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Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis.

ABSTRACT: Studying multiple outcomes simultaneously allows researchers to begin to identify underlying factors that affect all of a set of diseases (i.e., shared etiology) and what may give rise to differences in disorders between patients (i.e., disease subtypes). In this work, our goal is to build risk scores that are predictive of multiple phenotypes simultaneously and identify subpopulations at high risk of multiple phenotypes. Such analyses could yield insight into etiology or point to treatment and prevention strategies. The standard canonical correlation analysis (CCA) can be used to relate multiple continuous outcomes to multiple predictors. However, in order to capture the full complexity of a disorder, phenotypes may include a diverse range of data types, including binary, continuous, ordinal, and censored variables. When phenotypes are diverse in this way, standard CCA is not possible and no methods currently exist to model them jointly. In the presence of such complications, we propose a semi-parametric CCA method to develop risk scores that are predictive of multiple phenotypes. To guard against potential model mis-specification, we also propose a nonparametric calibration method to identify subgroups that are at high risk of multiple disorders. A resampling procedure is also developed to account for the variability in these estimates. Our method opens the door to synthesizing a wide array of data sources for the purposes of joint prediction.

SUBMITTER: Agniel D

PROVIDER: S-EPMC5640493 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis.

Agniel Denis D Cai Tianxi T

Biometrics 20170413 4

Studying multiple outcomes simultaneously allows researchers to begin to identify underlying factors that affect all of a set of diseases (i.e., shared etiology) and what may give rise to differences in disorders between patients (i.e., disease subtypes). In this work, our goal is to build risk scores that are predictive of multiple phenotypes simultaneously and identify subpopulations at high risk of multiple phenotypes. Such analyses could yield insight into etiology or point to treatment and ...[more]

PMID: 28407213

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Project description:Binary phenotypes commonly arise due to multiple underlying quantitative precursors and genetic variants may impact multiple traits in a pleiotropic manner. Hence, simultaneously analyzing such correlated traits may be more powerful than analyzing individual traits. Various genotype-level methods, e.g., MultiPhen (O'Reilly et al. []), have been developed to identify genetic factors underlying a multivariate phenotype. For univariate phenotypes, the usefulness and applicability of allele-level tests have been investigated. The test of allele frequency difference among cases and controls is commonly used for mapping case-control association. However, allelic methods for multivariate association mapping have not been studied much. In this article, we explore two allelic tests of multivariate association: one using a Binomial regression model based on inverted regression of genotype on phenotype (Binomial regression-based Association of Multivariate Phenotypes [BAMP]), and the other employing the Mahalanobis distance between two sample means of the multivariate phenotype vector for two alleles at a single-nucleotide polymorphism (Distance-based Association of Multivariate Phenotypes [DAMP]). These methods can incorporate both discrete and continuous phenotypes. Some theoretical properties for BAMP are studied. Using simulations, the power of the methods for detecting multivariate association is compared with the genotype-level test MultiPhen's. The allelic tests yield marginally higher power than MultiPhen for multivariate phenotypes. For one/two binary traits under recessive mode of inheritance, allelic tests are found to be substantially more powerful. All three tests are applied to two different real data and the results offer some support for the simulation study. We propose a hybrid approach for testing multivariate association that implements MultiPhen when Hardy-Weinberg Equilibrium (HWE) is violated and BAMP otherwise, because the allelic approaches assume HWE.

Dataset Information

Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis.

Publications

Analysis of multiple diverse phenotypes via semiparametric canonical correlation analysis.

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