The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease

Abstract

Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

“…We note that our results contrast those of Moutsianas et al (2015), who used the Eyre-Walker (2010) model and found higher power for RVATs when rare alleles explain a large fraction of trait variance. However, we also note that their study predominantly focused on a model of binary traits that have unidirectional effects, whereas we have focused on a model of quantitative traits under stabilizing selection with bidirectional effects.…”

“…However, we note that SKAT-O was specifically designed to retain power in both situations (equal and unequal proportions of trait-increasing/trait-decreasing variants) (Lee et al 2012a,b), so we do not expect that the main results of the paper (i.e., that selection strength and growth rate alter architecture and power) are affected by this choice. Moreover, a recent study examined the Pearson correlation between P-values reported by a wide range of RVATs (Moutsianas et al 2015), and found that SKAT-O is highly correlated to SKAT (0.93), C-ALPHA (Neale et al 2011) (0.92), and KBAC (Liu and Leal 2010) (Price et al 2010) (0.63), and BURDEN (https://atgu.mgh. harvard.edu/plinkseq/) (0.77).…”

Extended exome sequencing identifies BACH2 as a novel major risk locus for Addison's disease

“…Even though correction for population stratification is extensively enabled in our study, rare variant association analysis is limited by the current sample size [74]. Because not all genes and not all noncoding regions could be selected in our study, whole-genome sequencing would be a desirable next step.…”

The Genetics of Stress-Related Disorders: PTSD, Depression, and Anxiety Disorders

“…In the case of common variants, the requirement for large samples is due to the small effect sizes expected--typically an allelic odds ratio of less than 1.20. In the case of rare variants, effect sizes may be larger, but their rarity again entails large sample sizes to allow detection of a sufficient number of risk variant carriers (Moutsianas et al, 2015;Zuk et al, 2014). Gene-environment interaction (G × E) studies examine whether the effect of a genetic variant is modified by an environmental exposure.…”