A multiple-phenotype imputation method for genetic studies
2016 (English)In: Nature Genetics, ISSN 1061-4036, E-ISSN 1546-1718, Vol. 48, no 4, 466-472 p.Article in journal (Refereed) PublishedText
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.
Place, publisher, year, edition, pages
2016. Vol. 48, no 4, 466-472 p.
IdentifiersURN: urn:nbn:se:uu:diva-293023DOI: 10.1038/ng.3513ISI: 000372908800018PubMedID: 26901065OAI: oai:DiVA.org:uu-293023DiVA: diva2:927227
FunderEU, European Research Council, 617306