%0 Generic %1 duforetfrebourg2014genome %A Duforet-Frebourg, N. %A Bazin, E. %A Blum, M. G. B. %D 2014 %K PCA methods population_genomics scans_for_selection %T Genome scans for detecting footprints of local adaptation using a Bayesian factor model %U http://arxiv.org/abs/1402.5321 %X 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.

@misc{duforetfrebourg2014genome, abstract = {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.}, added-at = {2014-02-26T19:26:35.000+0100}, author = {Duforet-Frebourg, N. and Bazin, E. and Blum, M. G. B.}, biburl = {https://www.bibsonomy.org/bibtex/2e391c17ef16b05f2d87423aad1256d35/peter.ralph}, interhash = {6881fd513878154221ce156d9b32a9a4}, intrahash = {e391c17ef16b05f2d87423aad1256d35}, keywords = {PCA methods population_genomics scans_for_selection}, note = {cite arxiv:1402.5321}, timestamp = {2014-02-26T19:26:35.000+0100}, title = {Genome scans for detecting footprints of local adaptation using a {Bayesian} factor model}, url = {http://arxiv.org/abs/1402.5321}, year = 2014 }