rs113857285 - TSBP1-AS1, TSBP1

Magnitude 4.5 · 1 study on file

Reported associations

• A generalized linear mixed model association tool for biobank-scale data. - Nature genetics (2021) · Jiang L, Zheng Z, Fang H, Yang J · PubMed 34737426

  Compared with linear mixed model-based genome-wide association (GWA) methods, generalized linear mixed model (GLMM)-based methods have better statistical properties when applied to binary traits but are computationally much slower. In the present study, leveraging efficient sparse matrix-based algorithms, we developed a GLMM-based GWA tool, fastGWA-GLMM, that is severalfold to orders of magnitude faster than the state-of-the-art tools when applied to the UK Biobank (UKB) data and scalable to cohorts with millions of individuals. We show by simulation that the fastGWA-GLMM test statistics of both common and rare variants are well calibrated under the null, even for traits with extreme case-control ratios. We applied fastGWA-GLMM to the UKB data of 456,348 individuals, 11,842,647 variants an

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