This study aimed at identifying single nucleotide polymorphisms (SNP) associated with height and body mass index. The design of our study consisted of combining publicly available summary statistics from previous genome-wide association studies of height and BMI in ~250,000 individuals with new GWAS of height and BMI in participants from the UK Biobank (UKB). Our study was based on 456,426 participants of the UK Biobank. Using genotyped and imputed genotypes at ~1.1 million SNPs, we classified these participants as European descents. Our analyses of height and BMI were based on the UKB fields 50-0.0 and 21001-0.0 respectively. Height and BMI were adjusted for age (UKB field 21022-0.0), sex (inferred from genotypes), assessment centre (UKB field 54-0.0), genotyping chip and 10 genotypic principal components (PCs) calculated from 132,102 out of the 147,604 genotyped SNPs pre-selected by the UKB quality control team for PC analysis. The difference is explained by our additional quality control of SNPs (minor allele frequency >0.01, genotype call rate >95% and Hardy Weinberg test P-value > 10-6) applied to a different set of samples as compared to what performed by the UKB quality control team. PCs were calculated using the flashPCA software.
The limits of predicting complex traits and diseases from genetic data
Results from genome-wide association studies (GWAS) have proven valuable for understanding the genetic architecture of complex traits and are potentially valuable for predicting disease risk. As GWAS sample sizes grow the prediction accuracy will increase and may eventually yield clinically actionable predictions, for example by stratifying individuals on risk. One limitation for making accurate disease risk prediction is the experimental sample size. We aim to quantify the limits of predicting disease risk for an individual by developing sophisticated statistical methods and applying them to quantitative traits in the large UK Biobank sample. Understanding of the limitations of predicting an individual?s risk of disease using genetic data is of great importance for disease prevention, and meets the UK Biobank?s stated purposes. Gaining accurate genetic risk predictors through the development of robust and powerful statistical methods, together with a large discovery sample (e.g. UK Biobank data), is critical for use in disease screening programs to stratify the population, which is expected to reduce the financial burden of the health system for the whole society. Through a focus on quantitative phenotypes, we will develop new approaches applicable to predicting disease risk. The genetic marker data will be used to estimate genome-wide relationships, which we will then correlate with phenotype. This analysis will simultaneously quantify how much of the observed individual differences in phenotype is due to genetic factors, and how accurate a genetic predictor can be. The accuracy of prediction will then be tested. We focus on well-characterised quantitative phenotypes of height, body mass index, blood pressure, osteoporosis, and metabolism. To have maximum power to predict risk of disease, we require access to the full cohort, because one of the main limiting factors of prediction is sample size. Our analyses will thus require individual-level imputed genotype and phenotype data. We request a wide range of phenotypes because prediction accuracy is sensitive to the underlying genetic architecture and we wish to quantify the limits of prediction across multiple diseases.
|Lead investigator:||Professor Peter Visscher|
|Lead institution:||University of Queensland|
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|3075||Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry||Yengo et al||2018||Human Molecular Genetics (2018)|