Search

The relative contribution of selection and neutrality in shaping species genetic diversity is one of the most central and controversial questions in evolutionary theory. Genomic data provide growing evidence that linked selection, i.e. the modification of genetic diversity at neutral sites through linkage with selected sites, might be pervasive over the genome. Several studies proposed that linked selection could be modeled as first approximation by a local reduction (e.g. purifying selection, selective sweeps) or increase (e.g. balancing selection) of effective population size (Ne). At the genome-wide scale, this leads to variations of Ne from one region to another, reflecting the heterogeneity of selective constraints and recombination rates between regions. We investigate here the consequences of such genomic variations of Ne on the genome-wide distribution of coalescence times. The underlying motivation concerns the impact of linked selection on demographic inference, because the distribution of coalescence times is at the heart of several important demographic inference approaches. Using the concept of inverse instantaneous coalescence rate, we demonstrate that in a panmictic population, linked selection always results in a spurious apparent decrease of Ne along time. Balancing selection has a particularly large effect, even when it concerns a very small part of the genome. We also study more general models including genuine population size changes, population structure or transient selection and find that the effect of linked selection can be significantly reduced by that of population structure. The models and conclusions presented here are also relevant to the study of other biological processes generating apparent variations of Ne along the genome. [ABSTRACT FROM AUTHOR]

Detecting the molecular basis of adaptation is one of the major questions in population genetics. With the advance in sequencing technologies, nearly complete interrogation of genome-wide polymorphisms in multiple populations is becoming feasible in some species, with the expectation that it will extend quickly to new ones. Here, we investigate the advantages of sequencing for the detection of adaptive loci in multiple populations, exploiting a recently published data set in cattle (Bos taurus). We used two different approaches to detect statistically significant signals of positive selection: a within-population approach aimed at identifying hard selective sweeps and a population-differentiation approach that can capture other selection events such as soft or incomplete sweeps. We show that the two methods are complementary in that they indeed capture different kinds of selection signatures. Our study confirmed some of the well-known adaptive loci in cattle (e.g., MC1R, KIT, GHR, PLAG1, NCAPG/LCORL) and detected some new ones (e.g., ARL15, PRLR, CYP19A1, PPM1L). Compared to genome scans based on medium- or high-density SNP data, we found that sequencing offered an increased detection power and a higher resolution in the localization of selection signatures. In several cases, we could even pinpoint the underlying causal adaptive mutation or at least a very small number of possible candidates (e.g., MC1R, PLAG1). Our results on these candidates suggest that a vast majority of adaptive mutations are likely to be regulatory rather than protein-coding variants. [ABSTRACT FROM AUTHOR]

Medicago truncatula is a model legume species used to investigate plant–microorganism interactions, notably root symbioses. Massive population genomic and transcriptomic data now available for this species open the way for a comprehensive investigation of genomic variations associated with adaptation of M. truncatula to its environment. Here we performed a fine-scale genome scan of selective sweep signatures in M. truncatula using more than 15 million single nucleotide polymorphisms identified on 283 accessions from two populations (Circum and Far West), and exploited annotation and published transcriptomic data to identify biological processes associated with molecular adaptation. We identified 58 swept genomic regions with a 15 kb average length and comprising 3.3 gene models on average. The unimodal sweep state probability distribution in these regions enabled us to focus on the best single candidate gene per region. We detected two unambiguous species-wide selective sweeps, one of which appears to underliemorphological adaptation. Population genomic analyses of the remaining 56 sweep signatures indicate that sweeps identified in the Far West population are less population-specific and probably more ancient than those identified in the Circum population. Functional annotation revealed a predominance of immunity-related adaptations in the Circum population. Transcriptomic data from accessions of the Far West population allowed inference of four clusters of coregulated genes putatively involved in the adaptive control of symbiotic carbon flow and nodule senescence, as well as in other root adaptations upon infection with soil microorganisms. We demonstrate that molecular adaptations in M. truncatula were primarily triggered by selective pressures from root-associated microorganisms. [ABSTRACT FROM AUTHOR]

