Poisson random field model on SNP data. Our method serves to approximate the deleterious DFE of mutations that are segregating, regardless of their genomic consequence. We can then compare the proportion of mutations that are negatively selected or neutral across various categories, including different types of regulatory sites. We observe that the distribution of intergenic polymorphisms is highly peaked at neutrality, while the distribution of nonsynonymous polymorphisms has a second peak at [Formula: see text]. Other types of polymorphisms have shapes that fall roughly in between these two. We find that transcriptional start sites, strong CTCF-enriched elements and enhancers are the regulatory categories with the largest proportion of deleterious polymorphisms.

Longevity and plasticity of CFTR provide an argument for noncanonical SNP organization in hominid DNA

Like many other ancient genes, the cystic fibrosis transmembrane conductance regulator (CFTR) has survived for hundreds of millions of years. In this report, we consider whether such prodigious longevity of an individual gene – as opposed to an entire genome or species – should be considered surprising in the face of eons of relentless DNA replication errors, mutagenesis, and other causes of sequence polymorphism. The conventions that modern human SNP patterns result either from purifying selection or random (neutral) drift were not well supported, since extant models account rather poorly for the known plasticity and function (or the established SNP distributions) found in a multitude of genes such as CFTR. Instead, our analysis can be taken as a polemic indicating that SNPs in CFTR and many other mammalian genes may have been generated—and continue to accrue—in a fundamentally more organized manner than would otherwise have been expected. The resulting viewpoint contradicts earlier claims of ‘directional’ or ‘intelligent design-type’ SNP formation, and has important implications regarding the pace of DNA adaptation, the genesis of conserved non-coding DNA, and the extent to which eukaryotic SNP formation should be viewed as adaptive.

Evolutionary dynamics of coding and non-coding transcriptomes

Gene expression changes may underlie much of phenotypic evolution. The development of high-throughput RNA sequencing protocols has opened the door to unprecedented large-scale and cross-species transcriptome comparisons by allowing accurate and sensitive assessments of transcript sequences and expression levels. Here, we review the initial wave of the new generation of comparative transcriptomic studies in mammals and vertebrate outgroup species in the context of earlier work. Together with various large-scale genomic and epigenomic data, these studies have unveiled commonalities and differences in the dynamics of gene expression evolution for various types of coding and non-coding genes across mammalian lineages, organs, developmental stages, chromosomes and sexes. They have also provided intriguing new clues to the regulatory basis and phenotypic implications of evolutionary gene expression changes.

Evidence for widespread positive and negative selection in coding and conserved noncoding regions of Capsella grandiflora

The extent that both positive and negative selection vary across different portions of plant genomes remains poorly understood. Here, we sequence whole genomes of 13 Capsella grandiflora individuals and quantify the amount of selection across the genome. Using an estimate of the distribution of fitness effects, we show that selection is strong in coding regions, but weak in most noncoding regions, with the exception of 5' and 3' untranslated regions (UTRs). However, estimates of selection on noncoding regions conserved across the Brassicaceae family show strong signals of selection. Additionally, we see reductions in neutral diversity around functional substitutions in both coding and conserved noncoding regions, indicating recent selective sweeps at these sites. Finally, using expression data from leaf tissue we show that genes that are more highly expressed experience stronger negative selection but comparable levels of positive selection to lowly expressed genes. Overall, we observe widespread positive and negative selection in coding and regulatory regions, but our results also suggest that both positive and negative selection on plant noncoding sequence are considerably rarer than in animal genomes.

Purifying selection in deeply conserved human enhancers is more consistent than in coding sequences

Comparison of polymorphism at synonymous and non-synonymous sites in protein-coding DNA can provide evidence for selective constraint. Non-coding DNA that forms part of the regulatory landscape presents more of a challenge since there is not such a clear-cut distinction between sites under stronger and weaker selective constraint. Here, we consider putative regulatory elements termed Conserved Non-coding Elements (CNEs) defined by their high level of sequence identity across all vertebrates. Some mutations in these regions have been implicated in developmental disorders; we analyse CNE polymorphism data to investigate whether such deleterious effects are widespread in humans. Single nucleotide variants from the HapMap and 1000 Genomes Projects were mapped across nearly 2000 CNEs. In the 1000 Genomes data we find a significant excess of rare derived alleles in CNEs relative to coding sequences; this pattern is absent in HapMap data, apparently obscured by ascertainment bias. The distribution of polymorphism within CNEs is not uniform; we could identify two categories of sites by exploiting deep vertebrate alignments: stretches that are non-variant, and those that have at least one substitution. The conserved category has fewer polymorphic sites and a greater excess of rare derived alleles, which can be explained by a large proportion of sites under strong purifying selection within humans--higher than that for non-synonymous sites in most protein coding regions, and comparable to that at the strongly conserved trans-dev genes. Conversely, the more evolutionarily labile CNE sites have an allele frequency distribution not significantly different from non-synonymous sites. Future studies should exploit genome-wide re-sequencing to obtain better coverage in selected non-coding regions, given the likelihood that mutations in evolutionarily conserved enhancer sequences are deleterious. Discovery pipelines should validate non-coding variants to aid in identifying causal and risk-enhancing variants in complex disorders, in contrast to the current focus on exome sequencing.

Spontaneous mutation accumulation in multiple strains of the green alga, Chlamydomonas reinhardtii

Estimates of mutational parameters, such as the average fitness effect of a new mutation and the rate at which new genetic variation for fitness is created by mutation, are important for the understanding of many biological processes. However, the causes of interspecific variation in mutational parameters and the extent to which they vary within species remain largely unknown. We maintained multiple strains of the unicellular eukaryote Chlamydomonas reinhardtii, for approximately 1000 generations under relaxed selection by transferring a single cell every ∼10 generations. Mean fitness of the lines tended to decline with generations of mutation accumulation whereas mutational variance increased. We did not find any evidence for differences among strains in any of the mutational parameters estimated. The overall change in mean fitness per cell division and rate of input of mutational variance per cell division were more similar to values observed in multicellular organisms than to those in other single-celled microbes. However, after taking into account differences in genome size among species, estimates from multicellular organisms and microbes, including our new estimates from C. reinhardtii, become substantially more similar. Thus, we suggest that variation in genome size is an important determinant of interspecific variation in mutational parameters.