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Summary The Eurasian Holocene (beginning c. 12 thousand years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using an imputed dataset of >1600 complete ancient genome sequences, and new computational methods for locating selection in time and space, we reconstructed the selection landscape of the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify major selection signals related to metabolism, possibly associated with the dietary shift occurring in this period. We show that the selection on loci such as the FADS cluster, associated with fatty acid metabolism, and the lactase persistence locus, began earlier than previously thought. A substantial amount of selection is also found in the HLA region and other loci associated with immunity, possibly due to the increased exposure to pathogens during the Neolithic, which may explain the current high prevalence of auto-immune disease, such as psoriasis, due to genetic trade-offs. By using ancient populations to infer local ancestry tracks in hundreds of thousands of samples from the UK Biobank, we find strong genetic differentiation among ancient Europeans in loci associated with anthropometric traits and susceptibility to several diseases that contribute to present-day disease burden. These were previously thought to be caused by local selection, but in fact can be attributed to differential genetic contributions from various source populations that are ancestral to present-day Europeans. Thus, alleles associated with increased height seem to have increased in frequency following the Yamnaya migration into northwestern Europe around 5,000 years ago. Alleles associated with increased risk of some mood-related phenotypes are overrepresented in the farmer ancestry component entering Europe from Anatolia around 11,000 years ago, while western hunter-gatherers show a strikingly high contribution of alleles conferring risk of traits related to diabetes. Our results paint a picture of the combined contributions of migration and selection in shaping the phenotypic landscape of present-day Europeans that suggests a combination of ancient selection and migration, rather than recent local selection, is the primary driver of present-day phenotypic differences in Europe.

One of the most powerful and commonly used approaches for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this article, we present a new maximum likelihood method for finding regions under positive selection. It is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. Using simulated data, we compare our method to related approaches, and show that it is orders of magnitude faster than the state-of-the-art, while retaining similar or higher power for most simulation scenarios. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin, and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, such as immunity, fat distribution, food intake, vision, and hair development.

Causal mutations and their frequency in agricultural fields are well-characterized for herbicide resistance. However, we still lack an understanding of their evolutionary history: the extent of parallelism in the origins of target-site resistance (TSR), how long these mutations persist, how quickly they spread, and allelic interactions that mediate their selective advantage. We addressed these questions with genomic data from 18 agricultural populations of common waterhemp (Amaranthus tuberculatus), which we show to have undergone a massive expansion over the past century with a contemporary effective population size estimate of 8 × 107. We found variation at seven characterized TSR loci, two of which had multiple amino acid substitutions, and three of which were common. These three common resistance variants show parallelism in their mutational origins, with gene flow having shaped their distribution across the landscape. Allele age estimates supported a strong role of adaptation from de novo mutations, with a median allele age of 30 suggesting that most resistance alleles arose soon after the onset of herbicide use. However, resistant lineages varied in both their age and evidence for selection over two different timescales, implying considerable heterogeneity in the forces that govern their persistence. The evolutionary history of TSR has also been shaped by both intra- and inter-locus allelic interactions. We report a signal of extended haplotype competition between two common TSR alleles, and extreme linkage with genome-wide alleles with known functions in resistance adaptation. Together, this work reveals a remarkable example of spatial parallel evolution in a metapopulation, with important implications for the management of herbicide resistance.

Inbred populations often suffer from heightened mutational load and decreased fitness due to lower efficiency of purifying selection at small effective population size. Genetic rescue (GR) is a tool that is studied and deployed with the aim of increasing fitness of such inbred populations. The success of GR is known to depend on certain factors that may vary between different populations, such as their demographic history and distribution of dominance effects of mutations. While we understand the effects of these factors on the evolution of overall ancestry in the inbred population after GR, it is less clear what the effect is on local adaptations and their genetic architecture. To this end, we conduct a population genetic simulation study evaluating the effect of several different factors on the efficacy of GR including trait complexity (Mendelian vs. polygenic), dominance effects, and demographic history. We find that the effect on local adaptations depends highly on the mutational load at the time of GR, which is shaped dynamically by interactions between demographic history and dominance effects of deleterious variation. While local adaptations are generally restored post-GR in the long run, in the short term they are often compromised in the process of purging deleterious variation. We also show that while local adaptations are almost always fully restored, the degree to which ancestral genetic variation comprising the trait is replaced by donor variation can vary drastically, and is especially high for complex traits. Our results provide considerations for practical GR and its effects on trait evolution.

