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Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score
BACKGROUND: Polygenic risk score (PRS) analysis is used to predict disease risk. Although PRS has been shown to have great potential in improving clinical care, PRS accuracy assessment has been mainly focused on European ancestry. This study aimed to develop an accurate genetic risk score for knee o...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258963/ https://www.ncbi.nlm.nih.gov/pubmed/37309008 http://dx.doi.org/10.1186/s13075-023-03082-y |
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author | Morita, Yugo Kamatani, Yoichiro Ito, Hiromu Ikegawa, Shiro Kawaguchi, Takahisa Kawaguchi, Shuji Takahashi, Meiko Terao, Chikashi Ito, Shuji Nishitani, Kohei Nakamura, Shinichiro Kuriyama, Shinichi Tabara, Yasuharu Matsuda, Fumihiko Matsuda, Shuichi |
author_facet | Morita, Yugo Kamatani, Yoichiro Ito, Hiromu Ikegawa, Shiro Kawaguchi, Takahisa Kawaguchi, Shuji Takahashi, Meiko Terao, Chikashi Ito, Shuji Nishitani, Kohei Nakamura, Shinichiro Kuriyama, Shinichi Tabara, Yasuharu Matsuda, Fumihiko Matsuda, Shuichi |
author_sort | Morita, Yugo |
collection | PubMed |
description | BACKGROUND: Polygenic risk score (PRS) analysis is used to predict disease risk. Although PRS has been shown to have great potential in improving clinical care, PRS accuracy assessment has been mainly focused on European ancestry. This study aimed to develop an accurate genetic risk score for knee osteoarthritis (OA) using a multi-population PRS and leveraging a multi-trait PRS in the Japanese population. METHODS: We calculated PRS using PRS-CS-auto, derived from genome-wide association study (GWAS) summary statistics for knee OA in the Japanese population (same ancestry) and multi-population. We further identified risk factor traits for which PRS could predict knee OA and subsequently developed an integrated PRS based on multi-trait analysis of GWAS (MTAG), including genetically correlated risk traits. PRS performance was evaluated in participants of the Nagahama cohort study who underwent radiographic evaluation of the knees (n = 3,279). PRSs were incorporated into knee OA integrated risk models along with clinical risk factors. RESULTS: A total of 2,852 genotyped individuals were included in the PRS analysis. The PRS based on Japanese knee OA GWAS was not associated with knee OA (p = 0.228). In contrast, PRS based on multi-population knee OA GWAS showed a significant association with knee OA (p = 6.7 × 10(−5), odds ratio (OR) per standard deviation = 1.19), whereas PRS based on MTAG of multi-population knee OA, along with risk factor traits such as body mass index GWAS, displayed an even stronger association with knee OA (p = 5.4 × 10(−7), OR = 1.24). Incorporating this PRS into traditional risk factors improved the predictive ability of knee OA (area under the curve, 74.4% to 74.7%; p = 0.029). CONCLUSIONS: This study showed that multi-trait PRS based on MTAG, combined with traditional risk factors, and using large sample size multi-population GWAS, significantly improved predictive accuracy for knee OA in the Japanese population, even when the sample size of GWAS of the same ancestry was small. To the best of our knowledge, this is the first study to show a statistically significant association between the PRS and knee OA in a non-European population. TRIAL REGISTRATION: No. C278. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-023-03082-y. |
format | Online Article Text |
id | pubmed-10258963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102589632023-06-13 Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score Morita, Yugo Kamatani, Yoichiro Ito, Hiromu Ikegawa, Shiro Kawaguchi, Takahisa Kawaguchi, Shuji Takahashi, Meiko Terao, Chikashi Ito, Shuji Nishitani, Kohei Nakamura, Shinichiro Kuriyama, Shinichi Tabara, Yasuharu Matsuda, Fumihiko Matsuda, Shuichi Arthritis Res Ther Research BACKGROUND: Polygenic risk score (PRS) analysis is used to predict disease risk. Although PRS has been shown to have great potential in improving clinical care, PRS accuracy assessment has been mainly focused on European ancestry. This study aimed to develop an accurate genetic risk score for knee osteoarthritis (OA) using a multi-population PRS and leveraging a multi-trait PRS in the Japanese population. METHODS: We calculated PRS using PRS-CS-auto, derived from genome-wide association study (GWAS) summary statistics for knee OA in the Japanese population (same ancestry) and multi-population. We further identified risk factor traits for which PRS could predict knee OA and subsequently developed an integrated PRS based on multi-trait analysis of GWAS (MTAG), including genetically correlated risk traits. PRS performance was evaluated in participants of the Nagahama cohort study who underwent radiographic evaluation of the knees (n = 3,279). PRSs were incorporated into knee OA integrated risk models along with clinical risk factors. RESULTS: A total of 2,852 genotyped individuals were included in the PRS analysis. The PRS based on Japanese knee OA GWAS was not associated with knee OA (p = 0.228). In contrast, PRS based on multi-population knee OA GWAS showed a significant association with knee OA (p = 6.7 × 10(−5), odds ratio (OR) per standard deviation = 1.19), whereas PRS based on MTAG of multi-population knee OA, along with risk factor traits such as body mass index GWAS, displayed an even stronger association with knee OA (p = 5.4 × 10(−7), OR = 1.24). Incorporating this PRS into traditional risk factors improved the predictive ability of knee OA (area under the curve, 74.4% to 74.7%; p = 0.029). CONCLUSIONS: This study showed that multi-trait PRS based on MTAG, combined with traditional risk factors, and using large sample size multi-population GWAS, significantly improved predictive accuracy for knee OA in the Japanese population, even when the sample size of GWAS of the same ancestry was small. To the best of our knowledge, this is the first study to show a statistically significant association between the PRS and knee OA in a non-European population. TRIAL REGISTRATION: No. C278. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-023-03082-y. BioMed Central 2023-06-12 2023 /pmc/articles/PMC10258963/ /pubmed/37309008 http://dx.doi.org/10.1186/s13075-023-03082-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Morita, Yugo Kamatani, Yoichiro Ito, Hiromu Ikegawa, Shiro Kawaguchi, Takahisa Kawaguchi, Shuji Takahashi, Meiko Terao, Chikashi Ito, Shuji Nishitani, Kohei Nakamura, Shinichiro Kuriyama, Shinichi Tabara, Yasuharu Matsuda, Fumihiko Matsuda, Shuichi Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score |
title | Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score |
title_full | Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score |
title_fullStr | Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score |
title_full_unstemmed | Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score |
title_short | Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score |
title_sort | improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258963/ https://www.ncbi.nlm.nih.gov/pubmed/37309008 http://dx.doi.org/10.1186/s13075-023-03082-y |
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