Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations.

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation.

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.

An epigenome-wide view of osteoarthritis in primary tissues.

Osteoarthritis is a complex degenerative joint disease. Here, we investigate matched genotype and methylation profiles of primary chondrocytes from macroscopically intact (low-grade) and degraded (high-grade) osteoarthritis cartilage and from synoviocytes collected from 98 osteoarthritis-affected individuals undergoing knee replacement surgery. We perform an epigenome-wide association study of knee cartilage degeneration and report robustly replicating methylation markers, which reveal an etiologic mechanism linked to the migration of epithelial cells. Using machine learning, we derive methylation models of cartilage degeneration, which we validate with 82% accuracy in independent data. We report a genome-wide methylation quantitative trait locus (mQTL) map of articular cartilage and synovium and identify 18 disease-grade-specific mQTLs in osteoarthritis cartilage. We resolve osteoarthritis GWAS loci through causal inference and colocalization analyses and decipher the epigenetic mechanisms that mediate the effect of genotype on disease risk. Together, our findings provide enhanced insights into epigenetic mechanisms underlying osteoarthritis in primary tissues.

A molecular map of long non-coding RNA expression, isoform switching and alternative splicing in osteoarthritis.

Osteoarthritis is a prevalent joint disease and a major cause of disability worldwide with no curative therapy. Development of disease-modifying therapies requires a better understanding of the molecular mechanisms underpinning disease. A hallmark of osteoarthritis is cartilage degradation. To define molecular events characterizing osteoarthritis at the whole transcriptome level, we performed deep RNA sequencing in paired samples of low- and high-osteoarthritis grade knee cartilage derived from 124 patients undergoing total joint replacement. We detected differential expression between low- and high-osteoarthritis grade articular cartilage for 365 genes and identified a 38-gene signature in osteoarthritis cartilage by replicating our findings in an independent dataset. We also found differential expression for 25 novel long non-coding RNA genes (lncRNAs) and identified potential lncRNA interactions with RNA-binding proteins in osteoarthritis. We assessed alterations in the relative usage of individual gene transcripts and identified differential transcript usage for 82 genes, including ABI3BP, coding for an extracellular matrix protein, AKT1S1, a negative regulator of the mTOR pathway and TPRM4, coding for a transient receptor potential channel. We further assessed genome-wide differential splicing, for the first time in osteoarthritis, and detected differential splicing for 209 genes, which were enriched for extracellular matrix, proteoglycans and integrin surface interactions terms. In the largest study of its kind in osteoarthritis, we find that isoform and splicing changes, in addition to extensive differences in both coding and non-coding sequence expression, are associated with disease and demonstrate a novel layer of genomic complexity to osteoarthritis pathogenesis.

Primary osteoarthritis chondrocyte map of chromatin conformation reveals novel candidate effector genes.

OBJECTIVES: Osteoarthritis is a complex disease with a huge public health burden. Genome-wide association studies (GWAS) have identified hundreds of osteoarthritis-associated sequence variants, but the effector genes underpinning these signals remain largely elusive. Understanding chromosome organisation in three-dimensional (3D) space is essential for identifying long-range contacts between distant genomic features (e.g., between genes and regulatory elements), in a tissue-specific manner. Here, we generate the first whole genome chromosome conformation analysis (Hi-C) map of primary osteoarthritis chondrocytes and identify novel candidate effector genes for the disease. METHODS: Primary chondrocytes collected from 8 patients with knee osteoarthritis underwent Hi-C analysis to link chromosomal structure to genomic sequence. The identified loops were then combined with osteoarthritis GWAS results and epigenomic data from primary knee osteoarthritis chondrocytes to identify variants involved in gene regulation via enhancer-promoter interactions. RESULTS: We identified 345 genetic variants residing within chromatin loop anchors that are associated with 77 osteoarthritis GWAS signals. Ten of these variants reside directly in enhancer regions of 10 newly described active enhancer-promoter loops, identified with multiomics analysis of publicly available chromatin immunoprecipitation sequencing (ChIP-seq) and assay for transposase-accessible chromatin using sequencing (ATAC-seq) data from primary knee chondrocyte cells, pointing to two new candidate effector genes SPRY4 and PAPPA (pregnancy-associated plasma protein A) as well as further support for the gene SLC44A2 known to be involved in osteoarthritis. For example, PAPPA is directly associated with the turnover of insulin-like growth factor 1 (IGF-1) proteins, and IGF-1 is an important factor in the repair of damaged chondrocytes. CONCLUSIONS: We have constructed the first Hi-C map of primary human chondrocytes and have made it available as a resource for the scientific community. By integrating 3D genomics with large-scale genetic association and epigenetic data, we identify novel candidate effector genes for osteoarthritis, which enhance our understanding of disease and can serve as putative high-value novel drug targets.