Summary We present a full-likelihood method to infer polygenic adaptation from DNA sequence variation and GWAS summary statistics to quantify recent transient directional selection acting on a complex trait. Through simulations of polygenic trait architecture evolution and GWASs, we show the method substantially improves power over current methods. We examine the robustness of the method under stratification, uncertainty and bias in marginal effects, uncertainty in the causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, controlling for pleiotropy even among traits with strong genetic correlation ( | r g | = 80 % ) while retaining high power to attribute selection to the causal trait. When the causal trait is excluded from analysis, selection is attributed to its closest proxy. We discuss limitations of the method, cautioning against strongly causal interpretations of the results, and the possibility of undetectable gene-by-environment (GxE) interactions. We apply the method to 56 human polygenic traits, revealing signals of directional selection on pigmentation, life history, glycated hemoglobin (HbA1c), and other traits. We also conduct joint testing of 137 pairs of genetically correlated traits, revealing widespread correlated response acting on these traits (2.6-fold enrichment, p = 1.5 × 10−7). Signs of selection on some traits previously reported as adaptive (e.g., educational attainment and hair color) are largely attributable to correlated response (p = 2.9 × 10−6 and 1.7 × 10−4, respectively). Lastly, our joint test shows antagonistic selection has increased type 2 diabetes risk and decrease HbA1c (p = 1.5 × 10−5).

We present a full-likelihood method to estimate and quantify polygenic adaptation from contemporary DNA sequence data. The method combines population genetic DNA sequence data and GWAS summary statistics from up to thousands of nucleotide sites in a joint likelihood function to estimate the strength of transient directional selection acting on a polygenic trait. Through population genetic simulations of polygenic trait architectures and GWAS, we show that the method substantially improves power over current methods. We examine the robustness of the method under uncorrected GWAS stratification, uncertainty and ascertainment bias in the GWAS estimates of SNP effects, uncertainty in the identification of causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, fully controlling for pleiotropy even among traits with strong genetic correlation (|rg| = 80%; c.f. schizophrenia and bipolar disorder) while retaining high power to attribute selection to the causal trait. We apply the method to study 56 human polygenic traits for signs of recent adaptation. We find signals of directional selection on pigmentation (tanning, sunburn, hair, P=5.5e-15, 1.1e-11, 2.2e-6, respectively), life history traits (age at first birth, EduYears, P=2.5e-4, 2.6e-4, respectively), glycated hemoglobin (HbA1c, P=1.2e-3), bone mineral density (P=1.1e-3), and neuroticism (P=5.5e-3). We also conduct joint testing of 137 pairs of genetically correlated traits. We find evidence of widespread correlated response acting on these traits (2.6-fold enrichment over the null expectation, P=1.5e-7). We find that for several traits previously reported as adaptive, such as educational attainment and hair color, a significant proportion of the signal of selection on these traits can be attributed to correlated response, vs direct selection (P=2.9e-6, 1.7e-4, respectively). Lastly, our joint test uncovers antagonistic selection that has acted to increase type 2 diabetes (T2D) risk and decrease HbA1c (P=1.5e-5).

The Shine–Dalgarno (SD) sequence motif is frequently found upstream of protein coding genes and is thought to be the dominant mechanism of translation initiation used by bacteria. Experimental studies have shown that the SD sequence facilitates start codon recognition and enhances translation initiation by directly interacting with the highly conserved anti-SD sequence on the 30S ribosomal subunit. However, the proportion of SD-led genes within a genome varies across species and the factors governing this variation in translation initiation mechanisms remain largely unknown. Here, we conduct a phylogenetically informed analysis and find that species capable of rapid growth contain a higher proportion of SD-led genes throughout their genomes. We show that SD sequence utilization covaries with a suite of genomic features that are important for efficient translation initiation and elongation. In addition to these endogenous genomic factors, we further show that exogenous environmental factors may influence the evolution of translation initiation mechanisms by finding that thermophilic species contain significantly more SD-led genes than mesophiles. Our results demonstrate that variation in translation initiation mechanisms across bacterial species is predictable and is a consequence of differential life-history strategies related to maximum growth rate and environmental-specific constraints.

