Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk.
We analyzed ~250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci.
Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals.
Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.
Genetic architecture of disease and related anthropometric phenotypes
We are interested in how anthropomorphic and health/disease phenotypes are associated with health and disease. The specific aim of this study is to examine association signals from SNPs, examine how much of the variation is captured by examining all SNPs simultaneously, and look at the extent to which SNPs that predict variation in these phenotypes in one ethnic group also predict variation in these traits in other ethnic groups. We would also like to conduct association analyses of tobacco use and alcohol phenotypes and meta-analyze them with results from other studies. Finally, we are interested in using these phenotypes to test new methods for using SNP data to estimate the heritability of traits. Several "intermediate" factors, such as anthropometric phenotypes (e.g.,BMI and height) and blood proteins (e.g., cholesterol) affect human health and disease, yet the genetic underpinnings of these phenotypes remain poorly characterized. This is a request for access to data relevant to health elated traits in order to help elucidate the patterns of genetic variation that may underlie these traits. Access to data containing health and disease outcomes will allow study of the genetic variation underlying the particular diseases, and the relationship of that variation to anthropometric risk factors and correlates. Understanding the genetic architecture of anthropomorphic and health/disease phenotypes can lead to greater understanding of the mediating factors that affect the burden of health and disease in modern societies. We plan to use genetic data (single nucleotide polymorphism data) to investigate whether certain genetic variants predict these intermediate and disease traits, and how well we can predict these traits by considering all genetic data simultaneously. Our methods require large samples; we would like access to all whole genome genotyped data possible.
|Lead investigator:||Dr Matthew Keller|
|Lead institution:||University of Colorado|