Causal relationships between anthropometric traits, bone mineral density, osteoarthritis and spinal stenosis: A Mendelian randomisation investigation.

OBJECTIVE: Spinal stenosis is a common condition among older individuals, with significant morbidity attached. Little is known about its risk factors but degenerative conditions, such as osteoarthritis (OA) have been identified for their mechanistic role. This study aims to explore causal relationships between anthropometric risk factors, OA, and spinal stenosis using Mendelian randomisation (MR) techniques. DESIGN: We applied two-sample MR to investigate the causal relationships between genetic liability for select risk factors and spinal stenosis. Next, we examined the genetic relationship between OA and spinal stenosis with linkage disequilibrium score regression and Causal Analysis Using Summary Effect estimates MR method. Finally, we used multivariable MR (MVMR) to explore whether OA and body mass index (BMI) mediate the causal pathways identified. RESULTS: Our analysis revealed strong evidence for the effect of higher BMI (odds ratio [OR] = 1.54, 95%CI: 1.41-1.69, p-value = 2.7 × 10-21), waist (OR = 1.43, 95%CI: 1.15-1.79, p-value = 1.5 × 10-3) and hip (OR = 1.50, 95%CI: 1.27-1.78, p-value = 3.3 × 10-6) circumference on spinal stenosis. Strong evidence of causality was also observed for higher bone mineral density (BMD): total body (OR = 1.21, 95%CI: 1.12-1.29, p-value = 1.6 × 10-7), femoral neck (OR = 1.35, 95%CI: 1.09-1.37, p-value = 7.5×10-7), and lumbar spine (OR = 1.38, 95%CI: 1.25-1.52, p-value = 4.4 × 10-11). We detected high genetic correlations between spinal stenosis and OA (rg range: 0.47-0.66), with Causal Analysis Using Summary Effect estimates results supporting a causal effect of OA on spinal stenosis (ORallOA = 1.6, 95%CI: 1.41-1.79). Direct effects of BMI, BMD on spinal stenosis remained after adjusting for OA in the MVMR. CONCLUSIONS: Genetic susceptibility to anthropometric risk factors, particularly higher BMI and BMD can increase the risk of spinal stenosis, independent of OA status. These results may inform preventative strategies and treatments.

Linking chondrocyte and synovial transcriptional profile to clinical phenotype in osteoarthritis.

OBJECTIVES: To determine how gene expression profiles in osteoarthritis joint tissues relate to patient phenotypes and whether molecular subtypes can be reproducibly captured by a molecular classification algorithm. METHODS: We analysed RNA sequencing data from cartilage and synovium in 113 osteoarthritis patients, applying unsupervised clustering and Multi-Omics Factor Analysis to characterise transcriptional profiles. We tested the association of the molecularly defined patient subgroups with clinical characteristics from electronic health records. RESULTS: We detected two patient subgroups in low-grade cartilage (showing no/minimal degeneration, cartilage normal/softening only), with differences associated with inflammation, extracellular matrix-related and cell adhesion pathways. The high-inflammation subgroup was associated with female sex (OR 4.12, p=0.0024) and prescription of proton pump inhibitors (OR 4.21, p=0.0040). We identified two independent patient subgroupings in osteoarthritis synovium: one related to inflammation and the other to extracellular matrix and cell adhesion processes. A seven-gene classifier including MMP13, APOD, MMP2, MMP1, CYTL1, IL6 and C15orf48 recapitulated the main axis of molecular heterogeneity in low-grade knee osteoarthritis cartilage (correlation ρ=-0.88, p<10-10) and was reproducible in an independent patient cohort (ρ=-0.85, p<10-10). CONCLUSIONS: These data support the reproducible stratification of osteoarthritis patients by molecular subtype and the exploration of new avenues for tailored treatments.

Genome-wide association of phenotypes based on clustering patterns of hand osteoarthritis identify WNT9A as novel osteoarthritis gene.

