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Genome-wide association studies with high-dimensional phenotypes

Author

Listed:
  • Marttinen Pekka

    (Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076 Aalto, Finland)

  • Gillberg Jussi

    (Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076 Aalto, Finland)

  • Havulinna Aki

    (National Institute for Health and Welfare, Department of Chronic Disease Prevention, P.O. Box 30, FI-00271 Helsinki, Finland)

  • Corander Jukka

    (Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, Finland)

  • Kaski Samuel

    (Helsinki Institute for Information Technology HIIT, Department of Information and Computer Science, Aalto University, P.O. Box 15400, FI-00076 Aalto, Finland Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, P.O. Box 68, FI-00014 Helsinki, Finland)

Abstract

High-dimensional phenotypes hold promise for richer findings in association studies, but testing of several phenotype traits aggravates the grand challenge of association studies, that of multiple testing. Several methods have recently been proposed for testing jointly all traits in a high-dimensional vector of phenotypes, with prospect of increased power to detect small effects that would be missed if tested individually. However, the methods have rarely been compared to the extent of enabling assessment of their relative merits and setting up guidelines on which method to use, and how to use it. We compare the methods on simulated data and with a real metabolomics data set comprising 137 highly correlated variables and approximately 550,000 SNPs. Applying the methods to genome-wide data with hundreds of thousands of markers inevitably requires division of the problem into manageable parts facilitating parallel processing, parts corresponding to individual genetic variants, pathways, or genes, for example. Here we utilize a straightforward formulation according to which the genome is divided into blocks of nearby correlated genetic markers, tested jointly for association with the phenotypes. This formulation is computationally feasible, reduces the number of tests, and lets the methods take advantage of combining information over several correlated variables not only on the phenotype side, but also on the genotype side. Our experiments show that canonical correlation analysis has higher power than alternative methods, while remaining computationally tractable for routine use in the GWAS setting, provided the number of samples is sufficient compared to the numbers of phenotype and genotype variables tested. Sparse canonical correlation analysis and regression models with latent confounding factors show promising performance when the number of samples is small compared to the dimensionality of the data.

Suggested Citation

  • Marttinen Pekka & Gillberg Jussi & Havulinna Aki & Corander Jukka & Kaski Samuel, 2013. "Genome-wide association studies with high-dimensional phenotypes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 413-431, August.
  • Handle: RePEc:bpj:sagmbi:v:12:y:2013:i:4:p:413-431:n:1
    DOI: 10.1515/sagmb-2012-0032
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    References listed on IDEAS

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    1. Peter Donnelly, 2008. "Progress and challenges in genome-wide association studies in humans," Nature, Nature, vol. 456(7223), pages 728-731, December.
    2. Lê Cao Kim-Anh & Rossouw Debra & Robert-Granié Christèle & Besse Philippe, 2008. "A Sparse PLS for Variable Selection when Integrating Omics Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-32, November.
    3. Nicoló Fusi & Oliver Stegle & Neil D Lawrence, 2012. "Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies," PLOS Computational Biology, Public Library of Science, vol. 8(1), pages 1-9, January.
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