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Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery

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  • Jose A Seoane
  • Colin Campbell
  • Ian N M Day
  • Juan P Casas
  • Tom R Gaunt

Abstract

Genome-wide association studies have identified a wealth of genetic variants involved in complex traits and multifactorial diseases. There is now considerable interest in testing variants for association with multiple phenotypes (pleiotropy) and for testing multiple variants for association with a single phenotype (gene-based association tests). Such approaches can increase statistical power by combining evidence for association over multiple phenotypes or genetic variants respectively. Canonical Correlation Analysis (CCA) measures the correlation between two sets of multidimensional variables, and thus offers the potential to combine these two approaches. To apply CCA, we must restrict the number of attributes relative to the number of samples. Hence we consider modules of genetic variation that can comprise a gene, a pathway or another biologically relevant grouping, and/or a set of phenotypes. In order to do this, we use an attribute selection strategy based on a binary genetic algorithm. Applied to a UK-based prospective cohort study of 4286 women (the British Women's Heart and Health Study), we find improved statistical power in the detection of previously reported genetic associations, and identify a number of novel pleiotropic associations between genetic variants and phenotypes. New discoveries include gene-based association of NSF with triglyceride levels and several genes (ACSM3, ERI2, IL18RAP, IL23RAP and NRG1) with left ventricular hypertrophy phenotypes. In multiple-phenotype analyses we find association of NRG1 with left ventricular hypertrophy phenotypes, fibrinogen and urea and pleiotropic relationships of F7 and F10 with Factor VII, Factor IX and cholesterol levels.Author Summary: Pleiotropy appears when a variation in one gene affects to several non-related phenotypes. The study of this phenomenon can be useful in gene function discovery, but also in the study of the evolution of a gene. In this paper, we present a methodology, based on Canonical Correlation Analysis, which studies gene-centered multiple association of the variation of SNPs in one or a set of genes with one or a set of phenotypes. The resulting methodology can be applied in gene-centered association analysis, multiple association analysis or pleiotropic pattern discovery. We apply this methodology with a genotype dataset and a set of cardiovascular related phenotypes, and discover new gene association between gene NRG1 and phenotypes related with left ventricular hypertrophy, and pleiotropic effects of this gene with other phenotypes as coagulation factors and urea or pleiotropic effects between coagulation related genes F7 and F10 with coagulation factors and cholesterol levels. This methodology could be also used to find multiple associations in other omics datasets.

Suggested Citation

  • Jose A Seoane & Colin Campbell & Ian N M Day & Juan P Casas & Tom R Gaunt, 2014. "Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-13, October.
    • Handle: RePEc:plo:pcbi00:1003876
    DOI: 10.1371/journal.pcbi.1003876
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    1. Leonardo Bottolo & Marc Chadeau-Hyam & David I Hastie & Tanja Zeller & Benoit Liquet & Paul Newcombe & Loic Yengo & Philipp S Wild & Arne Schillert & Andreas Ziegler & Sune F Nielsen & Adam S Butterwo, 2013. "GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm," PLOS Genetics, Public Library of Science, vol. 9(8), pages 1-17, August.
    2. Paul F O’Reilly & Clive J Hoggart & Yotsawat Pomyen & Federico C F Calboli & Paul Elliott & Marjo-Riitta Jarvelin & Lachlan J M Coin, 2012. "MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-1, May.
    3. Parkhomenko Elena & Tritchler David & Beyene Joseph, 2009. "Sparse Canonical Correlation Analysis with Application to Genomic Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-36, January.
    4. Witten Daniela M & Tibshirani Robert J., 2009. "Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-29, June.
    5. Tamra E Meyer & Germaine C Verwoert & Shih-Jen Hwang & Nicole L Glazer & Albert V Smith & Frank J A van Rooij & Georg B Ehret & Eric Boerwinkle & Janine F Felix & Tennille S Leak & Tamara B Harris & Q, 2010. "Genome-Wide Association Studies of Serum Magnesium, Potassium, and Sodium Concentrations Identify Six Loci Influencing Serum Magnesium Levels," PLOS Genetics, Public Library of Science, vol. 6(8), pages 1-11, August.
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    1. Nan Lin & Yun Zhu & Ruzong Fan & Momiao Xiong, 2017. "A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-33, October.

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