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

Arthritis Research & Therapy volume 25, Article number: 103 (2023) Cite this article

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.

Background

Knee osteoarthritis (OA) is a common multifactorial disease that causes major public health problems and is a large economic burden on society [1, 2]. The prevalence of knee OA is approximately 35% in individuals over 65 years of age and has been increasing over the past decades [3, 4]. The conventional risk factors for knee OA include sex (women), obesity, and knee injury [5]. Moreover, knee OA has also been shown to be influenced by genetic factors.

The genetic contribution to OA has been demonstrated in twin studies reporting 45–60% heritability estimates for knee OA [6,7,8]. Moreover, genome-wide association studies (GWAS) have revealed independent susceptibility loci, including rs143384 in the GDF5 gene, for knee OA [9,10,11,12,13,14,15,16]. A previous extensive GWAS meta-analysis for knee OA in 826,690 participants identified 100 independent OA-associated variants and explained up to 11% of the total heritability of knee OA [17, 18]. The discovery of new variants enables a polygenic risk assessment [19], revealing the polygenic architecture of knee OA [20,21,22]. A polygenic risk score (PRS) is a practical tool that enhances disease risk prediction by aggregating the effects of various common variants [19]. Applications to clinical practice or screening programs in society by stratifying populations into risk groups are currently being explored. There has been intense research on the prediction of knee OA using the PRS [18, 23]. However, PRS accuracy remains moderate (odds ratio [OR] of 1.3 per SD for the risk of knee OA and an OR of 1.1 per SD for the risk of knee replacement), and its assessment is limited to the European population. The reason for lower PRS accuracy for the non-European population compared to that for the European population is because of the dearth of well-conducted studies in globally diverse populations [24].

This study aimed to investigate the predictive accuracy of PRS based on OA GWAS summary statistics in the Japanese population and to improve the predictive ability using multi-population and multi-trait PRSs based on multi-trait analysis of GWAS (MTAG) [25].

Materials and methods

Study population

We used phenotypic and genotypic data from the Nagahama Cohort for Comprehensive Human Bioscience, a community-based prospective cohort study. The Nagahama study included 11,645 middle-aged to older adults recruited from 2007 to 2010 and 2016 as the general population living in Nagahama City, a rural city located in central Japan. A total of 8,559 study individuals participated in the first follow-up from 2012 to 2015. Details of participant recruitment have been previously reported [26]. This study was conducted following the principles of the Declaration of Helsinki and was approved by the ethics committee of Kyoto University Graduate School of Medicine and Nagahama Municipal Review Board (no. 278). Written informed consent was obtained from all participants.

Radiographic evaluation of knee OA

Of the total participants, those aged ≥ 60 years (n = 5,018) from 2012 to 2016 were included in the first surveillance and subjected to further radiographic evaluation of the knees; 1,739 participants declined and the remaining 3,279 participants (65.3%) who agreed to participate were included in the present study. The anteroposterior radiography of the knee joints in a fully extended weight-bearing position was evaluated by a registered orthopedic surgeon and a trained examiner using the Kellgren–Lawrence classification (K–L grade) [27]. Knee OA was defined as a K–L grade ≥ 2.

Genotyping and sequencing

Genomic DNA was extracted from peripheral blood samples using standard laboratory procedures. Our genotype data comprised two data sets: genotyping data sets obtained by single-nucleotide polymorphism (SNP) arrays (9,077 samples, regardless of the knee OA phenotypes) and whole genome sequencing (WGS) data (1,322 samples). Genotyping by the SNP arrays was conducted using a series of four BeadChip microarrays (Illumina, San Diego, CA, USA), namely Asian Screening Array-24v1-0 (ASA; 5,249 samples), HumanOmni2.5-4v1 (2.5–4; 1,261 samples), HumanOmni5Exome-4v1 (5Exome; 725 samples), and HumanCoreExome-12v1 (CoreExome; 1,842 samples). WGS of 1,322 samples was conducted using an Illumina HiSeq X Ten sequencer (Illumina). Genotype calling was performed with 1,826 other Japanese samples based on the GATK best practice [28] using the GRCh37 human reference genome.

Genotype quality control

Genotype quality control for SNP arrays was performed for each series of BeadChip arrays. Initially, we excluded a total of 120 samples by discordant sex information (n = 3), ethnic background other than Japanese (n = 21), and low sample call rate < 99% (n = 96). Then, SNPs with low SNP call rate (< 0.99), discordance from the Hardy–Weinberg equilibrium (HWE; p < 1 × 10–6), and low minor allele count (MAC; < 5) in each array were excluded. Finally, we obtained 5,162 samples and 491,997 SNPs for ASA; 1,239 samples and 1,219,577 SNPs for 2.5–4; 723 samples and 1,714,166 SNPs for 5Exome; and 1,833 samples and 249,717 SNPs for CoreExome. The total number of samples that satisfied the quality control (QC) metrics was 8,957. The details are presented in Supplementary Fig. S1a. Genotype QC for WGS was performed as described above for the SNP array. Initially, we obtained 41,888,202 autosomal biallelic SNPs that were marked as PASS by GATK (bcftools -f PASS -v SNP -M 2). Then, we performed sample QC by removing the high discordance rate to the SNP array (> 0.9), ethnic background other than Japanese, high heterozygosity (> 0.05), singleton (> 0.001), and missing rates for at least one chromosome (> 0.1). Only one sample was excluded from the Nagahama study because of a high missing rate, and 12 were removed from the other Japanese samples for miscellaneous reasons. We then excluded 1,974,460 SNPs with a low call rate (< 0.95), 55,712 SNPs that deviated from the HWE (p < 1 × 10–6), and 18,818,084 SNPs with low MAC (< 2). Finally, we obtained 3,135 samples, including 1,321 samples from the Nagahama cohort, of 21,039,946 SNPs (Supplementary Fig. S1b).

Genotype imputation for the SNP array data

Imputation of the quality-controlled SNP genotypes of the array dataset was performed using SHAPEIT2 and Minimac4 [29] in each SNP array using 3,135 quality-controlled WGS samples as a reference panel. SNPs with low imputation quality metrics (r2 < 0.8) and low minor allele frequency (MAF; < 0.01) in at least one array were excluded from the imputation datasets. The 1,321 overlapping samples were excluded from the imputed genotype dataset, and the genotype of WGS was used instead. Finally, we constructed a genotype dataset of 4,693,074 SNPs from 8,957 samples for further analysis.

