Computationally efficient whole-genome regression for quantitative and binary traits
By J. Mbatchou, Leland Barnard, J. Backman, A. Marcketta, J. Kosmicki, Andrey Ziyatdinov, Christian Benner, C. O’Dushlaine, Mathew Barber, Boris Boutkov and 7 more. Nature Genetics, 2020
1,233 citations recorded by the source corpus.
Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine-learning method called REGENIE for fitting a whole-genome regression model for quantitative and binary phenotypes that is substantially faster than alternatives in multi-trait analyses while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes and requires only local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives, which must load genome-wide matrices into memory. This results in substantial savings in compute time and memory usage. We introduce a fast, approximate Firth logistic regression test for unbalanced case–control phenotypes. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach using the UK Biobank dataset with up to 407,746 individuals. REGENIE is a whole-genome regression method based on ridge regression that enables highly parallelized analysis of quantitative and binary traits in biobank-scale data with reduced computational requirements.
Subfields: Genetics
View Paper: https://www.semanticscholar.org/paper/e43b9f447e909682f307950e23e28427c4c967da
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Citations in the corpus
- Plasma proteomic associations with genetics and health in the UK Biobank (2023, methodology): “GWAS analyses were performed using REGENIE v.2.2.1 through a two-step procedure to account for population structure detailed previously 59 .”
- A generalized linear mixed model association tool for biobank-scale data (2021, methodology): “…performance of REGENIE (v.2.0.2) using the same data ( n = 400,000) and on the same platform as we used to benchmark fastGWA-GLMM and SAIGE in three scenarios: a single trait, 8 traits (the set of traits included in our real data analysis) and 50 traits (the number used in the REGENIE paper 18 ).”