A multiple-phenotype imputation method for genetic studies

作者：Dahl Andrew; Iotchkova Valentina; Baud Amelie; Johansson Asa; Gyllensten Ulf; Soranzo Nicole; Mott Richard; Kranis Andreas; Marchini Jonathan^*

来源：Nature Genetics, 2016, 48(4): 466-+.

DOI：10.1038/ng.3513

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.

• 出版日期2016-4