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Heritability Maps of Cortical Structure Estimated with MEGHA
We are distributing vertex-wise surface maps for the heritability estimates and significance of cortical thickness, sulcal depth, curvature, and surface area measures. These maps are in standard FreeSurfer surface space (fsaverage) and can be used as a prior in genetic association studies of cortical surface morphology. For example, researchers can prioritize different regions or morphometric measurements based on the heritability estimates or significance we provide. Alternatively, researchers can use these maps to define regions of interest (ROIs) to focus their analyses on.
These maps were computed using MEGHA (Massively Expedited Genome-wide Heritability Analysis). We developed MEGHA, a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide single nucleotide polymorphism (SNP) data from unrelated individuals, and accompanying nonparametric sampling techniques that enable flexible inferences for arbitrary statistics of interest. MEGHA produces estimates and significance measures of heritability with several orders of magnitude less computational time than existing methods, making heritability-based prioritization of millions of phenotypes based on data from unrelated individuals tractable for the first time. We computed vertex-wise surface maps in FreeSurfer fsaverage space for SNP-based heritability estimates and significance of cortical thickness, sulcal depth, curvature, and surface area measures, using data from 1,320 unrelated young (18-35y) healthy adults of non-Hispanic European ancestry as part of the Harvard/MGH Brain Genomics Superstruct Project (GSP). These surface maps are available for download.
References
Software
A MATLAB implementation of MEGHA can be found here.
Surface Maps for Cortical Thickness Measures