Sample extraction for PRS calculation

Of the 3,269 individuals with radiographic data of the knees, we excluded subjects with a history of rheumatoid arthritis (n = 76), outliers in principal component analysis who were defined as more than three interquartile ranges in the first and second principal components (n = 89), and encrypted first-degree relatedness (Pi-Hat > 0.25, n = 252) [30] (Supplementary Fig. S1c).

Genotype QC for extracted samples

PRS was calculated for the merged imputation and WGS datasets after QC as follows: (1) SNPs with imputation quality metrics r2 < 0.8 (12,250,404 SNPs for ASA; 11,074,943 SNPs for 2.5–4; 11,324,741 SNPs for 5Exome; and 13,920,025 SNPs for CoreExome). Thereafter those with MAF < 0.01 (2,947,830 SNPs for ASA; 3,554,382 SNPs for 2.5–4; 3,324,770 SNPs for 5Exome; and 1,784,244 SNPs for CoreExome) were excluded from imputation datasets; and (2) SNPs with MAF < 0.01 (13,985,443 SNPs) were excluded from WGS dataset. We obtained 1,644 samples and 5,841,712 SNPs for ASA; 186 samples and 6,410,621 SNPs for 2.5–4; 119 samples and 6,390,435 SNPs for 5Exome; 274 samples and 5,335,677 SNPs for CoreExome; and 629 samples and 7,054,503 SNPs for WGS dataset. Finally, 4,693,074 variants common to all imputation and WGS datasets were extracted from each dataset to avoid bias due to the different number of variants in the datasets, and all datasets were merged (Supplementary Fig. S1d). Data management and analyses were performed using PLINK 2.0, and data merging was performed using PLINK 1.07 [31].

PRS calculation based on single traits

We calculated PRS as the sum of alleles associated with a trait weighted by the effect size determined by a previous GWAS using PRS-CS [32] with East Asian linkage disequilibrium (LD) score reference panels from the 1000 Genome Project. We used the ‘auto’ mode of PRS-CS to derive model parameters from a fully Bayesian approach without a validation dataset. The PRS of each individual was calculated based on best-guess genotypes, and only the SNPs found at HapMap3 sites were included to contribute to the score to reduce the computational cost [32, 33].

A flowchart of the PRS analysis is shown in Fig. 1. In the OA PRS calculation, the Japanese knee OA PRS was computed by obtaining the largest Japanese knee OA GWAS summary statistics that identified two genome-wide significant SNPs in the human leukocyte antigen class II/III locus associated with susceptibility to knee OA using 4,800 Japanese participants [9]. To calculate for the larger sample size OA PRS and summary statistics of extensive GWAS meta-analysis for OA (10 different OA phenotypes [17] and knee pain GWAS summary statistics [34]) in 826,690 participants across 13 cohorts worldwide (encompassing Japanese knee OA GWAS [9]) were obtained.

Fig. 1
figure 1

Polygenic risk score analysis flow chart. PRS, polygenic risk score; GWAS, genome-wide association study; OA, osteoarthritis; TKR, total knee replacement; BMI, body mass index; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; Ca, calcium; TJR, total knee and/or hip joint replacement; UKB, UK Biobank; MTAG, multi-trait analysis of genome-wide association

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To identify the risk factors for knee OA, GWAS summary statistics of 62 quantitative traits were obtained from the Biobank Japan (BBJ) Project [35,36,37,38], and the Japanese single-ancestry PRSs were calculated. We calculated the multi-population PRS [39,40,41] of the corresponding traits in the MTAG of Japanese single-ancestry GWAS using GWAS summary statistics published in the UK biobank (Pan-UKB team. https://pan.ukbb.broadinstitute.org. 2020.; Supplementary Table S1). Before analysis, SNPs with MAF < 0.01 and imputation quality metrics r2 < 0.8 were excluded from the GWAS summary statistics.

We also calculated PRSs using LDpred2-auto [42] for Japanese knee OA and traits that were suggested as risk factors for knee OA (PRS-CS analysis, p < 0.05). For LDpred2-auto, only the SNPs found at HapMap3 sites were included, and the posterior effect size was computed using 30 initial values for the proportion of causal variants from 1 × 10–4 to 0.2 equally spaced on the log scale. The initial heritability value was obtained using constrained LD score regression. After filtering the outlier predictions (> 3 median absolute deviations from their median), we used the mean of the remaining predictions as the final PRS.

PRS calculation based on multi-traits

We selected GWAS for traits suggested as risk factors for knee OA (PRS analysis, p < 0.05). We identified traits that were genetically correlated with each other in the selected traits using the linkage disequilibrium score regression [43]. Traits with a genetic correlation p-value < 0.05 were identified as suitable for inclusion in the MTAG. Of these, the traits that were only genetically correlated with each other we incorporated into traits associated with knee OA in the PRS analysis using MTAG (version 1.0.7). East Asian LD score reference panels from the 1000 Genome Project were used for all the analyses.

Statistical analysis

Statistical analyses were performed using the R statistical software version 4.0.4 (R Foundation for Statistical Computing, Vienna, Austria). The association between PRSs and knee OA in the Nagahama study participants was evaluated using a logistic regression model. We calculated adjusted OR per SD and 95% confidence interval (CI) for the risk of knee OA. Age, sex, 10 principal components, and the type of dataset to which the sample belongs (SNP array or WGS dataset) were included as covariates. Disease liability explained by PRS was estimated by the conversion of observed PVE to the R2 on the liability scale using a linear model with disease prevalence in the Nagahama study participants (37.1%) [44]. To evaluate the ability of PRS in case/control discrimination, receiver operating characteristic (ROC) analyses were performed by plotting the true- against the false-positive rate. The area under the curve (AUC) was calculated using a non-parametric method [45]. In addition, to assess the clinical usefulness of the PRS in the prediction of knee OA, the PRS was combined with a currently known clinical risk score and differences in model performance were evaluated. The first model (MODEL I) was constructed using only the clinical risk score of individuals, including sex, age, and body mass index (BMI) [46]. The second model (MODEL II) was generated by incorporating the PRS into MODEL I (sex, age, and BMI). To test the improvement in model performance by incorporating PRS, the AUC test (Delong method) [47] and net reclassification improvement (NRI) analysis [48] were performed. We compared the AUC (c statistic) generated from the full model with the values generated from the model, with each factor removed, to evaluate the relative contribution of each factor to the model. In a sensitivity analysis, we explored whether additional adjustment for excluding statin users would change our findings on lipid traits PRS, high- and low-density lipoprotein cholesterol (HDL-C and LDL-C), total cholesterol, and triglyceride. To assess the ability of PRS in case/control discrimination and its correlation with disease severity, the Jonckheere–Terpstra trend test was used to compare the distributions of radiographic knee OA severity (K-L grade) between different quintiles of PRS. All polygenic scores were standardized to facilitate interpretability. For knee OA risk factor screening to identify traits to include in MTAG, we applied a nominal threshold of p = 0.05. We applied the Bonferroni correction for multiple comparisons (number of PRS tested N = 19), considering as significant a p-value < 0.0026 (0.05/19) in risk factors PRS analysis based on single and multi traits. In a sensitivity analysis excluding statin users, a p-value < 0.0125 (0.05/4) was considered significant.

