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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 also limits the scope to univariate phenotype, which cannot be directly applied to most biomedical studies with more than one phenotype. In the paper, we develop a Bayesian model to study the genetic effect on the whole brain-wise local variation. Specifically we conduct genome-wide heritability analysis with all the voxel level imaging biomarker as phenotypes. To capture the sparsity and localized correlation of genetic effect on the brain, we introduce a functional selection indicator and adopt Ising models and Dirichlet process priors to capture the local correlation and smoothness. Besides extensive simulation studies, we also apply the model to an imaging genetics study with around 200,000 voxels and more than 400,000 SNPs obtained from ADNI database. We show that our model can successfully analyze dataset with such a large dimension with acceptable computational cost.
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