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74 results for “Genome scans”

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Data from: The coupling hypothesis: why genome scans may fail to map local adaptation genes

Genomic scans of multiple populations often reveal marker loci with greatly increased differentiation between populations. Often this differentiation coincides in space with contrasts in ecological factors, forming a genetic–environment association (GEA). GEAs imply a role for local adaptation, and so it is tempting to conclude that the strongly differentiated markers are themselves under ecologically based divergent selection, or are closely linked to loci under such selection. Here, we highlight an alternative and neglected explanation: intrinsic (i.e. environment-independent) pre- or post-zygotic genetic incompatibilities rather than local adaptation can be responsible for increased differentiation. Intrinsic genetic incompatibilities create endogenous barriers to gene flow, also known as tension zones, whose location can shift over time. However, tension zones have a tendency to become trapped by, and therefore to coincide with, exogenous barriers due to ecological selection. This coupling of endogenous and exogenous barriers can occur easily in spatially subdivided populations, even if the loci involved are unlinked. The result is that local adaptation explains where genetic breaks are positioned, but not necessarily their existence, which can be best explained by endogenous incompatibilities. More precisely, we show that (i) the coupling of endogenous and exogenous barriers can easily occur even when ecological selection is weak; (ii) when environmental heterogeneity is fine-grained, GEAs can emerge at incompatibility loci, but only locally, in places where habitats and gene pools are sufficiently intermingled to maintain linkage disequilibria between genetic incompatibilities, local-adaptation genes and neutral loci. Furthermore, the association between the locally adapted and intrinsically incompatible alleles (i.e. the sign of linkage disequilibrium between endogenous and exogenous loci) is arbitrary and can form in either direction. Reviewing results from the literature, we find that many predictions of our model are supported, including endogenous genetic barriers that coincide with environmental boundaries, local GEA in mosaic hybrid zones, and inverted or modified GEAs at distant locations. We argue that endogenous genetic barriers are often more likely than local adaptation to explain the majority of Fst-outlying loci observed in genome scan approaches – even when these are correlated to environmental variables.

opencc-zeroDec 2010View details →

dryad28/100

Data from: Genome scans reveal candidate regions involved in the adaptation to host plant in the pea aphid complex

A major goal in evolutionary biology is to uncover the genetic basis of adaptation. Divergent selection exerted on ecological traits may result in adaptive population differentiation and reproductive isolation and affect differentially the level of genetic divergence along the genome. Genome-wide scan of large sets of individuals from multiple populations is a powerful approach to identify loci or genomic regions under ecologically divergent selection. Here, we focused on the pea aphid, a species complex of divergent host races, to explore the organization of the genomic divergence associated with host-plant adaptation and ecological speciation. We analyzed 390 microsatellite markers located at variable distances from predicted genes in replicate samples of sympatric populations of the pea aphid collected on alfalfa, red clover and pea, which correspond to three common host-adapted races reported in this species complex. Using a method that accounts for the hierarchical structure of our dataset, we found a set of 11 outlier loci that show higher genetic differentiation between host races than expected under the null hypothesis of neutral evolution. Two of the outliers are close to olfactory receptor genes and three other nearby genes encoding salivary proteins. The remaining outliers are located in regions with genes of unknown functions, or which functions are unlikely to be involved in interactions with the host plant. This study reveals genetic signatures of divergent selection across the genome and provides an inventory of candidate genes responsible for plant specialization in the pea aphid, thereby setting the stage for future functional studies.

opencc-zeroDec 2011View details →

dryad28/100

Data from: Genome scan reveals selection acting on genes linked to stress response in wild pearl millet

Uncovering genomic regions involved in adaption is a major goal in evolutionary biology. High-throughput sequencing now makes it possible to tackle this challenge in nonmodel species. Yet, despite the increasing number of methods targeted to specifically detect genomic footprints of selection, the complex demography of natural populations often causes high rates of false positive in gene discoveries. The aim of this study was to identify climate adaptations in wild pearl millet populations, Cenchrus americanus ssp. monodii. We focused on two climate gradients, one in Mali and one in Niger. We used a two-step strategy to limit false-positive outliers. First, we considered gradients as biological replicates and performed RNA sequencing of four populations at the extremities. We combined four methods—three based on differentiation among populations and one based on diversity patterns within populations—to identify outlier SNPs from a set of 87 218 high-quality SNPs. Among 11 155 contigs of pearl millet reference transcriptome, 540 exhibited selection signals as evidenced by at least one of the four methods. In a second step, we genotyped 762 samples in 11 additional populations distributed along the gradients using SNPs from the detected contigs and random SNPs as control. We further assessed selection on this large data set using a differentiation-based method and a method based on correlations with environmental variables based. Four contigs displayed consistent signatures between the four extreme and 11 additional populations, two of which were linked to abiotic and biotic stress responses.

