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Award ID contains: 2152687

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  1. ; ; ; ; (, SIAM Journal on Matrix Analysis and Applications)
    Free, publicly-accessible full text available March 31, 2026
  2. ; ; (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  3. ; (, SIAM News)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, SIAM News)
    Accepted Manuscript Full Text Available
  5. ; ; (, SIAM Journal on Matrix Analysis and Applications)
    Full Text Available 
  6. ; ; ; ; (, Genome Research)
    Linear mixed models (LMMs) have been widely used in genome-wide association studies to control for population stratification and cryptic relatedness. However, estimating LMM parameters is computationally expensive, necessitating large-scale matrix operations to build the genetic relationship matrix (GRM). Over the past 25 years, Randomized Linear Algebra has provided alternative approaches to such matrix operations by leveragingmatrix sketching, which often results in provably accurate fast and efficient approximations. We leverage matrix sketching to develop a fast and efficient LMM method calledMatrix-Sketching LMM (MaSk-LMM) by sketching the genotype matrix to reduce its dimensions and speed up computations. Our framework comes with both theoretical guarantees and a strong empirical performance compared to the current state-of-the-art for simulated traits and complex diseases. 
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