Computationally efficient whole-genome regression for quantitative and binary traits
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 quantitat...
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| Published in: | Nature genetics Vol. 53; no. 7; pp. 1097 - 1103 |
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| Main Authors: | , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Nature Publishing Group US
01.07.2021
Nature Publishing Group |
| Subjects: | |
| ISSN: | 1061-4036, 1546-1718, 1546-1718 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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. 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. 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.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. |
| Audience | Academic |
| Author | Reid, Jeffrey Mbatchou, Joelle Benner, Christian Ferreira, Manuel Kosmicki, Jack A. Baras, Aris Abecasis, Goncalo Maxwell, Evan Boutkov, Boris Marcketta, Anthony Marchini, Jonathan Habegger, Lukas Ziyatdinov, Andrey Barber, Mathew Backman, Joshua Barnard, Leland O’Dushlaine, Colm |
| Author_xml | – sequence: 1 givenname: Joelle orcidid: 0000-0002-2245-3743 surname: Mbatchou fullname: Mbatchou, Joelle organization: Regeneron Genetics Center – sequence: 2 givenname: Leland surname: Barnard fullname: Barnard, Leland organization: Regeneron Genetics Center – sequence: 3 givenname: Joshua surname: Backman fullname: Backman, Joshua organization: Regeneron Genetics Center – sequence: 4 givenname: Anthony surname: Marcketta fullname: Marcketta, Anthony organization: Regeneron Genetics Center – sequence: 5 givenname: Jack A. surname: Kosmicki fullname: Kosmicki, Jack A. organization: Regeneron Genetics Center – sequence: 6 givenname: Andrey surname: Ziyatdinov fullname: Ziyatdinov, Andrey organization: Regeneron Genetics Center – sequence: 7 givenname: Christian surname: Benner fullname: Benner, Christian organization: Regeneron Genetics Center – sequence: 8 givenname: Colm surname: O’Dushlaine fullname: O’Dushlaine, Colm organization: Regeneron Genetics Center – sequence: 9 givenname: Mathew surname: Barber fullname: Barber, Mathew organization: Regeneron Genetics Center – sequence: 10 givenname: Boris surname: Boutkov fullname: Boutkov, Boris organization: Regeneron Genetics Center – sequence: 11 givenname: Lukas surname: Habegger fullname: Habegger, Lukas organization: Regeneron Genetics Center – sequence: 12 givenname: Manuel surname: Ferreira fullname: Ferreira, Manuel organization: Regeneron Genetics Center – sequence: 13 givenname: Aris orcidid: 0000-0002-6830-3396 surname: Baras fullname: Baras, Aris organization: Regeneron Genetics Center – sequence: 14 givenname: Jeffrey orcidid: 0000-0001-8645-4713 surname: Reid fullname: Reid, Jeffrey organization: Regeneron Genetics Center – sequence: 15 givenname: Goncalo surname: Abecasis fullname: Abecasis, Goncalo organization: Regeneron Genetics Center – sequence: 16 givenname: Evan orcidid: 0000-0002-7325-7531 surname: Maxwell fullname: Maxwell, Evan organization: Regeneron Genetics Center – sequence: 17 givenname: Jonathan orcidid: 0000-0003-0610-8322 surname: Marchini fullname: Marchini, Jonathan email: jonathan.marchini@regeneron.com organization: Regeneron Genetics Center |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34017140$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Nature America, Inc. 2021 2021. The Author(s), under exclusive licence to Springer Nature America, Inc. COPYRIGHT 2021 Nature Publishing Group Copyright Nature Publishing Group Jul 2021 |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer Nature America, Inc. 2021 – notice: 2021. The Author(s), under exclusive licence to Springer Nature America, Inc. – notice: COPYRIGHT 2021 Nature Publishing Group – notice: Copyright Nature Publishing Group Jul 2021 |
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