期刊
PLOS COMPUTATIONAL BIOLOGY
卷 6, 期 10, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1000954
关键词
-
资金
- Price Foundation
- Scripps Genomic Medicine
- National Human Genome Research Institute (NHGRI) [T32 HG002295]
- [IIS-0810905]
- [U19 AG023122-05]
- [R01 MH078151-03]
- [N01 MH22005]
- [U01 DA024417-01]
- [P50 MH081755-01]
- [R01 AG030474-02]
- [N01 MH022005]
- [R01 HL089655-02]
- [R01 MH080134-03]
- [U54 CA143906-01]
- [UL1 RR025774-03]
Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RARECOVER. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RARECOVER to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RARECOVER analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.
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