Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI
Abstract Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. He...
| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45135-z |
| _version_ | 1797273927112720384 |
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| author | Quan Sun Bryce T. Rowland Jiawen Chen Anna V. Mikhaylova Christy Avery Ulrike Peters Jessica Lundin Tara Matise Steve Buyske Ran Tao Rasika A. Mathias Alexander P. Reiner Paul L. Auer Nancy J. Cox Charles Kooperberg Timothy A. Thornton Laura M. Raffield Yun Li |
| author_facet | Quan Sun Bryce T. Rowland Jiawen Chen Anna V. Mikhaylova Christy Avery Ulrike Peters Jessica Lundin Tara Matise Steve Buyske Ran Tao Rasika A. Mathias Alexander P. Reiner Paul L. Auer Nancy J. Cox Charles Kooperberg Timothy A. Thornton Laura M. Raffield Yun Li |
| author_sort | Quan Sun |
| collection | DOAJ |
| description | Abstract Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women’s Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals. |
| first_indexed | 2024-03-07T14:51:07Z |
| format | Article |
| id | doaj.art-fe84353ad55c46c3a18b767c25d45ba3 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-07T14:51:07Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-fe84353ad55c46c3a18b767c25d45ba32024-03-05T19:42:49ZengNature PortfolioNature Communications2041-17232024-02-0115111410.1038/s41467-024-45135-zImproving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDIQuan Sun0Bryce T. Rowland1Jiawen Chen2Anna V. Mikhaylova3Christy Avery4Ulrike Peters5Jessica Lundin6Tara Matise7Steve Buyske8Ran Tao9Rasika A. Mathias10Alexander P. Reiner11Paul L. Auer12Nancy J. Cox13Charles Kooperberg14Timothy A. Thornton15Laura M. Raffield16Yun Li17Department of Biostatistics, University of North Carolina at Chapel HillDepartment of Biostatistics, University of North Carolina at Chapel HillDepartment of Biostatistics, University of North Carolina at Chapel HillDepartment of Biostatistics, University of WashingtonDepartment of Epidemiology, University of North Carolina at Chapel HillDivision of Public Health Sciences, Fred Hutchinson Cancer CenterDivision of Public Health Sciences, Fred Hutchinson Cancer CenterDepartment of Genetics, Rutgers UniversityDepartment of Statistics, Rutgers UniversityVanderbilt Genetics Institute, Vanderbilt University Medical CenterDepartment of Medicine, Johns Hopkins UniversityDepartment of Epidemiology, University of WashingtonDivision of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of WisconsinVanderbilt Genetics Institute, Vanderbilt University Medical CenterDivision of Public Health Sciences, Fred Hutchinson Cancer CenterDepartment of Biostatistics, University of WashingtonDepartment of Genetics, University of North Carolina at Chapel HillDepartment of Biostatistics, University of North Carolina at Chapel HillAbstract Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women’s Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.https://doi.org/10.1038/s41467-024-45135-z |
| spellingShingle | Quan Sun Bryce T. Rowland Jiawen Chen Anna V. Mikhaylova Christy Avery Ulrike Peters Jessica Lundin Tara Matise Steve Buyske Ran Tao Rasika A. Mathias Alexander P. Reiner Paul L. Auer Nancy J. Cox Charles Kooperberg Timothy A. Thornton Laura M. Raffield Yun Li Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI Nature Communications |
| title | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI |
| title_full | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI |
| title_fullStr | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI |
| title_full_unstemmed | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI |
| title_short | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI |
| title_sort | improving polygenic risk prediction in admixed populations by explicitly modeling ancestral differential effects via gaudi |
| url | https://doi.org/10.1038/s41467-024-45135-z |
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