Detecting recent positive selection with high accuracy and reliability by conditional coalescent tree

A highland-adaptation variant near MCUR1 reduces its transcription and attenuates erythrogenesis in Tibetans

To identify genomic regions subject to positive selection that might contain genes involved in high-altitude adaptation (HAA), we performed a genome-wide scan by whole-genome sequencing of Tibetan highlanders and Han lowlanders. We revealed a collection of candidate genes located in 30 genomic loci under positive selection. Among them, MCUR1 at 6p23 was a novel pronounced candidate. By single-cell RNA sequencing and comprehensive functional studies, we demonstrated that MCUR1 depletion leads to impairment of erythropoiesis under hypoxia and normoxia. Mechanistically, MCUR1 knockdown reduced mitochondrial Ca2+ uptake and then concomitantly increased cytosolic Ca2+ levels, which thereby reduced erythropoiesis via the CAMKK2-AMPK-mTOR axis. Further, we revealed rs61644582 at 6p23 as an expression quantitative trait locus for MCUR1 and a functional variant that confers an allele-specific transcriptional regulation of MCUR1. Overall, MCUR1-mediated mitochondrial Ca2+ homeostasis is highlighted as a novel regulator of erythropoiesis, deepening our understanding of the genetic mechanism of HAA.

Modeling recent positive selection using identity-by-descent segments

Recent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments overlapping a locus. The statistical methods that we propose here address three major objectives in studying selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficient s. First, we implement a selection scan to locate regions with excess IBD rates. Second, we estimate the allele frequency and location of an unknown sweeping allele by aggregating over variants that are more abundant in an inferred outgroup with excess IBD rate versus the rest of the sample. Third, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are more precise at estimating s when s≥0.015. We also show that our 95% confidence intervals contain s in nearly 95% of our simulations. We apply these methods to study positive selection in European ancestry samples from the Trans-Omics for Precision Medicine project. We analyze eight loci where IBD rates are more than four standard deviations above the genome-wide median, including LCT where the maximum IBD rate is 35 standard deviations above the genome-wide median. Overall, we present robust and accurate approaches to study recent adaptive evolution without knowing the identity of the causal allele or using time series data.

Genomic imputation of ancient Asian populations contrasts local adaptation in pre- and post-agricultural Japan

Early modern humans lived as hunter-gatherers for millennia before agriculture, yet the genetic adaptations of these populations remain a mystery. Here, we investigate selection in the ancient hunter-gatherer-fisher Jomon and contrast pre- and post-agricultural adaptation in the Japanese archipelago. Building on the successful validation of imputation with ancient Asian genomes, we identify selection signatures in the Jomon, particularly robust signals from KITLG variants, which may have influenced dark pigmentation evolution. The Jomon lacks well-known adaptive variants (EDAR, ADH1B, and ALDH2), marking their emergence after the advent of farming in the archipelago. Notably, the EDAR and ADH1B variants were prevalent in the archipelago 1,300 years ago, whereas the ALDH2 variant could have emerged later due to its absence in other ancient genomes. Overall, our study underpins local adaptation unique to the Jomon population, which in turn sheds light on post-farming selection that continues to shape contemporary Asian populations.

Harnessing deep learning for population genetic inference

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.

Signatures of Convergent Evolution and Natural Selection at the Alcohol Dehydrogenase Gene Region are Correlated with Agriculture in Ethnically Diverse Africans

The alcohol dehydrogenase (ADH) family of genes encodes enzymes that catalyze the metabolism of ethanol into acetaldehyde. Nucleotide variation in ADH genes can affect the catalytic properties of these enzymes and is associated with a variety of traits, including alcoholism and cancer. Some ADH variants, including the ADH1B*48His (rs1229984) mutation in the ADH1B gene, reduce the risk of alcoholism and are under positive selection in multiple human populations. The advent of Neolithic agriculture and associated increase in fermented foods and beverages is hypothesized to have been a selective force acting on such variants. However, this hypothesis has not been tested in populations outside of Asia. Here, we use genome-wide selection scans to show that the ADH gene region is enriched for variants showing strong signals of positive selection in multiple Afroasiatic-speaking, agriculturalist populations from Ethiopia, and that this signal is unique among sub-Saharan Africans. We also observe strong selection signals at putatively functional variants in nearby lipid metabolism genes, which may influence evolutionary dynamics at the ADH region. Finally, we show that haplotypes carrying these selected variants were introduced into Northeast Africa from a West-Eurasian source within the last ∼2,000 years and experienced positive selection following admixture. These selection signals are not evident in nearby, genetically similar populations that practice hunting/gathering or pastoralist subsistence lifestyles, supporting the hypothesis that the emergence of agriculture shapes patterns of selection at ADH genes. Together, these results enhance our understanding of how adaptations to diverse environments and diets have influenced the African genomic landscape.

