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Abstract:
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Complex traits are thought to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effect models (LMM) can account for correlations due to relatedness but are not applicable in high-dimensional settings. To overcome these challenges, we develop a general penalized LMM framework that simultaneously selects and estimates variables, accounting for between individual correlations, in one step. Our method can accommodate several sparsity inducing penalties such as the lasso, elastic net and group lasso, and also readily handles prior annotation information in the form of weights. We develop a groupwise-majorization descent algorithm which is highly scalable, computationally efficient and has theoretical guarantees of the convergence. We apply our method to identify SNPs that predict bone mineral density in the UK Biobank cohort. This approach can also be used to generate genetic risk scores and finding groups of predictors associated with the response, such as variants within a gene or pathway.
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