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Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.

Original publication

DOI

10.1038/ng.3513

Type

Journal article

Journal

Nat Genet

Publication Date

04/2016

Volume

48

Pages

466 - 472

Keywords

Algorithms, Animals, Animals, Outbred Strains, Bayes Theorem, Blood Platelets, Chickens, Female, Genome-Wide Association Study, Humans, Male, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide, Rats, T-Lymphocytes, Triticum