Results

Of the 2,852 individuals (1,776 women and 1,076 men) included in our final genetic analysis. Among these 2,852 individuals, 1,059 (37.1%) had knee OA and the mean age was 68.2 years ([SD] = 5.3; range, 60–80 years; Table 1). Although the Nagahama cohort study was conducted on volunteers from a rural area in Japan, the prevalence of knee OA was almost perfectly consistent with that of previous studies in other countries [49, 50].

Table 1 Demographic data of participants
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Performance of PRSs derived from OA GWAS

We first evaluated the ancestry-specific PRS derived from a Japanese knee OA GWAS (Supplementary Table S1). The PRS was not associated with risk for knee OA (p = 0.228). We then evaluated PRS performance based on multi-cohort OA GWAS because increasing evidence has demonstrated larger sample size multi-ancestry PRS improved performance [32, 39,40,41]. We selected the largest multi-population OA GWAS [17] and found that PRSs derived from multi-population GWAS of knee OA, knee and/or hip OA, OA, total knee replacement (TKR), total knee and/or hip joint replacement (TJP), and knee pain showed nominal associations (p < 0.05) with knee OA (Table 2). The strongest association was observed for multi-population knee OA PRS (p = 6.7 × 10–5, OR per SD increase 1.19 [95% CI 1.09–1.29]). The adjusted OR and R2 on the liability scale ranged between 1.10–1.19 and 0.14–0.52%, respectively.

Table 2 PRS analysis summary of traits that showed association with knee OA
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Performance of PRSs derived from GWAS of the risk factors for OA

We evaluated the association between the PRS of quantitative traits using the summary statistics of BBJ and knee OA to identify potential risk factor traits that may share genetic components with knee OA to be included in the MTAG. High PRS scores for BMI and low scores for HDL-C and calcium (Ca) were nominally associated (p < 0.05) with an increased risk of knee OA (Table 2). BMI PRS displayed the strongest association with knee OA (p = 4.6 × 10−6, OR per SD increase 1.22 [95% CI 1.12–1.32]) and outperformed multi-population knee OA PRS, highlighting the known risk of BMI for knee OA as well as the currently explained heritability of BMI GWAS in contrast to that of OA GWAS. HDL-C PRS showed the second strongest correlation with knee OA (p = 6.9 × 10−4, OR per SD increase 0.87 [95% CI 0.80–0.94]), suggesting a shared genetic background of knee OA with lipid metabolism. In a sensitivity analysis for lipid traits PRS, 916 statin users were excluded, and 1,936 individuals (683 knee OA patients; 35.3%) were analyzed. After Bonferroni correction, only HDL-C PRS showed significant associations (p = 0.004, OR per SD increase of 0.86 [95% CI 0.78–0.95]; Supplementary Table S2). Ca PRS was inversely associated with knee OA (p = 6.9 × 10−4, OR per SD increase 0.91 [95% CI 0.83–0.99]). We obtained UKB-GWAS data for the above-mentioned quantitative traits and calculated the PRSs. We observed that, despite the larger sample size, multi-ancestry PRSs of the risk factors performed worse than those of the BBJ traits and only the UKB BMI PRS showed nominal associations (p = 0.002, OR per SD increase of 1.14 ([95% CI: 1.05–1.24]; Supplementary Table S1). A comprehensive analysis of risk factors suggested that BMI, HDL-C, and Ca have a genetic risk effect on knee OA. The correlation with BMI was particularly strong and remained strong even in the UKB-BMI PRS, which excluded the Japanese population. PRSs estimated using LDpred 2-auto are shown in Supplementary Table 3. The results revealed slightly lower performance in the PRSs computed using LDpred 2-auto than those computed using PRScs; nevertheless, both PRSs using PRScs and LDpred 2-auto showed similar trends. The multi-population knee OA PRS and BBJ BMI PRS were significant after Bonferroni correction in both PRS analyses using PRScs and LDpred2-auto.

Improved prediction of the knee OA risk using MTAG-based PRS

MTAG can increase the statistical power by appropriately incorporating the information in GWAS estimates for other genetically correlated traits [25]. We first selected traits that showed nominal association with knee OA risk in the single-trait PRS analysis to develop MTAG-based PRSs for a better prediction model. We selected 10 traits from the GWAS summary statistics, including six multi-ancestry OA-related traits, three single-ancestry risk factor traits, and a multi-ancestry BMI. Next, we evaluated genetic correlations among the 10 traits (Supplementary Fig. S2) and incorporated traits that were genetically correlated with each other into all 10 traits (Fig. 1). All MTAG-based PRS showed significant associations with knee OA after Bonferroni correction (p < 0.0026). Most MTAG-based PRS yielded stronger risk effects with knee OA (Fig. 2) and outperformed R2 on the liability scale compared to the corresponding single-trait PRS, with an average of 2.0-fold (Table 3). The PRS with the best performance was the MTAG-based multi-population knee OA PRS, calculated by incorporating BBJ GWAS of BMI, multi-population GWAS of the knee and/or hip OA, all OA, TKR, TJP, and knee pain, and UKB-GWAS of BMI into multi-population knee OA GWAS using MTAG (p = 5.4 × 10−7, OR per SD increase 1.24 [95% CI 1.14–1.35]).

