%0 Journal Article %1 moser2015simultaneous %A Moser, Gerhard %A Lee, Sang Hong %A Hayes, Ben J. %A Goddard, Michael E. %A Wray, Naomi R. %A Visscher, Peter M. %D 2015 %I Public Library of Science %J PLoS Genet %K GWAS methods mixed_models statistics %N 4 %P e1004969 %R 10.1371/journal.pgen.1004969 %T Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model %U http://dx.doi.org/10.1371%2Fjournal.pgen.1004969 %V 11 %X Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model. We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of seven common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits. These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.

@article{moser2015simultaneous, abstract = {Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model. We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of seven common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits. These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.}, added-at = {2015-04-22T23:57:38.000+0200}, author = {Moser, Gerhard and Lee, Sang Hong and Hayes, Ben J. and Goddard, Michael E. and Wray, Naomi R. and Visscher, Peter M.}, biburl = {https://www.bibsonomy.org/bibtex/21d160285802c9ffccb7c77148b2865c1/peter.ralph}, doi = {10.1371/journal.pgen.1004969}, interhash = {0008ce4f05e1b9b2cd4039c20555e3a6}, intrahash = {1d160285802c9ffccb7c77148b2865c1}, journal = {PLoS Genet}, keywords = {GWAS methods mixed_models statistics}, month = {04}, number = 4, pages = {e1004969}, publisher = {Public Library of Science}, timestamp = {2015-05-30T01:47:19.000+0200}, title = {Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a {Bayesian} Mixture Model}, url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1004969}, volume = 11, year = 2015 }