Efficient Bayesian mixed-model analysis increases association power in large cohorts.
Linear mixed models are a powerful statistical tool for identifying genetic associations and avoiding confounding. However, existing methods are computationally intractable in large cohorts and may not optimize power. All existing methods require time cost O(MN(2)) (where N is the number of samples and M is the number of SNPs) and implicitly assume an infinitesimal genetic architecture in which effect sizes are normally distributed, which can limit power. Here we present a far more efficient mixed-model association method, BOLT-LMM, which requires only a small number of O(MN) time iterations and increases power by modeling more realistic, non-infinitesimal genetic architectures via a Bayesian mixture prior on marker effect sizes. We applied BOLT-LMM to 9 quantitative traits in 23,294 samples from the Women's Genome Health Study (WGHS) and observed significant increases in power, consistent with simulations. Theory and simulations show that the boost in power increases with cohort size, making BOLT-LMM appealing for genome-wide association studies in large cohorts.
|Year of Publication||
|PubMed Central ID||
K99 HL122515 / HL / NHLBI NIH HHS / United States
R01 HL043851 / HL / NHLBI NIH HHS / United States
R01 HG006399 / HG / NHGRI NIH HHS / United States
CA047988 / CA / NCI NIH HHS / United States
UM1 CA182913 / CA / NCI NIH HHS / United States
F32 HG007805 / HG / NHGRI NIH HHS / United States
R01 CA047988 / CA / NCI NIH HHS / United States
R01 GM105857 / GM / NIGMS NIH HHS / United States
R01 HL080467 / HL / NHLBI NIH HHS / United States
HL080467 / HL / NHLBI NIH HHS / United States