Forum de statistique : conférence «Improving genetic risk prediction for common complex diseases »

Prof. Matthew Robinson
Faculté de biologie et médecine, Département de biologie computationnelle; UNIL, Lausanne

Implementation of linear mixed models (LMM) in human genomics has demonstrated that a large proportion of complex trait heritability can be captured by jointly accounting for all common genetic variants. LMM can be used to obtain SNP markers effects with best linear unbiased prediction properties (BLUP), which have been shown to improve genomic prediction over genetic predictors obtained from large-scale meta-analyses. The classical LMM, however, assumes that there is a single distribution of genetic effects, implying that all markers contribute to SNP-based heritability. The speaker will discuss how this approach can be improved through the use of a Bayesian mixture model framework, where a mixture of normal distributions of SNP effects, including a zero effect, is adopted instead. He will then investigate how summary statistics from different traits can be combined to create genomic predictors which utilize all of the available information. Finally, the lecturer will discuss the likely importance of genotype-covariate effects for genomic prediction using body mass index as an example..

Date et heure
Jeudi, 21 Septembre, 2017 - 11:00
Salle Delachaux (étage 01), Institut universitaire de médecine sociale et préventive, Bâtiment Biopôle 2, route de la Corniche 10, 1010 Lausanne

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