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Activity Number: 171
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321379
Title: Identification of Genetic Effects on Abnormalities of Cardiac Structure and Function Using Bayesian Hierarchical Model
Author(s): Akram Yazdani* and Azam Yazdani and Luca Sartore and Eric Boerwinkle
Companies: The University of Texas Health Science Center at Houston and and National Institute of Statistical Sciences and The University of Texas Health Science Center at Houston
Keywords: Bayesian Variable Selection ; cardiac function and structure ; linkage disequilibrium ; MCMC

Heart failure is a major risk factor for premature death. Given the heterogeneity of the heart failure syndrome, determining the genetics of cardiac function and structure may provide greater insight into heart failure. We investigated the association of 12 components of cardiac structure and function with 260,688 SNPs measured over 6,102 individuals in a large cohort of the Atherosclerosis Risk in Communities (ARIC) study. We develop a multivariate multiple linear regression using hierarchical Bayesian model. The model improves the power of identifying promising SNPs by incorporating the correlation structure among the phenotypes and individuals. Placing shrinkage prior and using spike and slab model provide a good tool for variable selection. We leveraged the genomic available knowledge, to set the hyperparameters in the model. Other parameters of the model are estimated through Markov Chain Monte Carlo (MCMC) techniques. To tackle mixing problem with MCMC sampling due to high linkage disequilibrium, LD, among neighboring SNPs, we calculated the joint inclusion probabilities for SNPs in high LD.

Authors who are presenting talks have a * after their name.

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