BACKGROUND: Despite recent advances in the understanding of the genetic architecture of osteoarthritis (OA), only two genetic loci have been identified for OA of the hand, in part explained by the complexity of the different hand joints and heterogeneity of OA pathology. METHODS: We used data from the Rotterdam Study (RSI, RSII and RSIII) to create three hand OA phenotypes based on clustering patterns of radiographic OA severity to increase power in our modest discovery genome-wide association studies in the RS (n=8700), and sought replication in an independent cohort, the Framingham Heart Study (n=1203). We used multiple approaches that leverage different levels of information and functional data to further investigate the underlying biological mechanisms and candidate genes for replicated loci. We also attempted to replicate known OA loci at other joint sites, including the hips and knees. RESULTS: We found two novel genome-wide significant loci for OA in the thumb joints. We identified WNT9A as a possible novel causal gene involved in OA pathogenesis. Furthermore, several previously identified genetic loci for OA seem to confer risk for OA across multiple joints: TGFa, RUNX2, COL27A1, ASTN2, IL11 and GDF5 loci. CONCLUSIONS: We identified a robust novel genetic locus for hand OA on chromosome 1, of which WNT9A is the most likely causal gene. In addition, multiple genetic loci were identified to be associated with OA across multiple joints. Our study confirms the potential for novel insight into the genetic architecture of OA by using biologically meaningful stratified phenotypes.

A Genome-wide Association Study of Dupuytren Disease Reveals 17 Additional Variants Implicated in Fibrosis.

Individuals with Dupuytren disease (DD) are commonly seen by physicians and surgeons across multiple specialties. It is an increasingly common and disabling fibroproliferative disorder of the palmar fascia, which leads to flexion contractures of the digits, and is associated with other tissue-specific fibroses. DD affects between 5% and 25% of people of European descent and is the most common inherited disease of connective tissue. We undertook the largest GWAS to date in individuals with a surgically validated diagnosis of DD from the UK, with replication in British, Dutch, and German individuals. We validated association at all nine previously described signals and discovered 17 additional variants with p ≤ 5 × 10-8. As a proof of principle, we demonstrated correlation of the high-risk genotype at the statistically most strongly associated variant with decreased secretion of the soluble WNT-antagonist SFRP4, in surgical specimen-derived DD myofibroblasts. These results highlight important pathways involved in the pathogenesis of fibrosis, including WNT signaling, extracellular matrix modulation, and inflammation. In addition, many associated loci contain genes that were hitherto unrecognized as playing a role in fibrosis, opening up new avenues of research that may lead to novel treatments for DD and fibrosis more generally. DD represents an ideal human model disease for fibrosis research.

Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits.

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

Very low-depth whole-genome sequencing in complex trait association studies.

MOTIVATION: Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. RESULTS: We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. AVAILABILITY AND IMPLEMENTATION: The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter, the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pathways to understanding the genomic aetiology of osteoarthritis.

Osteoarthritis is a common, complex disease with no curative therapy. In this review, we summarize current knowledge on disease aetiopathogenesis and outline genetics and genomics approaches that are helping catalyse a much-needed improved understanding of the biological underpinning of disease development and progression.

The 2018 Otto Aufranc Award: How Does Genome-wide Variation Affect Osteolysis Risk After THA?

BACKGROUND: Periprosthetic osteolysis resulting in aseptic loosening is a leading cause of THA revision. Individuals vary in their susceptibility to osteolysis and heritable factors may contribute to this variation. However, the overall contribution that such variation makes to osteolysis risk is unknown. QUESTIONS/PURPOSES: We conducted two genome-wide association studies to (1) identify genetic risk loci associated with susceptibility to osteolysis; and (2) identify genetic risk loci associated with time to prosthesis revision for osteolysis. METHODS: The Norway cohort comprised 2624 patients after THA recruited from the Norwegian Arthroplasty Registry, of whom 779 had undergone revision surgery for osteolysis. The UK cohort included 890 patients previously recruited from hospitals in the north of England, 317 who either had radiographic evidence of and/or had undergone revision surgery for osteolysis. All participants had received a fully cemented or hybrid THA using a small-diameter metal or ceramic-on-conventional polyethylene bearing. Osteolysis susceptibility case-control analyses and quantitative trait analyses for time to prosthesis revision (a proxy measure of the speed of osteolysis onset) in those patients with osteolysis were undertaken in each cohort separately after genome-wide genotyping. Finally, a meta-analysis of the two independent cohort association analysis results was undertaken. RESULTS: Genome-wide association analysis identified four independent suggestive genetic signals for osteolysis case-control status in the Norwegian cohort and 11 in the UK cohort (p ≤ 5 x 10). After meta-analysis, five independent genetic signals showed a suggestive association with osteolysis case-control status at p ≤ 5 x 10 with the strongest comprising 18 correlated variants on chromosome 7 (lead signal rs850092, p = 1.13 x 10). Genome-wide quantitative trait analysis in cases only showed a total of five and nine independent genetic signals for time to revision at p ≤ 5 x 10, respectively. After meta-analysis, 11 independent genetic signals showed suggestive evidence of an association with time to revision at p ≤ 5 x 10 with the largest association block comprising 174 correlated variants in chromosome 15 (lead signal rs10507055, p = 1.40 x 10). CONCLUSIONS: We explored the heritable biology of osteolysis at the whole genome level and identify several genetic loci that associate with susceptibility to osteolysis or with premature revision surgery. However, further studies are required to determine a causal association between the identified signals and osteolysis and their functional role in the disease. CLINICAL RELEVANCE: The identification of novel genetic risk loci for osteolysis enables new investigative avenues for clinical biomarker discovery and therapeutic intervention in this disease.

