Massively expedited genome-wide heritability analysis (MEGHA)

Abstract

The discovery and prioritization of heritable phenotypes is a computational challenge in a variety of settings, including neuroimaging genetics and analyses of the vast phenotypic repositories in electronic health record systems and population-based biobanks. Classical estimates of heritability require twin or pedigree data, which can be costly and difficult to acquire. Genome-wide complex trait analysis is an alternative tool to compute heritability estimates from unrelated individuals, using genome-wide data that are increasingly ubiquitous, but is computationally demanding and becomes difficult to apply in evaluating very large numbers of phenotypes. Here we present a fast and accurate statistical method for high-dimensional heritability analysis using genome-wide SNP data from unrelated individuals, termed massively expedited genome-wide heritability analysis (MEGHA) 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 to our knowledge. As a demonstration of application, we conducted heritability analyses on global and local morphometric measurements derived from brain structural MRI scans, using genome-wide SNP data from 1,320 unrelated young healthy adults of non-Hispanic European ancestry. We also computed surface maps of heritability for cortical thickness measures and empirically localized cortical regions where thickness measures were significantly heritable. Our analyses demonstrate the unique capability of MEGHA for large-scale heritability-based screening and high-dimensional heritability profile construction.

Keywords: endophenotype; genome-wide complex trait analysis; heritability; imaging genetics; phenomics.

Fig. 1.

MEGHA SNP-based heritability estimates for global morphometric measurements are plotted against GCTA results.

Fig. 2.

Evaluation of MEGHA using average cortical thickness measures in 68 regions of interest (ROIs). MEGHA SNP-based heritability estimates (Left) and P values (Right) are plotted against GCTA results for each ROI. There is an excellent concordance between MEGHA and GCTA.

Fig. 3.

Superior (S), inferior (I), lateral (L), anterior (A), posterior (P), and medial (M) views of the vertex-wise surface maps for SNP-based heritability significance of cortical thickness measures constructed by MEGHA. All clusters identified with a cluster-forming threshold P = 0.01 are shown. Five clusters that are familywise error corrected (FWEc) significant in size (FWEc, P < 0.05) based on 1,000 permutations are white outlined and annotated.