Application of fast mixed model association and principal component analysis methods
Harvard School of Public Health
Dr Alkes Price
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We aim to identify genetic loci that are associated to specific health-related outcomes. More precisely, we will apply a new, more powerful statistical method (BOLT-LMM) to analyze outcomes that have been demonstrated to be heritable in previous genome-wide association studies, including direct health outcomes (disease status) as well as heritable quantitative measurements such as height, BMI and lipid levels associated to some health outcomes. We will investigate only genetic effects and will use environmental exposure data only as covariates in our analyses. This project is restricted to self-reported outcomes and traits measured at baseline. Our discovery of associated loci that could not be discovered using existing methods may potentially lead to actionable drug targets, and is in the public interest. We will analyze each outcome independently: i.e., for each disease code, we will compute association statistics between all genetic markers and the disease code (independent of other outcomes). We will apply a more powerful statistical method to the data than has previously been available. The new method (BOLT-LMM) applies a linear mixed model to analyze all genetic markers simultaneously, enabling a more powerful statistical analysis that is expected to detect associations that other methods miss. In addition to performing BOLT-LMM analysis, we will also compute association statistics using other standard methods for comparison. We will analyze the full cohort.