Beyond heritability: improving discoverability in imaging genetics

Abstract

Structural neuroimaging measures based on magnetic resonance imaging have been at the forefront of imaging genetics. Global efforts to ensure homogeneity of measurements across study sites have enabled large-scale imaging genetic projects, accumulating nearly 50K samples for genome-wide association studies (GWAS). However, not many novel genetic variants have been identified by these GWAS, despite the high heritability of structural neuroimaging measures. Here, we discuss the limitations of using heritability as a guidance for assessing statistical power of GWAS, and highlight the importance of discoverability-which is the power to detect genetic variants for a given phenotype depending on its unique genomic architecture and GWAS sample size. Further, we present newly developed methods that boost genetic discovery in imaging genetics. By redefining imaging measures independent of traditional anatomical conventions, it is possible to improve discoverability, enabling identification of more genetic effects. Moreover, by leveraging enrichment priors from genomic annotations and independent GWAS of pleiotropic traits, we can better characterize effect size distributions, and identify reliable and replicable loci associated with structural neuroimaging measures. Statistical tools leveraging novel insights into the genetic discoverability of human traits, promises to accelerate the identification of genetic underpinnings underlying brain structural variation.

Figure 1.

Heritability and effect size distribution of structural neuroimaging measures. (A) Heritability estimates based on family (9,14,16) or SNP data (18,51,52). For reference purpose, the heritability of two complex traits, height and schizophrenia, were also provided here. (B) Quantile–quantile plot of effect size distribution in imaging GWAS, including intracranial volumes (ICV), volumes of putamen, and volumes of hippocampus. Flex upward in the tail region represent higher than expected effect sizes in the GWAS findings. (C). Stratified quantile–quantile plot of GWAS for putamen. The effect sizes of putamen GWAS are further stratified according to how well the SNP were tagged, i.e. LD. (D) Stratified quantile–quantile plot of GWAS for putamen, given genic annotation. The effect sizes of putamen GWAS are stratified according to the regulatory regions where SNP located.

Figure 2.

Clustering results based on effect size distribution of vertexwise GWAS. Clustering based on summary statistics from GWAS of each vertex of cortical surface can form nearly identical patterns with clusters derived from twin/family analysis.