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


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
StatePublished - Jun 17 2014

Bibliographical note

Funding Information:
This work was supported in part by NIH grants 8P20GM103436-12 (DWF), K25AG043546(DWF), NS36695 (LD, LJM), AI070235 (HH, LJM), AI066738 (LJM), HL111459 (LJM, VP), T32-ES10957 (ESA), K12 HD001097-16 (BGK), K01HL103165 (TBM). This paper was also support by an Institutional Clinical and Translational Science Award (LD), NIH/NCATS Grant Number 8UL1TR000077-04. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The GAW18 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The Genetic Analysis Workshop is supported by NIH grant R01 GM031575. This article has been published as part of BMC Proceedings Volume 8 Supplement 1, 2014: Genetic Analysis Workshop 18. The full contents of the supplement are available online at supplements/8/S1. Publication charges for this supplement were funded by the Texas Biomedical Research Institute.

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

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology (all)


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