Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI
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 de...
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| Published in: | Nature communications Vol. 15; no. 1; pp. 1016 - 14 |
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| Main Authors: | , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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03.02.2024
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| ISSN: | 2041-1723, 2041-1723 |
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| 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.
Most polygenic risk score (PRS) methods focus only on individuals with distinct primary continental ancestry, without accommodating recently-admixed individuals. Here, the authors develop a novel penalized regression-based PRS method specifically designed for admixed individuals. |
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| AbstractList | 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. 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. 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.Most polygenic risk score (PRS) methods focus only on individuals with distinct primary continental ancestry, without accommodating recently-admixed individuals. Here, the authors develop a novel penalized regression-based PRS method specifically designed for admixed individuals. 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. Most polygenic risk score (PRS) methods focus only on individuals with distinct primary continental ancestry, without accommodating recently-admixed individuals. Here, the authors develop a novel penalized regression-based PRS method specifically designed for admixed individuals. 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.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. |
| ArticleNumber | 1016 |
| Author | Matise, Tara Mikhaylova, Anna V. Avery, Christy Auer, Paul L. Mathias, Rasika A. Li, Yun Thornton, Timothy A. Buyske, Steve Kooperberg, Charles Rowland, Bryce T. Tao, Ran Lundin, Jessica Sun, Quan Peters, Ulrike Cox, Nancy J. Raffield, Laura M. Reiner, Alexander P. Chen, Jiawen |
| Author_xml | – sequence: 1 givenname: Quan orcidid: 0000-0001-8324-2803 surname: Sun fullname: Sun, Quan organization: Department of Biostatistics, University of North Carolina at Chapel Hill – sequence: 2 givenname: Bryce T. surname: Rowland fullname: Rowland, Bryce T. organization: Department of Biostatistics, University of North Carolina at Chapel Hill – sequence: 3 givenname: Jiawen surname: Chen fullname: Chen, Jiawen organization: Department of Biostatistics, University of North Carolina at Chapel Hill – sequence: 4 givenname: Anna V. orcidid: 0000-0002-8350-5228 surname: Mikhaylova fullname: Mikhaylova, Anna V. organization: Department of Biostatistics, University of Washington – sequence: 5 givenname: Christy orcidid: 0000-0002-1044-8162 surname: Avery fullname: Avery, Christy organization: Department of Epidemiology, University of North Carolina at Chapel Hill – sequence: 6 givenname: Ulrike orcidid: 0000-0001-5666-9318 surname: Peters fullname: Peters, Ulrike organization: Division of Public Health Sciences, Fred Hutchinson Cancer Center – sequence: 7 givenname: Jessica surname: Lundin fullname: Lundin, Jessica organization: Division of Public Health Sciences, Fred Hutchinson Cancer Center – sequence: 8 givenname: Tara surname: Matise fullname: Matise, Tara organization: Department of Genetics, Rutgers University – sequence: 9 givenname: Steve orcidid: 0000-0001-8539-5416 surname: Buyske fullname: Buyske, Steve organization: Department of Statistics, Rutgers University – sequence: 10 givenname: Ran surname: Tao fullname: Tao, Ran organization: Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Department of Biostatistics, Vanderbilt University Medical Center – sequence: 11 givenname: Rasika A. orcidid: 0000-0002-2282-9042 surname: Mathias fullname: Mathias, Rasika A. organization: Department of Medicine, Johns Hopkins University – sequence: 12 givenname: Alexander P. orcidid: 0000-0002-1427-4470 surname: Reiner fullname: Reiner, Alexander P. organization: Department of Epidemiology, University of Washington – sequence: 13 givenname: Paul L. orcidid: 0000-0003-1735-8044 surname: Auer fullname: Auer, Paul L. organization: Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin – sequence: 14 givenname: Nancy J. surname: Cox fullname: Cox, Nancy J. organization: Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center – sequence: 15 givenname: Charles orcidid: 0000-0002-7986-8560 surname: Kooperberg fullname: Kooperberg, Charles organization: Division of Public Health Sciences, Fred Hutchinson Cancer Center – sequence: 16 givenname: Timothy A. surname: Thornton fullname: Thornton, Timothy A. organization: Department of Biostatistics, University of Washington – sequence: 17 givenname: Laura M. orcidid: 0000-0002-7892-193X surname: Raffield fullname: Raffield, Laura M. organization: Department of Genetics, University of North Carolina at Chapel Hill – sequence: 18 givenname: Yun orcidid: 0000-0002-9275-4189 surname: Li fullname: Li, Yun email: yun_li@med.unc.edu organization: Department of Biostatistics, University of North Carolina at Chapel Hill, Department of Genetics, University of North Carolina at Chapel Hill |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38310129$$D View this record in MEDLINE/PubMed |
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| Snippet | Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without... Abstract Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental... |
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| SubjectTerms | 45 45/43 631/114/2415 631/208/480 631/208/721 C-reactive protein Genomes Health promotion Humanities and Social Sciences Leukocytes multidisciplinary Polygenic inheritance Risk Science Science (multidisciplinary) Segments |
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| Title | Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI |
| URI | https://link.springer.com/article/10.1038/s41467-024-45135-z https://www.ncbi.nlm.nih.gov/pubmed/38310129 https://www.proquest.com/docview/2921585968 https://www.proquest.com/docview/2922448495 https://pubmed.ncbi.nlm.nih.gov/PMC10838303 https://doaj.org/article/fe84353ad55c46c3a18b767c25d45ba3 |
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