Very low-depth sequencing in a founder population identifies a cardioprotective APOC3 signal missed by genome-wide imputation.

Cohort-wide very low-depth whole-genome sequencing (WGS) can comprehensively capture low-frequency sequence variation for the cost of a dense genome-wide genotyping array. Here, we analyse 1x sequence data across the APOC3 gene in a founder population from the island of Crete in Greece (n = 1239) and find significant evidence for association with blood triglyceride levels with the previously reported R19X cardioprotective null mutation (ß = -1.09,σ = 0.163, P = 8.2 × 10-11) and a second loss of function mutation, rs138326449 (ß = -1.17,σ = 0.188, P = 1.14 × 10-9). The signal cannot be recapitulated by imputing genome-wide genotype data on a large reference panel of 5122 individuals including 249 with 4x WGS data from the same population. Gene-level meta-analysis with other studies reporting burden signals at APOC3 provides robust evidence for a replicable cardioprotective rare variant aggregation (P = 3.2 × 10-31, n = 13 480).

Analysis with the exome array identifies multiple new independent variants in lipid loci.

It has been hypothesized that low frequency (1-5% minor allele frequency (MAF)) and rare (<1% MAF) variants with large effect sizes may contribute to the missing heritability in complex traits. Here, we report an association analysis of lipid traits (total cholesterol, LDL-cholesterol, HDL-cholesterol triglycerides) in up to 27 312 individuals with a comprehensive set of low frequency coding variants (ExomeChip), combined with conditional analysis in the known lipid loci. No new locus reached genome-wide significance. However, we found a new lead variant in 26 known lipid association regions of which 16 were >1000-fold more significant than the previous sentinel variant and not in close LD (six had MAF <5%). Furthermore, conditional analysis revealed multiple independent signals (ranging from 1 to 5) in a third of the 98 lipid loci tested, including rare variants. Addition of our novel associations resulted in between 1.5- and 2.5-fold increase in the proportion of heritability explained for the different lipid traits. Our findings suggest that rare coding variants contribute to the genetic architecture of lipid traits.

Widespread epigenomic, transcriptomic and proteomic differences between hip osteophytic and articular chondrocytes in osteoarthritis.

Objectives: To identify molecular differences between chondrocytes from osteophytic and articular cartilage tissue from OA patients. Methods: We investigated genes and pathways by combining genome-wide DNA methylation, RNA sequencing and quantitative proteomics in isolated primary chondrocytes from the cartilaginous layer of osteophytes and matched areas of low- and high-grade articular cartilage across nine patients with OA undergoing hip replacement surgery. Results: Chondrocytes from osteophytic cartilage showed widespread differences to low-grade articular cartilage chondrocytes. These differences were similar to, but more pronounced than, differences between chondrocytes from osteophytic and high-grade articular cartilage, and more pronounced than differences between high- and low-grade articular cartilage. We identified 56 genes with significant differences between osteophytic chondrocytes and low-grade articular cartilage chondrocytes on all three omics levels. Several of these genes have known roles in OA, including ALDH1A2 and cartilage oligomeric matrix protein, which have functional genetic variants associated with OA from genome-wide association studies. An integrative gene ontology enrichment analysis showed that differences between osteophytic and low-grade articular cartilage chondrocytes are associated with extracellular matrix organization, skeletal system development, platelet aggregation and regulation of ERK1 and ERK2 cascade. Conclusion: We present a first comprehensive view of the molecular landscape of chondrocytes from osteophytic cartilage as compared with articular cartilage chondrocytes from the same joints in OA. We found robust changes at genes relevant to chondrocyte function, providing insight into biological processes involved in osteophyte development and thus OA progression.

