Genetic architecture of disease and related anthropometric phenotypes
Approved Research ID: 16651
Approval date: January 12th 2016
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.
EXPANDED SCOPE 2: In addition to previously discussed approaches, we would like to use UK Biobank exomic and imputed SNP data to a) understand the degree to which gene signals from common variants overlap with gene signals from rare variants; b) explore SNP and gene-by-environment interactions for addiction-related behaviors, psychiatric disorders, and age-related cognitive decline; c) estimate heritability and common familial environmental effects using close and distant relatives as detected using identical by descent segments; and d) examine the genetic and phenotypic factor structure of traits related to psychiatric disorders, personality, pain, and disease.