An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes

Characterizing the population history of a species and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The constant improvement of high-throughput sequencing techniques has facilitated the production of whole genome data in a wide range of species. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a profusion of analytical tools that can confuse and discourage users. Such confusion limits the amount of information effectively retrieved from complex genomic data sets, and impairs the diffusion of the most recent analytical tools into fields such as conservation biology. It may also lead to redundancy among methods. To address these isssues, we propose an overview of more than 100 state-of-the-art methods that can deal with whole genome data. We summarize the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.

Cytokines mapping for tissue-specific expression, eQTLs and GWAS traits

Dysregulation in cytokine production has been linked to the pathogenesis of various immune-mediated traits, in which genetic variability contributes to the etiopathogenesis. GWA studies have identified many genetic variants in or near cytokine genes, nonetheless, the translation of these findings into knowledge of functional determinants of complex traits remains a fundamental challenge. In this study we aimed at collection, analysis and interpretation of data on cytokines focused on their tissue-specific expression, eQTLs and GWAS traits. Using GO annotations, we generated a list of 314 cytokines and analyzed them with the GTEx resource. Cytokines were highly tissue-specific, 82.3% of cytokines had Tau expression metrics ≥ 0.8. In total, 3077 associations for 1760 unique SNPs in or near 244 cytokines were mapped in the NHGRI-EBI GWAS Catalog. According to the Experimental Factor Ontology resource, the largest numbers of disease associations were related to 'Inflammatory disease', 'Immune system disease' and 'Asthma'. The GTEx-based analysis revealed that among GWAS SNPs, 1142 SNPs had eQTL effects and influenced expression levels of 999 eGenes, among them 178 cytokines. Several types of enrichment analysis showed that it was cytokines expression variability that fundamentally contributed to the molecular origins of considered immune-mediated conditions.

Identifying adaptive alleles in the human genome: from selection mapping to functional validation

The suite of phenotypic diversity across geographically distributed human populations is the outcome of genetic drift, gene flow, and natural selection throughout human evolution. Human genetic variation underlying local biological adaptations to selective pressures is incompletely characterized. With the emergence of population genetics modeling of large-scale genomic data derived from diverse populations, scientists are able to map signatures of natural selection in the genome in a process known as selection mapping. Inferred selection signals further can be used to identify candidate functional alleles that underlie putative adaptive phenotypes. Phenotypic association, fine mapping, and functional experiments facilitate the identification of candidate adaptive alleles. Functional investigation of candidate adaptive variation using novel techniques in molecular biology is slowly beginning to unravel how selection signals translate to changes in biology that underlie the phenotypic spectrum of our species. In addition to informing evolutionary hypotheses of adaptation, the discovery and functional annotation of adaptive alleles also may be of clinical significance. While selection mapping efforts in non-European populations are growing, there remains a stark under-representation of diverse human populations in current public genomic databases, of both clinical and non-clinical cohorts. This lack of inclusion limits the study of human biological variation. Identifying and functionally validating candidate adaptive alleles in more global populations is necessary for understanding basic human biology and human disease.

Whole genome re-sequencing reveals recent signatures of selection in three strains of farmed Nile tilapia (Oreochromis niloticus)

Nile tilapia belongs to the second most cultivated group of fish in the world, mainly because of its favorable characteristics for production. Genetic improvement programs and domestication process of Nile tilapia may have modified the genome through selective pressure, leaving signals that can be detected at the molecular level. In this work, signatures of selection were identified using genome-wide SNP data, by two haplotype-based (iHS and Rsb) and one F_ST based method. Whole-genome re-sequencing of 326 individuals from three strains (A, B and C) of farmed tilapia maintained in Brazil and Costa Rica was carried out using Illumina HiSeq 2500 technology. After applying conventional SNP-calling and quality-control filters, ~ 1.3 M high-quality SNPs were inferred and used as input for the iHS, Rsb and F_ST based methods. We detected several candidate genes putatively subjected to selection in each strain. A considerable number of these genes are associated with growth (e.g. NCAPG, KLF3, TBC1D1, TTN), early development (e.g. FGFR3, PFKFB3), and immunity traits (e.g. NLRC3, PIGR, MAP1S). These candidate genes represent putative genomic landmarks that could be associated to traits of biological and commercial interest in farmed Nile tilapia.

