Bayesian multivariate genetic analysis improves translational insights
Summary: While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior me...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223019314 |
| _version_ | 1797647834070122496 |
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| author | Sarah M. Urbut Satoshi Koyama Whitney Hornsby Rohan Bhukar Sumeet Kheterpal Buu Truong Margaret S. Selvaraj Benjamin Neale Christopher J. O’Donnell Gina M. Peloso Pradeep Natarajan |
| author_facet | Sarah M. Urbut Satoshi Koyama Whitney Hornsby Rohan Bhukar Sumeet Kheterpal Buu Truong Margaret S. Selvaraj Benjamin Neale Christopher J. O’Donnell Gina M. Peloso Pradeep Natarajan |
| author_sort | Sarah M. Urbut |
| collection | DOAJ |
| description | Summary: While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale. |
| first_indexed | 2024-03-11T15:23:20Z |
| format | Article |
| id | doaj.art-99a66d8bd2d641c4868aafb2a30f5d8f |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-11T15:23:20Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-99a66d8bd2d641c4868aafb2a30f5d8f2023-10-28T05:08:45ZengElsevieriScience2589-00422023-10-012610107854Bayesian multivariate genetic analysis improves translational insightsSarah M. Urbut0Satoshi Koyama1Whitney Hornsby2Rohan Bhukar3Sumeet Kheterpal4Buu Truong5Margaret S. Selvaraj6Benjamin Neale7Christopher J. O’Donnell8Gina M. Peloso9Pradeep Natarajan10Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USACardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine Harvard Medical School, Boston, MA 02115, USACardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine Harvard Medical School, Boston, MA 02115, USACardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine Harvard Medical School, Boston, MA 02115, USACardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USACardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine Harvard Medical School, Boston, MA 02115, USACardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine Harvard Medical School, Boston, MA 02115, USAProgram in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine Harvard Medical School, Boston, MA 02115, USA; Analytic Translational and Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USADepartment of Medicine Harvard Medical School, Boston, MA 02115, USA; VA Boston Department of Veterans Affairs, Boston, MA 02130, USADepartment of Biostatistics, Boston University School of Public Health, Boston, MA 02218, USACardiovascular Research Center, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA 02142, USA; Department of Medicine Harvard Medical School, Boston, MA 02115, USA; Corresponding authorSummary: While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.http://www.sciencedirect.com/science/article/pii/S2589004223019314Human geneticsBiocomputational methodComputational bioinformaticsGenomic analysisAssociation analysis |
| spellingShingle | Sarah M. Urbut Satoshi Koyama Whitney Hornsby Rohan Bhukar Sumeet Kheterpal Buu Truong Margaret S. Selvaraj Benjamin Neale Christopher J. O’Donnell Gina M. Peloso Pradeep Natarajan Bayesian multivariate genetic analysis improves translational insights iScience Human genetics Biocomputational method Computational bioinformatics Genomic analysis Association analysis |
| title | Bayesian multivariate genetic analysis improves translational insights |
| title_full | Bayesian multivariate genetic analysis improves translational insights |
| title_fullStr | Bayesian multivariate genetic analysis improves translational insights |
| title_full_unstemmed | Bayesian multivariate genetic analysis improves translational insights |
| title_short | Bayesian multivariate genetic analysis improves translational insights |
| title_sort | bayesian multivariate genetic analysis improves translational insights |
| topic | Human genetics Biocomputational method Computational bioinformatics Genomic analysis Association analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223019314 |
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