Fig. 2
figure 2

Adjusted odds ratios of single-trait and MTAG-based PRSs of traits with correlations with knee OA. Adjusted odds ratios per standard deviation and 95% confidence intervals of the risk of knee OA were calculated using a logistic regression model, including age, sex, 10 principal components, and a dummy variable for the array type used in genotyping as covariates. PRS, polygenic risk score; MTAG, multi-trait analysis of genome-wide association; OA, osteoarthritis; GWAS, genome-wide association study; TKR, total knee replacement; TJR, total knee and/or hip joint replacement; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; UKB, UK Biobank; CI, confidence interval

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Table 3 MTAG-based PRS analysis summary
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To evaluate whether adding the PRS to a clinical risk model improves the overall model performance, we compared MODEL I (constructed with only the clinical risk score of individuals, including sex, age, and BMI) to MODEL II (incorporating PRS into MODEL I; Tables 2 and 3). The model incorporating MTAG-based multi-population knee OA PRS into MODEL I showed the best improvement in AUC (AUC 74.4–74.7%, AUC test p = 0.029) and a significant NRI value of 0.105 (95% CI 0.030–0.181, p = 0.006). The contributions of each factor were 9.0, 3.6, 3.3, and 0.3% for sex, age, BMI, and MTAG-multi-population knee OA PRS, respectively (Fig. 3). The MTAG-based multi-population knee OA PRS was also correlated with radiographic severity; we observed a strong association between MTAG-based multi-population knee OA PRS and severe OA (K-L 3 and 4; p = 1.3 × 10−5, OR per SD increase 1.35 [95% CI 1.18–1.55]) and increasing radiographic knee OA severity (high K–L grade) in increasing quintiles of MTAG-based multi-population knee OA PRS (The Jonckheere–Terpstra trend test p-value = 3.1 × 10−7; Fig. 4).

Fig. 3
figure 3

Receiver operating characteristic curves for identifying patients with knee OA using PRSs and clinical data. Line plot showing receiver operating characteristic curves of PRSs and models, including clinical data. MODEL I integrated risk models using clinical data, including sex, age, and BMI; MODEL II integrated risk models using clinical data, including sex, age, and BMI, and MTAG-based multi-population knee OA PRS. OA, osteoarthritis; PRS, polygenic risk score; BMI, body mass index; MTAG, multi-trait analysis of genome-wide association

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Fig. 4
figure 4

Distribution of K-L grade per quintile of MTAG-based multi-population knee OA PRS. K-L, Kellgren–Lawrence grade; MTAG, multi-trait analysis of genome-wide association; OA, osteoarthritis; PRS, polygenic risk score

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Discussion

Knee OA is a multifactorial disorder with a polygenic genetic architecture. Therefore, understanding the genetic etiology of knee OA is challenging. The largest OA GWAS to date performed by the Genetics of OA (GO) consortium, including two East Asian and 11 European cohorts, explained 11% of the genetic risk for knee OA [17]. In the European population, there have been a few previous attempts to predict OA using PRS, and a modest discriminatory ability of knee OA was reported, with an estimated risk effect of the OR of 1.2 for radiographic knee OA [18]. However, the PRS assessment of knee OA has been mostly limited to the European population and the prediction accuracy of PRS in non-Europeans remains limited [24]. In this study, we observed an OR of 1.19 per SD increase in multi-population knee OA PRS in the Japanese population. The slightly higher OR of 1.30 for severe radiographic knee OA shown in the Rotterdam Study [18] was also replicated in the Japanese population (OR per SD increase 1.32 [95% CI 1.15–1.52]). Moreover, the present study demonstrates that the predictive performance of the knee OA PRS can be improved by using multi-population OA GWAS with increased statistical power due to a larger sample size and multi-trait PRS using MTAG, including risk factor traits of knee OA, even when the Japanese knee OA GWAS population is small. The improved performance by multi-population OA GWAS could either be due to a multi-population nature or an increased sample size. In most cases, the MTAG-based PRS outperformed their corresponding single-trait PRSs in predicting knee OA, and our prediction model combining the MTAG-based multi-population knee OA PRS and established clinical risk factors showed better predictive ability than that of clinical risk factors only. PRS calculations were performed using PRScs which assumes the continuous shrinkage priors and LDpred2-auto which assumes a point-normal mixture distribution for SNP effect sizes [32]. Both methods with different concepts in PRS calculations yielded the same results, suggesting the accuracy of the results of this study.

In the present study, the PRS generated based on the GWAS of the Japanese population knee OA (single Japanese ancestry) with a limited sample size was not associated with the prevalence of knee OA, supporting the current concern regarding the difficulty in implementing equitable genomic medicine across non-European populations [24]. In these cases, the use of multi-population and/or multi-trait analysis can improve the predictive ability of PRS. The present study is the first to investigate the increase in the predictive performance of PRS for knee OA in non-European populations. The PRS from a multi-population knee OA GWAS was observed to be moderately accurate in the Japanese population (AUC = 0.540). PRS accuracy and predictive power depend on the power of the base GWAS data, and the multi-population OA GWAS may have increased the predictive accuracy because it represents a meta-analysis study that included the Japanese knee OA GWAS [9, 17] and overwhelmingly increased the sample size, reflecting the multifactorial genetic etiology of knee OA and the additive effect of genes associated with knee OA. Moreover, our results showed that compared to single-trait PRS, multi-trait PRS using MTAG improved the predictive performance by approximately 2.0 times as in R2 on the liability scale. The PRS approach is expected to provide insights into genetic etiology, free from confounding bias [24]. Unlike actual measurements, genetic scores do not vary with time and can be evaluated universally; therefore, PRS effectively identifies individuals at a high risk of knee OA. Highly accurate risk prediction allows for focused prevention strategies such as weight reduction, biomechanical interventions such as knee braces, and exercise for high-risk groups [51]. Furthermore, the statistically significant improvements in the predictability of the model by integrating clinical information and PRS suggest its potential clinical applications.

