Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment

Lili Ding, Brad G. Kurowski, Hua He, Eileen S. Alexander, Tesfaye B. Mersha, David W. Fardo, Xue Zhang, Valentina V. Pilipenko, Leah Kottyan, Lisa J. Martin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Genetic studies often collect data on multiple traits. Most genetic association analyses, however, consider traits separately and ignore potential correlation among traits, partially because of difficulties in statistical modeling of multivariate outcomes. When multiple traits are measured in a pedigree longitudinally, additional challenges arise because in addition to correlation between traits, a trait is often correlated with its own measures over time and with measurements of other family members. We developed a Bayesian model for analysis of bivariate quantitative traits measured longitudinally in family genetic studies. For a given trait, family-specific and subject-specific random effects account for correlation among family members and repeated measures, respectively. Correlation between traits is introduced by incorporating multivariate random effects and allowing time-specific trait residuals to correlate as in seemingly unrelated regressions. The proposed model can examine multiple single-nucleotide variations simultaneously, as well as incorporate familyspecific, subject-specific, or time-varying covariates. Bayesian multiplicity technique is used to effectively control false positives. Genetic Analysis Workshop 18 simulated data illustrate the proposed approach's applicability in modeling longitudinal multivariate outcomes in family genetic association studies.

Original languageEnglish
Article numberS69
JournalBMC Proceedings
Volume8
DOIs
StatePublished - Jun 17 2014

Bibliographical note

Publisher Copyright:
© 2014 Ding et al.; licensee BioMed Central Ltd.

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology

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