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Title: A consistent estimator of variance explained by genome-wide and whole-brain analyses Authors:  Wesley Thompson - Institute of Biological Psychiatry (Denmark) [presenting]
Abstract: A typical analysis of genome-wide association studies (GWAS) and whole-brain voxel or vertex analyses is characterized by a large number of univariate regressions, wherein an outcome is regressed on thousands to millions of markers or voxels, one at a time. Assuming a linear model linking the markers to the outcome, an estimator is proposed for the variance explained for the trait, defined as the fraction of the variance of the trait explained by the markers or voxels in the study. The estimator is easy to compute, highly interpretable, and is consistent as the number of markers and the sample size increase. Importantly, it can be computed from summary statistics typically reported in GWAS and thus not requiring access to the original data. The estimator takes full account of the correlation between genomic markers or voxels. We also provide an analytical form for the standard errors of the GWASH estimator. We also sketch how this method may be extended to incorporate genetic and imaging data simultaneously.