Uuring keskendub antropomeetrilistele tunnustele – tüsedus [kehamassiindeks ja vöö-puusa ümbermõõt] ja kehapikkus – ning läbi suuremate valimite, täielikemate genoomi variatsiooniandmebaaside ja innovaatiliste arvutusalgoritmidega üritab tuvastada täiendvaid tunnustega põhjuslikult seotud mutatsioone kogu alleelisageduste sketri ulatuses.
We focus on deciphering the genetic basis of obesity and of adult height. Obesity is an enormous public health problem with no safe and effective therapies that foretells a future epidemic of diabetes, cardiovascular disease, cancer, and early death. Understanding the specific genetic and biological factors that control susceptibility or resistance to obesity will provide crucial clues that can guide the design of new, critically-needed interventions and therapies. Adult height is the endpoint of childhood growth, a fundamental developmental process and marker of childhood health, but is also the classic model polygenic trait because it is highly heritable and easily measured. As such, studying height has facilitated the development (by us and others) of genetic and computational methods that have been applied broadly to other polygenic traits and diseases. Aim 1. Perform the largest GWAS to date, using >1.5 million deeply imputed samples from multiple ancestries, focused on measures of obesity and height. Within the GIANT framework, we will coordinate the generation of and perform the meta-analysis of deeply imputed GWAS data, to find associated variants not discoverable in previous GWAS. We will provide infrastructure for imputation to deep reference panels (including the haplotype reference consortium panel), and perform QC and meta-analysis of association data. Aim 2. Assemble and analyze data from >100,000 exome and whole genome sequences to discover low frequency and rare variants, coding and noncoding, that influence measures of obesity and height. We will make available infrastructure/software for sequence processing, variant calling and analysis. We will QC and perform meta-analysis of single variant associations and gene-based tests of rarer coding variants. Aim 3. Integrate the association results from common and rare/low-frequency variants, and use complementary data sets to implicate causal genes and biological processes. We will use the GWAS results to guide association tests of aggregations of rare noncoding variants from sequence data. We will also use integrative computational methods – developed, tested and/or refined using height – to interpret the association results for obesity. We will use expression, metabolite, epigenetic, and other genomic data to prioritize genes, gene sets, regulatory elements and metabolites as likely causal contributors to obesity.