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Genome scans for detecting footprints of local adaptation using a Bayesian factor model.

ABSTRACT: There is a considerable impetus in population genomics to pinpoint loci involved in local adaptation. A powerful approach to find genomic regions subject to local adaptation is to genotype numerous molecular markers and look for outlier loci. One of the most common approaches for selection scans is based on statistics that measure population differentiation such as FST. However, there are important caveats with approaches related to FST because they require grouping individuals into populations and they additionally assume a particular model of population structure. Here, we implement a more flexible individual-based approach based on Bayesian factor models. Factor models capture population structure with latent variables called factors, which can describe clustering of individuals into populations or isolation-by-distance patterns. Using hierarchical Bayesian modeling, we both infer population structure and identify outlier loci that are candidates for local adaptation. In order to identify outlier loci, the hierarchical factor model searches for loci that are atypically related to population structure as measured by the latent factors. In a model of population divergence, we show that it can achieve a 2-fold or more reduction of false discovery rate compared with the software BayeScan or with an FST approach. We show that our software can handle large data sets by analyzing the single nucleotide polymorphisms of the Human Genome Diversity Project. The Bayesian factor model is implemented in the open-source PCAdapt software.

SUBMITTER: Duforet-Frebourg N

PROVIDER: S-EPMC4137708 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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Genome scans for detecting footprints of local adaptation using a Bayesian factor model.

Duforet-Frebourg Nicolas N Bazin Eric E Blum Michael G B MG

Molecular biology and evolution 20140603 9

There is a considerable impetus in population genomics to pinpoint loci involved in local adaptation. A powerful approach to find genomic regions subject to local adaptation is to genotype numerous molecular markers and look for outlier loci. One of the most common approaches for selection scans is based on statistics that measure population differentiation such as FST. However, there are important caveats with approaches related to FST because they require grouping individuals into populations ...[more]

PMID: 24899666

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Project description:The study of local adaptation in the presence of ongoing gene flow is the study of natural selection in action, revealing the functional genetic diversity most relevant to contemporary pressures. In addition to individual genes, genome-wide architecture can itself evolve to enable adaptation. Distributed across a steep thermal gradient along the east coast of North America, Atlantic silversides (Menidia menidia) exhibit an extraordinary degree of local adaptation in a suite of traits, and the capacity for rapid adaptation from standing genetic variation, but we know little about the patterns of genomic variation across the species range that enable this remarkable adaptability. Here, we use low-coverage, whole-transcriptome sequencing of Atlantic silversides sampled along an environmental cline to show marked signatures of divergent selection across a gradient of neutral differentiation. Atlantic silversides sampled across 1371 km of the southern section of its distribution have very low genome-wide differentiation (median F ST = 0.006 across 1.9 million variants), consistent with historical connectivity and observations of recent migrants. Yet almost 14,000 single nucleotide polymorphisms (SNPs) are nearly fixed (F ST > 0.95) for alternate alleles. Highly differentiated SNPs cluster into four tight linkage disequilibrium (LD) blocks that span hundreds of genes and several megabases. Variants in these LD blocks are disproportionately nonsynonymous and concentrated in genes enriched for multiple functions related to known adaptations in silversides, including variation in lipid storage, metabolic rate, and spawning behavior. Elevated levels of absolute divergence and demographic modeling suggest selection maintaining divergence across these blocks under gene flow. These findings represent an extreme case of heterogeneity in levels of differentiation across the genome, and highlight how gene flow shapes genomic architecture in continuous populations. Locally adapted alleles may be common features of populations distributed along environmental gradients, and will likely be key to conserving variation to enable future responses to environmental change.

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Genome scans for detecting footprints of local adaptation using a Bayesian factor model.

Publications

Genome scans for detecting footprints of local adaptation using a Bayesian factor model.

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