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Exome chip meta-analysis elucidates the genetic architecture of rare coding variants in smoking and drinking behavior
Dajiang J Liu,
David M. Brazel,
Daniel R. Barnes,
A Mesut Erzurumluoglu,
Jessica D. Faul,
Anke R Hammerschlag,
Christiaan A de Leeuw,
Carl A. Melbourne,
Charles B. Eaton,
William G Iacono,
Nicholas G Martin,
Tinca J. Polderman,
CHD Exome+ Consortium,
Consortium for Genetics of Smoking Behavior,
H. Steven Scholte,
Jennifer A. Smith,
Hilary A. Tindle,
Andreis R van der Leij,
Sean P. David,
Sharon LR Kardia,
David R. Weir,
Gonçalo R Abecasis,
Posted 12 Sep 2017
bioRxiv DOI: 10.1101/187658 (published DOI: 10.1038/s41380-018-0313-0)
Posted 12 Sep 2017
Background: Smoking and alcohol use behaviors in humans have been associated with common genetic variants within multiple genomic loci. Investigation of rare variation within these loci holds promise for identifying causal variants impacting biological mechanisms in the etiology of disordered behavior. Microarrays have been designed to genotype rare nonsynonymous and putative loss of function variants. Such variants are expected to have greater deleterious consequences on gene function than other variants, and significantly contribute to disease risk. Methods: In the present study, we analyzed ~250,000 rare variants from 17 independent studies. Each variant was tested for association with five addiction-related phenotypes: cigarettes per day, pack years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted single variant tests of all variants, and gene-based burden tests of nonsynonymous or putative loss of function variants with minor allele frequency less than 1%. Results: Meta-analytic sample sizes ranged from 70,847 to 164,142 individuals, depending on the phenotype. Known loci tagged by common variants replicated, but there was no robust evidence for individually associated rare variants, either in gene based or single variant tests. Using a modified method-of-moment approach, we found that all low frequency coding variants, in aggregate, contributed 1.7% to 3.6% of the phenotypic variation for the five traits (p<.05). Conclusions: The findings indicate that rare coding variants contribute to phenotypic variation, but that much larger samples and/or denser genotyping of rare variants will be required to successfully identify associations with these phenotypes, whether individual variants or gene- based associations.
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