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We collated GWAS data from multiple traits measured accross several diverse cohortse cohorts: UK Biobank, FINRISK, Chinese NIPT, Biobank Japan, APCDR and PAGE. We evaluated how robust signals of polygenic adaptation are to the choice of GWAS cohort used to identify associated variants and their effect size estimates. We show that meta analysis creates overdispersion across populations which appears likes signs of polygenetic seleciton.
Combined effect of the genetic and lifestyle determinants of metabolic syndrome on cardiometabolic risk
Metabolic syndrome is a key risk factor for the development of type 2 diabetes and cardiovascular disease. This proposal seeks to leverage the large sample size of the UK Biobank data to study i) the effect of known genetic loci associated with metabolic syndrome or its subcomponents with the risk of cardiometabolic disease; ii) to examine whether these genetic loci interact with modifiable lifestyle risk factors of cardiometabolic disease; iii) to test whether the loci are associated with the levels of modifiable lifestyle risk factors; and iv) to estimate genetic correlations between subcomponents of the metabolic syndrome and its comorbidities By providing novel and clinically relevant information on the combined impact of the genetic and lifestyle determinants of metabolic syndrome and cardiometabolic risk, the proposed research is congruent with the stated aim of the UK Biobank to improve ?the prevention, diagnosis, and treatment of a wide range of serious and life-threatening illnesses?. The research will be undertaken by conducting epidemiological analyses to study associations between the genetic and lifestyle determinants of metabolic syndrome with cardiometabolic disease risk. Additional analyses will be performed to examine synergistic effects between the genetic and lifestyle risk factors, and to study the overall genetic relationships between subcomponents of the metabolic syndrome and its comorbidities We would like to include the full cohort