Nicolas Duforet-Frebourg (UJF, Grenoble): Genome scans for local adaptation: a Bayesian factor model

We present a new Bayesian hierarchical model based on factor models for detecting outliers in high-dimensional data. Outliers are explicitly modeled using a variance inflation approach. The degree of outlyingness for each variable is measured using Bayes factors. In population genetics where many genetic markers are typed in different populations, we show that factor models can be used to map genomic regions involved in Darwinian adaptation. We provide a comparison with common approaches of genome scans and show that factor models reduce the false discovery rate.

Keywords: Factor models, outlier detection, population genetics, local adaptation

 

Joint work with Michael G.B. Blum and Eric Bazin

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One Response to “Nicolas Duforet-Frebourg (UJF, Grenoble): Genome scans for local adaptation: a Bayesian factor model”

  1. MCMSki | Slept like a... Says:

    […] from University of Grenoble about detecting selection using either environmental gradients or PCA-like directions of variation in population structure – somewhat similar to methods people in the group have been […]

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