Parallel and nonparallel genomic responses contribute to herbicide resistance in Ipomoea purpurea, a common agricultural weed

The repeated evolution of herbicide resistance has been cited as an example of genetic parallelism, wherein separate species or genetic lineages utilize the same genetic solution in response to selection. However, most studies that investigate the genetic basis of herbicide resistance examine the potential for changes in the protein targeted by the herbicide rather than considering genome-wide changes. We used a population genomics screen and targeted exome re-sequencing to uncover the potential genetic basis of glyphosate resistance in the common morning glory, Ipomoea purpurea, and to determine if genetic parallelism underlies the repeated evolution of resistance across replicate resistant populations. We found no evidence for changes in 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), glyphosate's target protein, that were associated with resistance, and instead identified five genomic regions that showed evidence of selection. Within these regions, genes involved in herbicide detoxification-cytochrome P450s, ABC transporters, and glycosyltransferases-are enriched and exhibit signs of selective sweeps. One region under selection shows parallel changes across all assayed resistant populations whereas other regions exhibit signs of divergence. Thus, while it appears that the physiological mechanism of resistance in this species is likely the same among resistant populations, we find patterns of both similar and divergent selection across separate resistant populations at particular loci.

Identifying the favored mutation in a positive selective sweep

Most approaches that capture signatures of selective sweeps in population genomics data do not identify the specific mutation favored by selection. We present iSAFE (for "integrated selection of allele favored by evolution"), a method that enables researchers to accurately pinpoint the favored mutation in a large region (∼5 Mbp) by using a statistic derived solely from population genetics signals. iSAFE does not require knowledge of demography, the phenotype under selection, or functional annotations of mutations.

ADH1B: From alcoholism, natural selection, and cancer to the human phenome

The ADH1B (Alcohol Dehydrogenase 1B (class I), Beta Polypeptide) gene and its best-known functional alleles, Arg48His (rs1229984, ADH1B*2) and Arg370Cys (rs2066702, ADH1B*3), have been investigated in relation to many phenotypic traits; most frequently including alcohol metabolism and alcohol drinking behaviors, but also human evolution, liver function, cancer, and, recently, the comprehensive human phenome. To understand ADH1B functions and consequences, we provide here a bioinformatic analysis of its gene regulation and molecular functions, literature review of studies focused on this gene, and a discussion regarding future research perspectives. Certain ADH1B alleles have large effects on alcohol metabolism, and this relationship particularly encourages further investigations in relation to alcoholism and alcohol-associated cancer to understand better the mechanisms by which alcohol metabolism contributes to alcohol abuse and carcinogenesis. We also observed that ADH1B has complex mechanisms that regulate its expression across multiple human tissues, and these may be involved in cardiac and metabolic traits. Evolutionary data strongly suggest that the selection signatures at the ADH1B locus are primarily related to effects other than those on alcohol metabolism. This is also supported by the involvement of ADH1B in multiple molecular pathways and by the findings of our recent phenome-wide association study. Accordingly, future studies should also investigate other functions of ADH1B potentially relevant for the human phenome. © 2017 Wiley Periodicals, Inc.

A missense mutation in TCN2 is associated with decreased risk for congenital heart defects and may increase cellular uptake of vitamin B12 via Megalin

Deregulation of folate and vitamin B12 (VB12) metabolism contributes to the risk of congenital heart defects (CHDs). Transcobalamin (TCN2) is essential for transporting VB12 from blood to cells as TCN2-bound VB12 (holo-TC) is the only form for somatic cellular uptake. In this study, we performed an association study between common polymorphisms in 46 one carbon metabolism genes and CHD in 412 CHDs and 213 controls. Only two significant association signals in coding regions were identified: FTCD c.1470C>T & TCN2 c.230A>T. The only missense mutation, TCN2 c.230A>T, was further validated in 412 CHDs and 1177 controls. TCN2 c.230T is significantly associated with reduced CHD risk in North Chinese (odds ratio = 0.67, P = 4.62e-05), compared with the 230A allele. Interestingly, the mean level of plasma holo-TC in women with the TA genotype was 1.77-fold higher than that in women with the AA genotype. Further analysis suggested that c.230A>T enhanced the cellular uptake of holo-TC via the LRP2 receptor. Our results determined that a functional polymorphism in TCN2 contributes to the prevalence of CHDs. TCN2 c.230A>T is significantly associated with a reduced CHD risk, likely due to TCN2 c.230T improving the interaction between holo-TC and its LRP2 receptor.