Searching for missing heritability: Designing rare variant association studies

TLDR

Genomic studies have identified many common variants for diseases, yet they explain only part of heritability, suggesting rare variants may also contribute but remain largely unexplored. This paper proposes an analytical framework to design rare variant association studies. The framework offers guidance on sample size, allele selection, frequency thresholds, population choice, gene sets, and coding versus noncoding regions.

Abstract

Significance Discovering the genetic basis of common diseases, such as diabetes, heart disease, and schizophrenia, is a key goal in biomedicine. Genomic studies have revealed thousands of common genetic variants underlying disease, but these variants explain only a portion of the heritability. Rare variants are also likely to play an important role, but few examples are known thus far, and initial discovery efforts with small sample sizes have had only limited success. In this paper, we describe an analytical framework for the design of rare variant association studies of disease. It provides guidance with respect to sample size, as well as the roles of selection, disruptive and missense alleles, gene-specific allele frequency thresholds, isolated populations, gene sets, and coding vs. noncoding regions.