Combination therapy as a potential risk factor for the development of type 2 diabetes in patients with schizophrenia: the GOMAP study.

BACKGROUND: Schizophrenia (SCZ) is associated with increased risk of type 2 diabetes (T2D). The potential diabetogenic effect of concomitant application of psychotropic treatment classes in patients with SCZ has not yet been evaluated. The overarching goal of the Genetic Overlap between Metabolic and Psychiatric disease (GOMAP) study is to assess the effect of pharmacological, anthropometric, lifestyle and clinical measurements, helping elucidate the mechanisms underlying the aetiology of T2D. METHODS: The GOMAP case-control study (Genetic Overlap between Metabolic and Psychiatric disease) includes hospitalized patients with SCZ, some of whom have T2D. We enrolled 1653 patients with SCZ; 611 with T2D and 1042 patients without T2D. This is the first study of SCZ and T2D comorbidity at this scale in the Greek population. We retrieved detailed information on first- and second-generation antipsychotics (FGA, SGA), antidepressants and mood stabilizers, applied as monotherapy, 2-drug combination, or as 3- or more drug combination. We assessed the effects of psychotropic medication, body mass index, duration of schizophrenia, number of hospitalizations and physical activity on risk of T2D. Using logistic regression, we calculated crude and adjusted odds ratios (OR) to identify associations between demographic factors and the psychiatric medications. RESULTS: Patients with SCZ on a combination of at least three different classes of psychiatric drugs had a higher risk of T2D [OR 1.81 (95% CI 1.22-2.69); p = 0.003] compared to FGA alone therapy, after adjustment for age, BMI, sex, duration of SCZ and number of hospitalizations. We did not find evidence for an association of SGA use or the combination of drugs belonging to two different classes of psychiatric medications with increased risk of T2D [1.27 (0.84-1.93), p = 0.259 and 0.98 (0.71-1.35), p = 0.885, respectively] compared to FGA use. CONCLUSIONS: We find an increased risk of T2D in patients with SCZ who take a combination of at least three different psychotropic medication classes compared to patients whose medication consists only of one or two classes of drugs.

Radiographic endophenotyping in hip osteoarthritis improves the precision of genetic association analysis.

OBJECTIVE: Osteoarthritis (OA) has a strong genetic component but the success of previous genome-wide association studies (GWAS) has been restricted due to insufficient sample sizes and phenotype heterogeneity. Our aim was to examine the effect of clinically relevant endophenotyping according to site of maximal joint space narrowing (maxJSN) and bone remodelling response on GWAS signal detection in hip OA. METHODS: A stratified GWAS meta-analysis was conducted in 2118 radiographically defined hip OA cases and 6500 population-based controls. Signals were followed up by analysing differential expression of proximal genes for bone remodelling endophenotypes in 33 pairs of macroscopically intact and OA-affected cartilage. RESULTS: We report suggestive evidence (p<5×10-6) of association at 6 variants with OA endophenotypes that would have been missed by using presence of hip OA as the disease end point. For example, in the analysis of hip OA cases with superior maxJSN versus cases with non-superior maxJSN we detected association with a variant in the LRCH1 gene (rs754106, p=1.49×10-7, OR (95% CIs) 0.70 (0.61 to 0.80)). In the comparison of hypertrophic with non-hypertrophic OA the most significant variant was located between STT3B and GADL1 (rs6766414, p=3.13×10-6, OR (95% CIs) 1.45 (1.24 to 1.69)). Both of these associations were fully attenuated in non-stratified analyses of all hip OA cases versus population controls (p>0.05). STT3B was significantly upregulated in OA-affected versus intact cartilage, particularly in the analysis of hypertrophic and normotrophic compared with atrophic bone remodelling pattern (p=4.2×10-4). CONCLUSIONS: Our findings demonstrate that stratification of OA cases into more homogeneous endophenotypes can identify genes of potential functional importance otherwise obscured by disease heterogeneity.

Genome-wide meta-analysis of common variant differences between men and women.

The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 × 10(-8)) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across ~115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.