Hidden Heritability and Risk-Prediction Based on Genome-Wide Association Studies

• Activity Number: 460
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Nilanjan Chatterjee*+ and JuHyun Park and Joshua Sampson
• Companies: National Cancer Institute and National Cancer Instutute and DCEG, National Cancer Institute
• Keywords: case-control studies ; polygenic models ; risk discrimination ; variable selection ; penalized regression ; shriknage estimation

Abstract

We report a new model to project the predictive performance of polygenic models based on the number and distribution of effect sizes for the underlying susceptibility alleles, the size of the training dataset and the balance of true and false positives among loci selected in the models. Using effect-size distributions derived from discoveries from the largest genome-wide association studies and estimates of hidden heritability, we assess the predictive ability of common Single Nucleotide Polymorphisms (SNP) for ten complex traits. We project that while 45% of the total variance of adult height has been attributed to common SNPs, a model built based on one million people may only explain 33.4% of variance of the trait in an independent sample. Models built based on current GWAS data sets can identify 3.0%, 1.1%, and 7.0%, of the populations who are at two-fold or higher than average risk for Type 2 diabetes, coronary artery disease and prostate cancer, respectively. By tripling the sample size in the future, the corresponding percentages could be elevated to 18.8%, 6.1%, and 12.2%, respectively.