Speaker:
Thomas Nichols, Neuroimaging Statistics, Institute for Digital Healthcare, Warwick
Abstract:
There has been great interest in discovering and understanding the role of genetic variation in neuroimaging phenotypes. Typical imaging genetics studies use a small number of candidate SNPs, a small number of brain regions, or both. In this talk I will consider methods for searching for gene-brain associations over the entire genome and all brain regions. Such an approach presents massive computational and statistical challenges. I\\\'ll discuss two approaches, a mass-univariate approach and a multivariate approach. A mass-univariate model is the standard tool in neuroimaging analysis, but scaling it up for 100,000 SNPs requires a series of computational and statistical innovations. With our method applied to Tensor-Based Morphometry data from the ADNI project, we report the first gene-brain association to survive whole-genome, whole-brain familywise error correction. Our multivariate approach uses a Sparse Reduced Rank Regression (sRRR) to jointly and parsimoniously explain gene-brain associations. Detailed power analyses show that the multivariate approach should have even greater power than the univariate approach.