The detection of molecular signatures of selection is one of the major concerns of modern population genetics. A widely used strategy in this context is to compare samples from several populations and to look for genomic regions with outstanding genetic differentiation between these populations. Genetic differentiation is generally based on allele frequency differences between populations, which are measured by FST or related statistics. Here we introduce a new statistic, denoted hapFLK, which focuses instead on the differences of haplotype frequencies between populations. In contrast to most existing statistics, hapFLK accounts for the hierarchical structure of the sampled populations. Using computer simulations, we show that each of these two features--the use of haplotype information and of the hierarchical structure of populations--significantly improves the detection power of selected loci and that combining them in the hapFLK statistic provides even greater power. We also show that hapFLK is robust with respect to bottlenecks and migration and improves over existing approaches in many situations. Finally, we apply hapFLK to a set of six sheep breeds from Northern Europe and identify seven regions under selection, which include already reported regions but also several new ones. We propose a method to help identifying the population(s) under selection in a detected region, which reveals that in many of these regions selection most likely occurred in more than one population. Furthermore, several of the detected regions correspond to incomplete sweeps, where the favorable haplotype is only at intermediate frequency in the population(s) under selection. [ABSTRACT FROM AUTHOR]

Due to its cost effectiveness, next-generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for characterizing variation in population samples. Because Pool-Seq provides genome-wide SNP frequency data, it is possible to use them for demographic inference and/or the identification of selective sweeps. Here, we introduce a statistical method that is designed to detect selective sweeps from pooled data by accounting for statistical challenges associated with Pool-Seq, namely sequencing errors and random sampling among chromosomes. This allows for an efficient use of the information: all base calls are included in the analysis, but the higher credibility of regions with higher coverage and base calls with better quality scores is accounted for. Computer simulations show that our method efficiently detects sweeps even at very low coverage (0.5× per chromosome). Indeed, the power of detecting sweeps is similar to what we could expect from sequences of individual chromosomes. Since the inference of selective sweeps is based on the allele frequency spectrum (AFS), we also provide a method to accurately estimate the AFS provided that the quality scores for the sequence reads are reliable. Applying our approach to Pool-Seq data from Drosophila melanogaster, we identify several selective sweep signatures on chromosome X that include some previously well-characterized sweeps like the wapl region. [ABSTRACT FROM PUBLISHER]

Detecting and localizing selective sweeps on the basis of SNP data has recently received considerable attention. Here we introduce the use of hidden Markov models (HMMs) for the detection of selective sweeps in DNA sequences. Like previously published methods, our HMMs use the site frequency spectrum, and the spatial pattern of diversity along the sequence, to identify selection. In contrast to earlier approaches, our HMMs explicitly model the correlation structure between linked sites. The detection power of our methods, and their accuracy for estimating the selected site location, is similar to that of competing methods for constant size populations. In the case of population bottlenecks, however, our methods frequently showed fewer false positives. [ABSTRACT FROM AUTHOR]

Detecting genomic regions under selection is an important objective of population genetics. Typical analyses for this goal are based on exploiting genetic diversity patterns in present time data but rapid advances in DNA sequencing have increased the availability of time series genomic data. A common approach to analyze such data is to model the temporal evolution of an allele frequency as a Markov chain. Based on this principle, several methods have been proposed to infer selection intensity. One of their differences lies in how they model the transition probabilities of the Markov chain. Using the Wright- Fisher model is a natural choice but its computational cost is prohibitive for large population sizes so approximations to this model based on parametric distributions have been proposed. Here, we compared the performance of some of these approximations with respect to their power to detect selection and their estimation of the selection coefficient. We developped a new generic Hidden Markov Model likelihood calculator and applied it on genetic time series simulated under various evolutionary scenarios. The Beta with spikes approximation, which combines discrete fixation probabilities with a continuous Beta distribution, was found to perform consistently better than the others. This distribution provides an almost perfect fit to the Wright-Fisher model in terms of selection inference, for a computational cost that does not increase with population size. We further evaluated this model for population sizes not accessible to the Wright-Fisher model and illustrated its performance on a dataset of two divergently selected chicken populations. [ABSTRACT FROM AUTHOR]