In the present study, PRS analysis using nationwide biobank GWASs of the same population [24] suggested a genetic overlap with knee OA for several traits, including novel traits. The influence of high BMI on the development of knee OA has been reported for both the biomechanical [5, 52] and genetic pathways [14, 53, 54]. In our study, the genetic relationship between high BMI and OA of the knee and weight-bearing joints was consistent and robust. Moreover, a previous epidemiological study has reported that serum HDL-C levels were low in patients with knee OA [55] and that HDL-C levels in the synovial fluid were negatively correlated with cartilage damage and the severity of knee OA [56]. Although the genetic relationship between HDL-C and knee OA is unclear [53, 57], in the present study, HDL-C PRS was strongly negatively correlated with knee OA, second to BMI PRS. A previous Mendelian randomization study for OA has reported a causal relationship between low LDL-C and OA and the potential role of statin in OA pathogenesis [53]. We performed a sensitivity analysis excluding statin users, but only HDL-C PRS was significant after the Bonferroni correction. It is possible that the sample size was limited and the significance of other lipid traits PRSs was not shown. An epidemiological case–control study has reported a negative direct association between serum Ca concentration and knee OA through the physiological and pathological processes of chondrocytes [58]. Therefore, these previous and present study findings support the idea of a genetic overlap between decreased Ca levels and knee OA.

This study had several limitations. First, the sample size was small. Second, the R2 on the liability scale of each PRS was insufficiently high. This might be because the current PRSs may only partially capture the heritability of knee OA. Third, multiple array datasets were merged and analyzed; therefore, there is a risk of bias due to an imbalance in sample size and knee OA prevalence. However, when we evaluated the OR of knee OA of the MTAG-multi-population knee OA PRS for each array dataset and WGS data, we found a consistent contribution to knee OA (Supplementary Fig. S3).

In conclusion, this study showed that multi-trait PRS based on MTAG using multi-population GWAS with a large sample size was significantly associated with knee OA in the Japanese population, even when the sample size of GWAS of the same ancestry was small. To our knowledge, this is the first study to show a statistically significant association between PRS and knee OA in a non-European population.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

AUC:

Area under the curve

BBJ:

Biobank Japan

BMI:

Body mass index

Ca:

Calcium

CI:

Confidence interval

GO:

Genetics of osteoarthritis

GWAS:

Genome-wide association study

MAC:

Minor allele count

MAF:

Minor allele frequency

HDL-C:

High-density lipoprotein cholesterol

QC:

Quality control

HWE:

Hardy-Weinberg equilibrium

K–L:

Kellgren–Lawrence

LD:

Linkage disequilibrium

MTAG:

Multi-trait analysis of genome-wide association

NRI:

Net reclassification improvement

OA:

Osteoarthritis

OR:

Odds ratio

PRS:

Polygenic risk score

ROC:

Receiver operating characteristic

SD:

Standard deviation

SNP:

Single-nucleotide polymorphism

TKR:

Total knee replacement

TJR:

Total knee and/or hip joint replacement

WGS:

Whole genome sequencing

References

  1. Yoshimura N, Muraki S, Oka H, Mabuchi A, En-Yo Y, Yoshida M, et al. Prevalence of knee osteoarthritis, lumbar spondylosis, and osteoporosis in Japanese men and women: the research on osteoarthritis/osteoporosis against disability study. J Bone Miner Metab. 2009;27:620–8. https://0-doi-org.brum.beds.ac.uk/10.1007/s00774-009-0080-8.

    Article  PubMed  Google Scholar 

  2. Weinstein AM, Rome BN, Reichmann WM, Collins JE, Burbine SA, Thornhill TS, et al. Estimating the burden of total knee replacement in the United States. J Bone Joint Surg Am. 2013;95:385–92. https://0-doi-org.brum.beds.ac.uk/10.2106/JBJS.L.00206.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Jordan JM, Helmick CG, Renner JB, Luta G, Dragomir AD, Woodard J, et al. Prevalence of hip symptoms and radiographic and symptomatic hip osteoarthritis in African Americans and Caucasians: the Johnston County Osteoarthritis Project. J Rheumatol. 2009;36:809–15. https://0-doi-org.brum.beds.ac.uk/10.3899/jrheum.080677.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Deshpande BR, Katz JN, Solomon DH, Yelin EH, Hunter DJ, Messier SP, et al. Number of persons with symptomatic knee osteoarthritis in the US: impact of race and ethnicity, age, sex, and obesity. Arthritis Care Res. 2016;68:1743–50. https://0-doi-org.brum.beds.ac.uk/10.1002/acr.22897.

    Article  Google Scholar 

  5. Silverwood V, Blagojevic-Bucknall M, Jinks C, Jordan JL, Protheroe J, Jordan KP. Current evidence on risk factors for knee osteoarthritis in older adults: a systematic review and meta-analysis. Osteoarthritis Cartilage. 2015;23:507–15. https://0-doi-org.brum.beds.ac.uk/10.1016/j.joca.2014.11.019.

    Article  CAS  PubMed  Google Scholar 

  6. Neame RL, Muir K, Doherty S, Doherty M. Genetic risk of knee osteoarthritis: a sibling study. Ann Rheum Dis. 2004;63:1022–7. https://0-doi-org.brum.beds.ac.uk/10.1136/ard.2003.014498.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Spector TD, Macgregor AJ. Risk factors for osteoarthritis: genetics. Osteoarthritis Cartilage. 2004;12(Suppl A):S39-44. https://0-doi-org.brum.beds.ac.uk/10.1016/j.joca.2003.09.005.

    Article  PubMed  Google Scholar 

  8. Magnusson K, Scurrah K, Ystrom E, Ørstavik RE, Nilsen T, Steingrímsdóttir ÓA, et al. Genetic factors contribute more to hip than knee surgery due to osteoarthritis – a population-based twin registry study of joint arthroplasty. Osteoarthritis Cartilage. 2017;25:878–84. https://0-doi-org.brum.beds.ac.uk/10.1016/j.joca.2016.12.015.

    Article  CAS  PubMed  Google Scholar 

  9. Nakajima M, Takahashi A, Kou I, Rodriguez-Fontenla C, Gomez-Reino JJ, Furuichi T, et al. New sequence variants in HLA Class II/III region associated with susceptibility to knee osteoarthritis identified by genome-wide association study. PLoS One. 2010;5:e9723. https://0-doi-org.brum.beds.ac.uk/10.1371/journal.pone.0009723.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Evangelou E, Valdes AM, Kerkhof HJM, Styrkarsdottir U, Zhu YY, Meulenbelt I, et al. Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22. Ann Rheum Dis. 2011;70:349–55. https://0-doi-org.brum.beds.ac.uk/10.1136/ard.2010.132787.

