A multiple-phenotype imputation method for genetic studies.

Dahl A, Iotchkova V, Baud A, Johansson Å, Gyllensten U, Soranzo N, Mott R, Kranis A, Marchini J

Nat. Genet. 48 (4) 466-472 [2016-04-00; online 2016-02-22]

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.

Affiliated researcher

PubMed 26901065

DOI 10.1038/ng.3513

Crossref 10.1038/ng.3513

pii: ng.3513
pmc: PMC4817234
mid: EMS66922


Publications 7.1.2