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PLoS Genet. 2015 Apr 23;11(4):e1005165. doi: 10.1371/journal.pgen.1005165. eCollection 2015 Apr.

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

Author information

1
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
2
Program in Biophysics, Harvard University, Cambridge, Massachusetts, United States of America; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America.
3
Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, United States of America.
4
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America; Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
5
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, United States of America; Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America; Department of Genetics, Harvard Medical School, Boston, Massachusetts, United States of America; Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
6
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom.

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.

PMID:
25906071
PMCID:
PMC4407972
DOI:
10.1371/journal.pgen.1005165
[Indexed for MEDLINE]
Free PMC Article

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