    Article  CAS  PubMed  Google Scholar 

  11. Miyamoto Y, Shi D, Nakajima M, Ozaki K, Sudo A, Kotani A, et al. Common variants in DVWA on chromosome 3p24.3 are associated with susceptibility to knee osteoarthritis. Nat Genet. 2008;40:994–8. https://0-doi-org.brum.beds.ac.uk/10.1038/ng.176.

    Article  CAS  PubMed  Google Scholar 

  12. Day-Williams AG, Southam L, Panoutsopoulou K, Rayner NW, Esko T, Estrada K, et al. A variant in MCF2L is associated with osteoarthritis. Am J Hum Genet. 2011;89:446–50. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ajhg.2011.08.001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. arcOGEN Consortium, arcOGEN Collaborators, Zeggini E, Panoutsopoulou K, Southam L, Rayner NW, et al. Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genome-wide association study. Lancet. 2012;380:815–23. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(12)60681-3.

    Article  CAS  Google Scholar 

  14. Zengini E, Hatzikotoulas K, Tachmazidou I, Steinberg J, Hartwig FP, Southam L, et al. Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis. Nat Genet. 2018;50:549–58. https://0-doi-org.brum.beds.ac.uk/10.1038/s41588-018-0079-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tachmazidou I, Hatzikotoulas K, Southam L, Esparza-Gordillo J, Haberland V, Zheng J, et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet. 2019;51:230–6. https://0-doi-org.brum.beds.ac.uk/10.1038/s41588-018-0327-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Gill D, Karhunen V, Malik R, Dichgans M, Sofat N. Cardiometabolic traits mediating the effect of education on osteoarthritis risk: a Mendelian randomization study. Osteoarthritis Cartilage. 2021;29:365–71. https://0-doi-org.brum.beds.ac.uk/10.1016/j.joca.2020.12.015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Boer CG, Hatzikotoulas K, Southam L, Stefánsdóttir L, Zhang Y, de Almeida RC, et al. Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations. Cell. 2021;184:4784-4818.e17. https://0-doi-org.brum.beds.ac.uk/10.1016/j.cell.2021.11.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Sedaghati-Khayat B, Boer CG, Runhaar J, Bierma-Zeinstra SMA, Broer L, Ikram MA, et al. Risk assessment for hip and knee osteoarthritis using polygenic risk scores. Arthritis Rheumatol. 2022;74:1488–96. https://0-doi-org.brum.beds.ac.uk/10.1002/art.42246.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016;17:392–406. https://0-doi-org.brum.beds.ac.uk/10.1038/nrg.2016.27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Warner SC, Valdes AM. Genetic association studies in osteoarthritis: is it fairytale? Curr Opin Rheumatol. 2017;29:103–9. https://0-doi-org.brum.beds.ac.uk/10.1097/BOR.0000000000000352.

    Article  CAS  PubMed  Google Scholar 

  21. Gonzalez A. Osteoarthritis year 2013 in review: genetics and genomics. Osteoarthritis Cartilage. 2013;21:1443–51. https://0-doi-org.brum.beds.ac.uk/10.1016/j.joca.2013.07.001.

    Article  CAS  PubMed  Google Scholar 

  22. van Meurs JBJ. Osteoarthritis year in review 2016: genetics, genomics and epigenetics. Osteoarthritis Cartilage. 2017;25:181–9. https://0-doi-org.brum.beds.ac.uk/10.1016/j.joca.2016.11.011.

    Article  PubMed  Google Scholar 

  23. Lacaze P, Wang Y, Polekhina G, Bakshi A, Riaz M, Owen A, et al. Genomic risk score for advanced osteoarthritis in older adults. Arthritis Rheumatol. 2022;74:1480–7. https://0-doi-org.brum.beds.ac.uk/10.1002/art.42156.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51:584–91. https://0-doi-org.brum.beds.ac.uk/10.1038/s41588-019-0379-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, et al. Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet. 2018;50:229–37. https://0-doi-org.brum.beds.ac.uk/10.1038/s41588-017-0009-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Setoh K, Matsuda F. Cohort profile: the Nagahama prospective genome cohort for comprehensive human bioscience (The Nagahama Study). In: Yano M, Matsuda F, Sakuntabhai A, Hirota S, editors. Socio-Life Sci and the COVID-19 Outbreak. Singapore: Economics, Law, and Institutions in Asia Pacific. Springer; 2022. p. 127–43.

    Chapter  Google Scholar 

  27. Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis. 1957;16:494–502. https://0-doi-org.brum.beds.ac.uk/10.1136/ard.16.4.494.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. van der Auwera GA, O’Connor BD. Genomics in the cloud: using Docker, GATK, and WDL in. Terra. O’Reilly Media; 2020.

    Google Scholar 

  29. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26:2069–70. https://0-doi-org.brum.beds.ac.uk/10.1093/bioinformatics/btq330.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Terao C, Ota M, Iwasaki T, Shiokawa M, Kawaguchi S, Kuriyama K, et al. IgG4-related disease in the Japanese population: a genome-wide association study. Lancet Rheumatol. 2019;1:e14-22. https://0-doi-org.brum.beds.ac.uk/10.1016/S2665-9913(19)30006-2.

    Article  Google Scholar 

  31. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75. https://0-doi-org.brum.beds.ac.uk/10.1086/519795.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ge T, Chen CY, Ni Y, Feng Y-CA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10:1776. https://0-doi-org.brum.beds.ac.uk/10.1038/s41467-019-09718-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pirruccello JP, Di Achille P, Nauffal V, Nekoui M, Friedman SF, Klarqvist MDR, et al. Genetic analysis of right heart structure and function in 40,000 people. Nat Genet. 2022;54:792–803. https://0-doi-org.brum.beds.ac.uk/10.1038/s41588-022-01090-3.

