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

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.

Figure 1

Posterior exclusion probability and posterior density of regression coefficients of the causal SNVs. For the Bayesian bivariate model with 90 noise variants, the plot shows, for the 15 causal SNVs, (a) estimated posterior exclusion probabilities (red for DBP and blue for SDP), and (b) posterior density of regression coefficients (red for DBP and blue for SBP) when the SNVs were included in the model. Dashed reference lines indicate simulated effect sizes.