Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score
Abstract 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 fo...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | Arthritis Research & Therapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13075-023-03082-y |
| _version_ | 1797801343986958336 |
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| author | Yugo Morita Yoichiro Kamatani Hiromu Ito Shiro Ikegawa Takahisa Kawaguchi Shuji Kawaguchi Meiko Takahashi Chikashi Terao Shuji Ito Kohei Nishitani Shinichiro Nakamura Shinichi Kuriyama Yasuharu Tabara Fumihiko Matsuda Shuichi Matsuda on behalf of the Nagahama study group |
| author_facet | Yugo Morita Yoichiro Kamatani Hiromu Ito Shiro Ikegawa Takahisa Kawaguchi Shuji Kawaguchi Meiko Takahashi Chikashi Terao Shuji Ito Kohei Nishitani Shinichiro Nakamura Shinichi Kuriyama Yasuharu Tabara Fumihiko Matsuda Shuichi Matsuda on behalf of the Nagahama study group |
| author_sort | Yugo Morita |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-13T04:49:02Z |
| format | Article |
| id | doaj.art-f292ab102fa7457d919e69b469ff65ae |
| institution | Directory Open Access Journal |
| issn | 1478-6362 |
| language | English |
| last_indexed | 2024-03-13T04:49:02Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Arthritis Research & Therapy |
| spelling | doaj.art-f292ab102fa7457d919e69b469ff65ae2023-06-18T11:21:12ZengBMCArthritis Research & Therapy1478-63622023-06-0125111210.1186/s13075-023-03082-yImproved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic scoreYugo Morita0Yoichiro Kamatani1Hiromu Ito2Shiro Ikegawa3Takahisa Kawaguchi4Shuji Kawaguchi5Meiko Takahashi6Chikashi Terao7Shuji Ito8Kohei Nishitani9Shinichiro Nakamura10Shinichi Kuriyama11Yasuharu Tabara12Fumihiko Matsuda13Shuichi Matsuda14on behalf of the Nagahama study groupDepartment of Orthopedic Surgery, Kyoto University Graduate School of MedicineCenter for Genomic Medicine, Kyoto University Graduate School of MedicineDepartment of Orthopedic Surgery, Kyoto University Graduate School of MedicineLaboratory for Bone and Joint Diseases, Center for Genomic Medicine, RIKENCenter for Genomic Medicine, Kyoto University Graduate School of MedicineCenter for Genomic Medicine, Kyoto University Graduate School of MedicineCenter for Genomic Medicine, Kyoto University Graduate School of MedicineLaboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical SciencesLaboratory for Bone and Joint Diseases, Center for Genomic Medicine, RIKENDepartment of Orthopedic Surgery, Kyoto University Graduate School of MedicineDepartment of Orthopedic Surgery, Kyoto University Graduate School of MedicineDepartment of Orthopedic Surgery, Kyoto University Graduate School of MedicineCenter for Genomic Medicine, Kyoto University Graduate School of MedicineCenter for Genomic Medicine, Kyoto University Graduate School of MedicineDepartment of Orthopedic Surgery, Kyoto University Graduate School of MedicineAbstract 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.https://doi.org/10.1186/s13075-023-03082-yPolygenic risk scoreKnee osteoarthritisGeneticsMulti-populationMulti-traits |
| spellingShingle | Yugo Morita Yoichiro Kamatani Hiromu Ito Shiro Ikegawa Takahisa Kawaguchi Shuji Kawaguchi Meiko Takahashi Chikashi Terao Shuji Ito Kohei Nishitani Shinichiro Nakamura Shinichi Kuriyama Yasuharu Tabara Fumihiko Matsuda Shuichi Matsuda on behalf of the Nagahama study group Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score Arthritis Research & Therapy Polygenic risk score Knee osteoarthritis Genetics Multi-population Multi-traits |
| 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 | Polygenic risk score Knee osteoarthritis Genetics Multi-population Multi-traits |
| url | https://doi.org/10.1186/s13075-023-03082-y |
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