    Article  CAS  PubMed  Google Scholar 

  34. Meng W, Adams MJ, Palmer CNA, The 23andMe Research Team, Shi J, Auton A, et al. Genome-wide association study of knee pain identifies associations with GDF5 and COL27A1 in UK Biobank. Commun Biol. 2019;2:321. https://0-doi-org.brum.beds.ac.uk/10.1038/s42003-019-0568-2.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Akiyama M, Okada Y, Kanai M, Takahashi A, Momozawa Y, Ikeda M, et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat Genet. 2017;49:1458–67. https://0-doi-org.brum.beds.ac.uk/10.1038/ng.3951.

    Article  CAS  PubMed  Google Scholar 

  36. Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat Genet. 2018;50:390–400. https://0-doi-org.brum.beds.ac.uk/10.1038/s41588-018-0047-6.

    Article  CAS  PubMed  Google Scholar 

  37. Matoba N, Akiyama M, Ishigaki K, Kanai M, Takahashi A, Momozawa Y, et al. GWAS of smoking behaviour in 165,436 Japanese people reveals seven new loci and shared genetic architecture. Nat Hum Behav. 2019;3:471–7. https://0-doi-org.brum.beds.ac.uk/10.1038/s41562-019-0557-y.

    Article  PubMed  Google Scholar 

  38. Akiyama M, Ishigaki K, Sakaue S, Momozawa Y, Horikoshi M, Hirata M, et al. Characterizing rare and low-frequency height-associated variants in the Japanese population. Nat Commun. 2019;10:4393. https://0-doi-org.brum.beds.ac.uk/10.1038/s41467-019-12276-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Cavazos TB, Witte JS. Inclusion of variants discovered from diverse populations improves polygenic risk score transferability. HGG Adv. 2021;2:100017. https://0-doi-org.brum.beds.ac.uk/10.1016/j.xhgg.2020.100017.

    Article  CAS  PubMed  Google Scholar 

  40. Sordillo JE, Lutz SM, Jorgenson E, Iribarren C, McGeachie M, Dahlin A, et al. A polygenic risk score for asthma in a large racially diverse population. Clin Exp Allergy. 2021;51:1410–20. https://0-doi-org.brum.beds.ac.uk/10.1111/cea.14007.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Wu K-HH, Douville NJ, Konerman MC, Mathis MR, Scott HL, Wolford BN, et al. Polygenic risk score from a multi-ancestry GWAS uncovers susceptibility of heart failure. medRxiv. Published online 2021. https://0-doi-org.brum.beds.ac.uk/10.1101/2021.12.06.21267389.

  42. Privé F, Arbel J, Vilhjálmsson BJ. LDpred2: better, faster, stronger. Bioinformatics. 2020;36:5424–31. https://0-doi-org.brum.beds.ac.uk/10.1093/bioinformatics/btaa1029.

    Article  CAS  PubMed Central  Google Scholar 

  43. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33:272–9. https://doi.org/10.1093/bioinformatics/btw613.

    Article  CAS  PubMed  Google Scholar 

  44. Lee SH, Goddard ME, Wray NR, Visscher PM. A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012;36:214–24. https://0-doi-org.brum.beds.ac.uk/10.1002/gepi.21614.

    Article  PubMed  Google Scholar 

  45. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993;39:561–77. https://0-doi-org.brum.beds.ac.uk/10.1093/clinchem/39.4.561.

    Article  CAS  PubMed  Google Scholar 

  46. Kerkhof HJM, Bierma-Zeinstra SMA, Arden NK, Metrustry S, Castano-Betancourt M, Hart DJ, et al. Prediction model for knee osteoarthritis incidence, including clinical, genetic and biochemical risk factors. Ann Rheum Dis. 2014;73:2116–21. https://0-doi-org.brum.beds.ac.uk/10.1136/annrheumdis-2013-203620.

    Article  CAS  PubMed  Google Scholar 

  47. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45. https://0-doi-org.brum.beds.ac.uk/10.2307/2531595.

    Article  CAS  PubMed  Google Scholar 

  48. Hayden JA, van der Windt DA, Cartwright JL, Côté P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013;158:280–6. https://0-doi-org.brum.beds.ac.uk/10.7326/0003-4819-158-4-201302190-00009.

    Article  PubMed  Google Scholar 

  49. Jordan JM, Linder GF, Renner JB, Fryer JG. The impact of arthritis in rural populations. Arthritis Care Res. 1995;8:242–50. https://0-doi-org.brum.beds.ac.uk/10.1002/art.1790080407.

    Article  CAS  PubMed  Google Scholar 

  50. Cui A, Li H, Wang D, Zhong J, Chen Y, Lu H. Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies. EClinicalmedicine. 2020;29–30:100587. https://0-doi-org.brum.beds.ac.uk/10.1016/j.eclinm.2020.100587.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Roos EM, Arden NK. Strategies for the prevention of knee osteoarthritis. Nat Rev Rheumatol. 2016;12:92–101. https://0-doi-org.brum.beds.ac.uk/10.1038/nrrheum.2015.135.

    Article  CAS  PubMed  Google Scholar 

  52. Messier SP, Mihalko SL, Legault C, Miller GD, Nicklas BJ, DeVita P, et al. Effects of intensive diet and exercise on knee joint loads, inflammation, and clinical outcomes among overweight and obese adults with knee osteoarthritis: the IDEA randomized clinical trial. JAMA. 2013;310:1263–73. https://0-doi-org.brum.beds.ac.uk/10.1001/jama.2013.277669.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hindy G, Åkesson KE, Melander O, Aragam KG, Haas ME, Nilsson PM, et al. Cardiometabolic polygenic risk scores and osteoarthritis outcomes: A Mendelian randomization study using data from the Malmö diet and cancer study and the UK Biobank. Arthritis Rheumatol. 2019;71:925–34. https://0-doi-org.brum.beds.ac.uk/10.1002/art.40812.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Panoutsopoulou K, Metrustry S, Doherty SA, Laslett LL, Maciewicz RA, Hart DJ, et al. The effect of FTO variation on increased osteoarthritis risk is mediated through body mass index: a Mendelian randomisation study. Ann Rheum Dis. 2014;73:2082–6. https://0-doi-org.brum.beds.ac.uk/10.1136/annrheumdis-2013-203772.