Summary Phenotypic plasticity has been hypothesized to precede and facilitate adaptation to novel environments [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ], but examples of plasticity preceding adaptation in wild populations are rare (but see [ 9 , 10 ]). We studied a population of side-blotched lizards, Uta stansburiana, living on a lava flow that formed 22,500 years ago [ 11 ] to understand the origin of their novel melanic phenotype that makes them cryptic on the black lava. We found that lizards living on and off of the lava flow exhibited phenotypic plasticity in coloration but also appeared to have heritable differences in pigmentation. We sequenced the exomes of 104 individuals and identified two known regulators of melanin production, PREP and PRKAR1A, which had markedly increased levels of divergence between lizards living on and off the lava flow. The derived variants in PREP and PRKAR1A were only found in the lava population and were associated with increased pigmentation levels in an experimental cohort of hatchling lizards. Simulations suggest that the derived variants in the PREP and PRKAR1A genes arose recently and were under strong positive selection in the lava population. Overall, our results suggest that ancestral plasticity for coloration facilitated initial survival in the lava environment and was followed by genetic changes that modified the phenotype in the direction of the induced plastic response, possibly through de novo mutations. These observations provide a detailed example supporting the hypothesis that plasticity aids in the initial colonization of a novel habitat, with natural selection subsequently refining the phenotype with genetic adaptations to the new environment. Video Abstract Download : Download video (6MB)

Understanding the physiology and genetics of human hypoxia tolerance has important medical implications, but this phenomenon has thus far only been investigated in high-altitude human populations. Another system, yet to be explored, is humans who engage in breath-hold diving. The indigenous Bajau people ("Sea Nomads") of Southeast Asia live a subsistence lifestyle based on breath-hold diving and are renowned for their extraordinary breath-holding abilities. However, it is unknown whether this has a genetic basis. Using a comparative genomic study, we show that natural selection on genetic variants in the PDE10A gene have increased spleen size in the Bajau, providing them with a larger reservoir of oxygenated red blood cells. We also find evidence of strong selection specific to the Bajau on BDKRB2, a gene affecting the human diving reflex. Thus, the Bajau, and possibly other diving populations, provide a new opportunity to study human adaptation to hypoxia tolerance. VIDEO ABSTRACT.

Most current methods for detecting natural selection from DNA sequence data are limited in that they are either based on summary statistics or a composite likelihood, and as a consequence, do not make full use of the information available in DNA sequence data. We here present a new importance sampling approach for approximating the full likelihood function for the selection coefficient. Our method CLUES treats the ancestral recombination graph (ARG) as a latent variable that is integrated out using previously published Markov Chain Monte Carlo (MCMC) methods. The method can be used for detecting selection, estimating selection coefficients, testing models of changes in the strength of selection, estimating the time of the start of a selective sweep, and for inferring the allele frequency trajectory of a selected or neutral allele. We perform extensive simulations to evaluate the method and show that it uniformly improves power to detect selection compared to current popular methods such as nSL and SDS, and can provide reliable inferences of allele frequency trajectories under many conditions. We also explore the potential of our method to detect extremely recent changes in the strength of selection. We use the method to infer the past allele frequency trajectory for a lactase persistence SNP (MCM6) in Europeans. We also infer the trajectory of a SNP (EDAR) in Han Chinese, finding evidence that this allele’s age is much older than previously claimed. We also study a set of 11 pigmentation-associated variants. Several genes show evidence of strong selection particularly within the last 5,000 years, including ASIP, KITLG, and TYR. However, selection on OCA2/HERC2 seems to be much older and, in contrast to previous claims, we find no evidence of selection on TYRP1., Author summary Current methods to study natural selection using modern population genomic data are limited in their power and flexibility. Here, we present a new method to infer natural selection that builds on recent methodological advances in estimating genome-wide genealogies. By using importance sampling we are able to efficiently estimate the likelihood function of the selection coefficient. We show our method improves power to test for selection over competing methods across a diverse range of scenarios, and also accurately infers the selection coefficient. We also demonstrate a novel capability of our model, using it to infer the allele’s frequency over time. We validate these results with a study of a lactase persistence SNP in Europeans, and also study a SNP at EDAR, as well as a set of 11 pigmentation-associated variants.