The power of gene-based rare variant methods to detect disease-associated variation and test hypotheses about complex disease.

PLoS Genet
Authors
Keywords
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.

Year of Publication
2015
Journal
PLoS Genet
Volume
11
Issue
4
Pages
e1005165
Date Published
2015 Apr
ISSN
1553-7404
URL
DOI
10.1371/journal.pgen.1005165
PubMed ID
25906071
PubMed Central ID
PMC4407972
Links
Grant list
1RC2DK088389 / DK / NIDDK NIH HHS / United States
090532 / Wellcome Trust / United Kingdom
RC2-HG005688 / HG / NHGRI NIH HHS / United States
R01 DK098032 / DK / NIDDK NIH HHS / United States
T32 GM007753 / GM / NIGMS NIH HHS / United States
R01-DK098032 / DK / NIDDK NIH HHS / United States
T32GM008313 / GM / NIGMS NIH HHS / United States
090367 / Wellcome Trust / United Kingdom
DK062370 / DK / NIDDK NIH HHS / United States
P30 DK020572 / DK / NIDDK NIH HHS / United States
T32GM007753 / GM / NIGMS NIH HHS / United States
098381 / Wellcome Trust / United Kingdom
HG000376 / HG / NHGRI NIH HHS / United States
U01-DK085545 / DK / NIDDK NIH HHS / United States