opencc-zeroDec 2015View details →

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Data from: A new method to scan genomes for introgression in a secondary contact model

Secondary contact between divergent populations or incipient species may result in the exchange and introgression of genomic material. We develop a simple DNA sequence measure, called Gmin, which is designed to identify genomic regions experiencing introgression in a secondary contact model. Gmin is defined as the ratio of the minimum between-population number of nucleotide differences in a genomic window to the average number of between-population differences. Although it is conceptually simple, one advantage of Gmin is that it is computationally inexpensive relative to model-based methods for detecting gene flow and it scales easily to the level of whole-genome analysis. We compare the sensitivity and specificity of Gmin to those of the widely used index of population differentiation, FST, and suggest a simple statistical test for identifying genomic outliers. Extensive computer simulations demonstrate that Gmin has both greater sensitivity and specificity for detecting recent introgression than does FST. Furthermore, we find that the sensitivity of Gmin is robust with respect to both the population mutation and recombination rates. Finally, a scan of Gmin across the X chromosome of Drosophila melanogaster identifies candidate regions of introgression between sub-Saharan African and cosmopolitan populations that were previously missed by other methods. These results show that Gmin is a biologically straightforward, yet powerful, alternative to FST, as well as to more computationally intensive model-based methods for detecting gene flow.

opencc-zeroDec 2014View details →

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Data from: The relative power of genome scans to detect local adaptation depends on sampling design and statistical method

Although genome scans have become a popular approach towards understanding the genetic basis of local adaptation, the field still does not have a firm grasp on how sampling design and demographic history affect the performance of genome scans on complex landscapes. To explore these issues, we compared 20 different sampling designs in equilibrium (i.e. island model and isolation by distance) and nonequilibrium (i.e. range expansion from one or two refugia) demographic histories in spatially heterogeneous environments. We simulated spatially complex landscapes, which allowed us to exploit local maxima and minima in the environment in 'pair' and 'transect' sampling strategies. We compared FST outlier and genetic–environment association (GEA) methods for each of two approaches that control for population structure: with a covariance matrix or with latent factors. We show that while the relative power of two methods in the same category (FST or GEA) depended largely on the number of individuals sampled, overall GEA tests had higher power in the island model and FST had higher power under isolation by distance. In the refugia models, however, these methods varied in their power to detect local adaptation at weakly selected loci. At weakly selected loci, paired sampling designs had equal or higher power than transect or random designs to detect local adaptation. Our results can inform sampling designs for studies of local adaptation and have important implications for the interpretation of genome scans based on landscape data.

opencc-zeroDec 2014View details →

dryad28/100

Data from: pcadapt: an R package to perform genome scans for selection based on principal component analysis

The R package pcadapt performs genome scans to detect genes under selection based on population genomic data. It assumes that candidate markers are outliers with respect to how they are related to population structure. Because population structure is ascertained with principal component analysis, the package is fast and works with large-scale data. It can handle missing data and pooled sequencing data. By contrast to population-based approaches, the package handle admixed individuals and does not require grouping individuals into populations. Since its first release, pcadapt has evolved in terms of both statistical approach and software implementation. We present results obtained with robust Mahalanobis distance, which is a new statistic for genome scans available in the 2.0 and later versions of the package. When hierarchical population structure occurs, Mahalanobis distance is more powerful than the communality statistic that was implemented in the first version of the package. Using simulated data, we compare pcadapt to other computer programs for genome scans (BayeScan, hapflk, OutFLANK, sNMF). We find that the proportion of false discoveries is around a nominal false discovery rate set at 10% with the exception of BayeScan that generates 40% of false discoveries. We also find that the power of BayeScan is severely impacted by the presence of admixed individuals whereas pcadapt is not impacted. Last, we find that pcadapt and hapflk are the most powerful in scenarios of population divergence and range expansion. Because pcadapt handles next-generation sequencing data, it is a valuable tool for data analysis in molecular ecology.

opencc-zeroDec 2015View details →

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