We imputed four variable number tandem repeat polymorphisms (VNTRs) and one moderating single nucleotide polymorphism (SNP) in SLC6A3, DRD4, SLC6A4, and MAOA in 486,551 UK Biobank individuals. Using the HRC-imputed data, we phased a 3Mb window surrounding the target polymorphism using Shapeit2 (Delaneau et al. 2013). We then imputed the target polymorphisms using Minimac3 (Das et al. 2016). Our reference panel for imputation included two independent samples, 1) the Family Transitions Project (FTP), and 2) the combined Center for Antisocial Drug Dependence (CADD) and Genetics of Antisocial Drug Dependence (GADD) (Conger et al. 2012, Derringer et al. 2015, Young et al. 2000), which had previously been genotyped on various Illumina and Affymetrix platforms and had directly-genotyped VNTR genotypes, as previously described (Derringer et al. 2015, Haberstick et al. 2014, 2015). As the reference panels were genotyped on various platforms, we first imputed the reference panels to the HRC and restricted the analysis to biallelic SNPs with imputation INFO scores of at least 0.6. For MAOA, on the X chromosome, we imputed males and females separately. We used the two independent reference panels (FTP and CADD/GADD) with both genome-wide SNP data and directly-genotyped VNTR data to estimate our imputation accuracy. Genotypic match rates were above 0.8 for all polymorphisms and for both datasets. Applying a genotype probabilities threshold of 0.99 increased the minimum genotypic match rate to 0.96. In the UK Biobank individuals, the Minimac3 INFO scores were 0.925 for the SLC6A3 VNTR, 0.906 for the DRD4 VNTR, 0.907 for SLC6A4 rs25531, 0.883 for SLC6A4 5HTTLPR, and 0.968 for MAOA VNTR
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|