TY - JOUR
T1 - Modeling of multivariate longitudinal phenotypes in family genetic studies with Bayesian multiplicity adjustment
AU - Ding, Lili
AU - Kurowski, Brad G.
AU - He, Hua
AU - Alexander, Eileen S.
AU - Mersha, Tesfaye B.
AU - Fardo, David W.
AU - Zhang, Xue
AU - Pilipenko, Valentina V.
AU - Kottyan, Leah
AU - Martin, Lisa J.
N1 - Publisher Copyright:
© 2014 Ding et al.; licensee BioMed Central Ltd.
PY - 2014/6/17
Y1 - 2014/6/17
N2 - 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.
AB - 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.
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U2 - 10.1186/1753-6561-8-S1-S69
DO - 10.1186/1753-6561-8-S1-S69
M3 - Article
AN - SCOPUS:85018193520
SN - 1753-6561
VL - 8
JO - BMC Proceedings
JF - BMC Proceedings
M1 - S69
ER -