Аннотация
A central part of population genomics consists of finding genomic regions
implicated in local adaptation. Population genomic analyses are based on
genotyping numerous molecular markers and looking for outlier loci in terms of
patterns of genetic differentiation. One of the most common approach for
selection scan is based on statistics that measure population differentiation
such as $F_ST$. However they are important caveats with approaches related to
$F_ST$ 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. Using hierarchical Bayesian modeling, we both infer population
structure and identify outlier loci that are candidates for local adaptation.
Factor models are strongly related to principal components analysis (PCA) and
they model population structure with latent variables called factors. The
hierarchical factor model considers that outlier loci are atypically explained
by one of the factors. In a model of population divergence, we show that it can
achieve a 2-fold or more reduction of false discovery rate compared to the
software BayeScan or compared to a $F_ST$ approach. We show that our software
can handle large SNP datasets by analyzing the HGDP SNP dataset. The Bayesian
factor model is implemented in the command-line PCAdapt software.
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