Journal Article

Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score

Ito, Hiromu, Takahashi, Meiko, Ito, Shuji, Matsuda, Shuichi, Terao, Chikashi, Matsuda, Fumihiko, Kamatani, Yoichiro, Kawaguchi, Shuji, Nishitani, Kohei, Nakamura, Shinichiro, Tabara, Yasuharu, Ikegawa, Shiro, Morita, Yugo, Kawaguchi, Takahisa, Kuriyama, Shinichi

2023

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Analysis / Arrays / Arthritis / Cohort Studies / Datasets / Genetic aspects / Genetics / Genome-wide association studies / Genome-Wide Association Study / Genomes / Genotype & phenotype / Humans / Knee / Knee osteoarthritis / Medicine / Medicine & Public Health / Multi-population / Multi-traits / Multifactorial traits / Orthopedics / Osteoarthritis / Osteoarthritis, Knee / Polygenic risk score / Prevention / Quality control / Rheumatology / Risk Assessment / Risk Factors