    Article  PubMed  Google Scholar 

  55. Karvonen-Gutierrez CA, Sowers MR, Heeringa SG. Sex dimorphism in the association of cardiometabolic characteristics and osteophytes-defined radiographic knee osteoarthritis among obese and non-obese adults: NHANES III. Osteoarthritis Cartilage. 2012;20:614–21. https://0-doi-org.brum.beds.ac.uk/10.1016/j.joca.2012.02.644.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhang K, Ji Y, Dai H, Khan AA, Zhou Y, Chen R, et al. High-density lipoprotein cholesterol and apolipoprotein A1 in synovial fluid: potential predictors of disease severity of primary knee osteoarthritis. Cartilage. 2021;13:1465S-S1473. https://0-doi-org.brum.beds.ac.uk/10.1177/19476035211007919.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Funck-Brentano T, Nethander M, Movérare-Skrtic S, Richette P, Ohlsson C. Causal factors for knee, hip, and hand osteoarthritis: a Mendelian randomization study in the UK Biobank. Arthritis Rheumatol. 2019;71:1634–41. https://0-doi-org.brum.beds.ac.uk/10.1002/art.40928.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Li H, Zeng C, Wei J, Yang T, Gao SG, Li YS, et al. Serum calcium concentration is inversely associated with radiographic knee osteoarthritis: a cross-sectional study. Medicine. 2016;95:e2838. https://0-doi-org.brum.beds.ac.uk/10.1097/MD.0000000000002838.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We are grateful to Dr. Yoshihiko Kotoura for his tremendous help with clinical measurements and to the Nagahama City Office and the Zeroji Club, a non-profit organization, for their assistance in conducting this study. We would like to acknowledge the Pan-UK and UK Biobanks for making publicly available data GWAS.

Funding

The Nagahama study was supported by a university grant, The Center of Innovation Program, The Global University Project, and a Grant-in-Aid for Scientific Research (25293141, 26670313, 26293198, 17H04182, 17H04126, 17H04123, 18K18450) from the Ministry of Education, Culture, Sports, Science and Technology of Japan; the Practical Research Project for Rare/Intractable Diseases (ek0109070, ek0109070, ek0109196, ek0109348); the Comprehensive Research on Aging and Health Science Research Grants for Dementia R&D (dk0207006, dk0207027), the Program for an Integrated Database of Clinical and Genomic Information (kk0205008);the Practical Research Project for Lifestyle-related Diseases including Cardiovascular Diseases and Diabetes Mellitus (ek0210066, ek0210096, ek0210116); the Research Program for Health Behavior Modification by Utilizing IoT (le0110005) from Japan Agency for Medical Research and Development (AMED); the Takeda Medical Research Foundation; the Mitsubishi Foundation; the Daiwa Securities Health Foundation; and the Sumitomo Foundation. YM was supported by Grant of The Japanese Orthopaedic Society of Knee, Arthroscopy and Sports Medicine, 2019. HI, YK, CT, SI, MT, FM, and SM received the Japanese Orthopaedic Association subsidized Science Project Research Grant 2019–2021 from the Japanese Orthopaedic Association. SI was supported by a Grant-in-Aid for Scientific Research (22H03207) from the Ministry of Education, Culture, Sports, Science, and Technology of Japan.

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Authors and Affiliations

  1. Department of Orthopedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Yugo Morita, Hiromu Ito, Kohei Nishitani, Shinichiro Nakamura, Shinichi Kuriyama & Shuichi Matsuda

  2. Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan

    Yugo Morita, Yoichiro Kamatani, Takahisa Kawaguchi, Shuji Kawaguchi, Meiko Takahashi, Yasuharu Tabara & Fumihiko Matsuda

  3. Department of Orthopedic Surgery, Kurashiki Central Hospital, Kurashiki, Japan

    Hiromu Ito

  4. Laboratory for Bone and Joint Diseases, Center for Genomic Medicine, RIKEN, Tokyo, Japan

    Shiro Ikegawa & Shuji Ito

  5. Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Shiro Ikegawa, Chikashi Terao & Shuji Ito

  6. Department of Orthopedic Surgery, Shimane University Faculty of Medicine, Izumo, Japan

    Shuji Ito

  7. Graduate School of Public Health, Shizuoka Graduate University of Public Health, Aoi-Ku, Shizuoka, Japan

    Yasuharu Tabara

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  1. Yugo Morita
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Contributions

YK, HI, FM, and SM designed this study. YM, YK, HI, and TK wrote the manuscript. YM, YK, HI, TK, SK, MT, SI, CT, and FM collected the data and information of the participants. YM, YK, and TK performed the statistical analyses. HI and SM verified the data. HI, SI, FM, and SM obtained funding. FM and SM supervised this study. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Hiromu Ito.

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Ethics approval and consent to participate

This study was conducted following the principles of the Declaration of Helsinki and was approved by the ethics committee of Kyoto University Graduate School of Medicine and Nagahama Municipal Review Board (No. C278). Written informed consent was obtained from all participants.

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Not applicable.

Competing interests

YM received a research grant from the Japanese Orthopedic Society of Knee, Arthroscopy, and Sports Medicine. HI, YK, CT, SI, MT, FM, and SM received a research grant from the Japanese Orthopaedic Association. SI, TK, SK, KN, SN, SK, and YT declare no conflicts of interest. The sponsors were not involved in the study design, data collection or recruitment, analysis, interpretation of data, writing of the manuscript, or decision to submit the article for publication. The authors, their immediate families, and any research foundations to which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article.

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Supplementary Information

Additional file 1:

Supplementary Fig. 1. Flowchart of the genotype quality control and sample extraction for PRS calculation. Supplementary Fig. 2. Significant genetic correlation coefficient calculated using LDSC between each risk factor trait. Supplementary Fig. 3. Forest plot of adjusted odds ratios of MTAG-based multi-population knee OA PRS for each genotyping array and whole genome sequence data. Supplementary Table 1. Correlation between PRSs and knee OA in single-PRS analysis. Supplementary Table 2. Summary of the PRS analysis of lipid traits in the sensitivity analysis excluding statin users. Supplementary Table 3. Summary of the PRS analysis of traits that showed association with knee OA using LDpred2-auto.

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Morita, Y., Kamatani, Y., Ito, H. et al. Improved genetic prediction of the risk of knee osteoarthritis using the risk factor-based polygenic score. Arthritis Res Ther 25, 103 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s13075-023-03082-y

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  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13075-023-03082-y

Keywords

Arthritis Research & Therapy

ISSN: 1478-6362

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