Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform a genome-wide association study for osteoarthritis (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, fine-mapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1 , FGF18 , CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

Aim Management of low anterior resection syndrome (LARS) requires a high degree of patient engagement. This process may be facilitated by online health-related information and education. The aim of this study was to systematically review current online health information on LARS.Method An online search of Google, Yahoo and Bing was performed using the search terms 'low anterior/anterior resection syndrome' and 'bowel function/movements after rectal cancer surgery'. Websites were assessed for readability (eight standardized tests), suitability (using the Suitability Assessment of Materials instrument), quality (the DISCERN instrument), accuracy and content (using a LARS-specific content checklist). Websites were categorized as academic, governmental, nonprofit or private. ResultsOf 117 unique websites, 25 met the inclusion criteria. The median readability level was 10.4 (9.2-11.7) and 11 (44.0%) websites were highly suitable. Using the DISCERN instrument, seven (28.0%) websites had clear aims, two (8.0%) divulged the sources used and four (16.0%) had high overall quality. Only eight (32.0%) websites defined LARS and ten (40.0%) listed all five major symptoms associated with the LARS score. There was variation in the number of websites that discussed dietary modifications (80.0%), self-help strategies (72.0%), medication (68.0%), pelvic floor rehabilitation (60.0%) and neuromodulation (8.0%). The median accuracy of websites was 93.8% (88.2-96.7%). Governmental websites scored highest for overall suitability (P = 0.0079) and quality (P < 0.001).Conclusions Current online information on LARS is suboptimal. Websites are highly variable, important content is often lacking and material is too complex for patients.What does this paper add to the literature? Patients with low anterior resection syndrome (LARS) following surgery for rectal cancer may turn to the Internet for help relating to their bowel function. This study reviews the available online health information on LARS, identifying the highest rated websites and highlighting the gaps in what is currently available to patients.

Background Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a

Background Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. Methods We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. Results A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13–1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727–0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791–0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. Conclusions We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we perform the largest genome-wide association study for osteoarthritis to date (77,052 cases and 378,169 controls), analysing 4 phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discover 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine map to a single variant. We identify putative effector genes by integrating eQTL colocalization, finemapping, human rare disease, animal model, and osteoarthritis tissue expression data. We find enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organisation biological pathways. Ten of the likely effector genes, including TGFB1, FGF18, CTSK and IL11 have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.Osteoarthritis affects 40% of individuals over the age of 70 1 , is a major cause of pain, comorbidity and mortality 2 . Ten million people in the UK alone suffer from osteoarthritis, with a total indirect cost to the economy of £14.8 billion per annum 2 . Disease management targets the main symptom (pain) and culminates in joint replacement surgery (1.76 million per year in the EU) with variable outcomes 3 . There is a clear and urgent need to translate genomic evidence into druggable DATA AVAILABILITY

Background: Genomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)-a risk factor for osteoporotic fracture. Methods:We used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic prescreening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.Results: gSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r 2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], P = 10 -28 ) than that for measured SOS (OR = 1.60 [1.50-1.69], P = 10 -52 ) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], P = 10 -6 ). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10 -18 ) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10 -14 ). Introducing a genomic prescreening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%.Interpretation: The use of genotypes in a machine learning algorithm resulted in a clinicallyrelevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.

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Background - The clinical implications of a polygenic risk score (PRS) for low-density lipoprotein cholesterol (LDL-C) are not well understood, both within the general population and individuals with familial hypercholesterolemia (FH). Methods - We developed the LDL-C PRS using LASSO regression in 377,286 white British participants from UK Biobank and tested its association with LDL-C according to FH variant carrier status in another 41,748 whole-exome sequenced individuals. Next, we tested for an enrichment of FH variant carriers amongst individuals with severe hypercholesterolemia and low LDL-C PRS. Last, we contrasted the effect of the LDL-C PRS, measured LDL-C and FH variant carrier status on risk of ischemic heart disease (IHD) amongst 3,010 cases and 38,738 controls. Results - Amongst the 41,748 whole-exome sequenced white British individuals, one SD increase in the LDL-C PRS was associated with elevated LDL-C amongst both FH variant carriers (0.34, 95% CI 0.22 to 0.47 mmol/L) and non-carriers (0.42, 95% CI 0.42 to 0.43 mmol/L). Amongst individuals with severe hypercholesterolemia, FH variant carriers were enriched in those with a low LDL-C PRS (OR 2.20, 95% CI 1.66 to 2.71 per SD). Each SD increase in the LDL-C PRS was associated with risk of IHD to the comparable magnitude as measured LDL-C (OR 1.24, 95% CI 1.20 to 1.29 and OR 1.15, 95% CI 1.09 to 1.23, respectively). The LDL-C PRS was not strongly associated with other traditional IHD risk factors. Conclusions - An LDL-C PRS could be used to identify individuals with a higher probability of harboring FH variants. The association between IHD and the LDL-C PRS was comparable to measured LDL-C, likely because the PRS reflects lifetime exposure to LDL-C levels.