A covering method for detecting genetic associations between rare variants and common phenotypes.
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.
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA. gbhatia@mit.edu
Gaurav
G
Bhatia
Vikas
V
Bansal
Olivier
O
Harismendy
Nicholas J
NJ
Schork
Eric J
EJ
Topol
Kelly
K
Frazer
Vineet
V
Bafna
D000465:Algorithms; D000581:Amidohydrolases / Q000235:genetics; D015992:Body Mass Index; D063385:Cannabinoid Receptor Modulators / Q000378:metabolism; D016009:Chi-Square Distribution; D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005787:Gene Frequency; D056726:Genetic Association Studies / Q000379:methods*; D005828:Genetics, Population; D009154:Mutation / Q000235:genetics*; D009765:Obesity / Q000235:genetics / Q000378:metabolism; D010641:Phenotype; D029584:Prostatic Secretory Proteins / Q000235:genetics; D000099252:Fatty Acid Amide Hydrolases
D000581:Amidohydrolases; D063385:Cannabinoid Receptor Modulators; D029584:Prostatic Secretory Proteins; D000099252:Fatty Acid Amide Hydrolases; C043076:beta-microseminoprotein