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Abstract:
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Genome-wise complex trait analysis (GCTA) was developed and applied to heritability analyses on complex traits and more recently extended to mental disorders. However, besides the intensive computation, previous literature limits the scope to univariate phenotype, which brings difficulty in the interpretation and leads to misspecification under highly correlated phenotypes. In the paper, we develop a unified Bayesian model to study the joint genetic effect on the whole brain-wise phenotype variation. Specifically, by using joint Ising and Dirichlet process priors within the phenotype specific mixed effect models, we are capable to capture both local grouping effect and global sparsity for the heritability estimation. We also develop relevant posterior inference procedure and hyperparameter selection criteria. Besides extensive simulation studies to assess the proposed method comparing with multiple competing approaches, we also apply the model to an imaging genetics study from ADNI database, and obtain biologically interpretable result. The method in the paper is general and can be extended to a wide range of study with similar data structure.
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