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Abstract Background In knee osteoarthritis (OA), pain is the most frequent and dominant symptom. However, which factors other than radiological changes contribute to the symptoms is unresolved. The aims of this study were to identify factors affecting knee pain from various variables with radiological changes taken into count and exploratively examine what subgroups or phenotype could be identified by cluster analysis using the identified knee pain factors. Methods Patients 60 years or older who underwent radiographic evaluation were included in this cross-sectional study, and those subjects who completed a questionnaire about knee symptoms without missing data were eligible for analysis. Multiple regression analysis was used to examine the associations between selected variables and The Japanese Knee Osteoarthritis Measure (JKOM) pain score. We grouped the subjects by cluster analysis using identified variables. Results Two thousand five hundred forty-two subjects were included in the full set of analyses. Age, body mass index (BMI), radiological grade, bone mineral density (BMD), and high-sensitivity C-reactive protein (hs-CRP) showed a statistically significant correlation with radiological showing the strongest value. For dichotomous variable, presence of depression showed a statistically significant result. We used BMI, radiological grade, BMD, hs-CRP, and presence of depression as a variable for cluster analysis and identified six subgroups: (1) minimal joint disease subgroup, (2) male and high BMD subgroup, (3) high CRP subgroup, (4) severe radiological OA subgroup, (5) depressive subgroup, and (6) moderate radiological OA with high BMI subgroup, showing the worst knee outcome. Conclusion This study identified the factors affecting knee pain other than radiological changes and identified six subgroups of knee outcome in the general population. The results showed that obesity with radiological changes or depression was associated with worse knee outcome.

Abstract Background Next-generation sequencing has allowed for the identification of different genetic variations, which are known to contribute to diseases. Of these, insertions and deletions are the second most abundant type of variations in the genome, but their biological importance or disease association is not well-studied, especially for deletions of intermediate sizes. Methods We identified intermediate-sized deletions from whole-genome sequencing (WGS) data of Japanese samples (n = 174) with a novel deletion calling method which considered multiple samples. These deletions were used to construct a reference panel for use in imputation. Imputation was then conducted using the reference panel and data from 82 publically available Japanese samples with gene expression data. The accuracy of the deletion calling and imputation was examined with Nanopore long-read sequencing technology. We also conducted an expression quantitative trait loci (eQTL) association analysis using the deletions to infer their functional impacts on genes, before characterizing the deletions causal for gene expression level changes. Results We obtained a set of polymorphic 4378 high-confidence deletions and constructed a reference panel. The deletions were successfully imputed into the Japanese samples with high accuracy (97.3%). The eQTL analysis identified 181 deletions (4.1%) suggested as causal for gene expression level changes. The causal deletion candidates were significantly enriched in promoters, super-enhancers, and transcription elongation chromatin states. Generation of deletions in a cell line with the CRISPR-Cas9 system confirmed that they were indeed causative variants for gene expression change. Furthermore, one of the deletions was observed to affect the gene expression levels of a gene it was not located in. Conclusions This paper reports an accurate deletion calling method for genotype imputation at the whole genome level and shows the importance of intermediate-sized deletions in the human population.