Seminar 30 March 11:00 am – Amphi Jacques Monod
Centre de Biologie pour la Gestion des Populations
UMR INRA/IRD/CIRAD/Montpellier SupAgro
755 Avenue du Campus Agropolis CS30016 34988 Montferrier sur Lez Cedex
Title: Elucidating the genetic architecture of complex traits using across population Genome Wide Association scans
Abstract: Characterizing the genetic architecture of complex heritable traits still represents one of the main challenge of modern genetics with strong implications in evolution, medical and agriculture science. To that end, mapping of Quantitative Trait Loci that contribute to the trait variance observed across individuals originating from experimental or outbred populations has been very popular since the early development of genetic maps and genotyping technologies. In this presentation, I will discuss an alternative strategy derived from population genomics that consists in exploiting trait variation existing across (instead of within) populations that share a common and recent history. More precisely, I will first present a versatile method recently developed under a Bayesian hierarchical modeling framework and implemented in the software BayPass. This approach allows performing both genome-wide scan for adaptive divergence and association with population-specific covariates. Importantly, by including the neutral covariance structure across population allele frequencies, the method explicitly accounts for the joint demographic history of the populations that may otherwise confound association signals. As a proof of concept, I will further present an illustration showing how SNPs associated with across-population variation of major cattle dairy traits in 13 French cattle breeds uncover a gene network underlying milk production. More generally, combining systems biology tools to population genomics approaches proves quite promising to better understand the genetic architecture of complex traits.
Ref: Gautier M. (2015). Genome-Wide Scan for Adaptive Differentiation and Association Analysis with population-specific covariates. Genetics, 201(4):1555-79.