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.

Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. 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 previously unreported. We define eight non-overlapping clusters of T2D signals characterised 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, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care.

Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing.

Cardiometabolic diseases, such as type 2 diabetes and cardiovascular disease, have a high public health burden. Understanding the genetically determined regulation of proteins that are dysregulated in disease can help to dissect the complex biology underpinning them. Here, we perform a protein quantitative trait locus (pQTL) analysis of 248 serum proteins relevant to cardiometabolic processes in 2893 individuals. Meta-analyzing whole-genome sequencing (WGS) data from two Greek cohorts, MANOLIS (n = 1356; 22.5× WGS) and Pomak (n = 1537; 18.4× WGS), we detect 301 independently associated pQTL variants for 170 proteins, including 12 rare variants (minor allele frequency < 1%). We additionally find 15 pQTL variants that are rare in non-Finnish European populations but have drifted up in the frequency in the discovery cohorts here. We identify proteins causally associated with cardiometabolic traits, including Mep1b for high-density lipoprotein (HDL) levels, and describe a knock-out (KO) Mep1b mouse model. Our findings furnish insights into the genetic architecture of the serum proteome, identify new protein-disease relationships and demonstrate the importance of isolated populations in pQTL analysis.

Elevations in blood glucose before and after the appearance of islet autoantibodies in children.

The etiology of type 1 diabetes has polygenic and environmental determinants that lead to autoimmune responses against pancreatic ß cells and promote ß cell death. The autoimmunity is considered silent without metabolic consequences until late preclinical stages,and it remains unknown how early in the disease process the pancreatic ß cell is compromised. To address this, we investigated preprandial nonfasting and postprandial blood glucose concentrations and islet autoantibody development in 1,050 children with high genetic risk of type 1 diabetes. Pre- and postprandial blood glucose decreased between 4 and 18 months of age and gradually increased until the final measurements at 3.6 years of age. Determinants of blood glucose trajectories in the first year of life included sex, body mass index, glucose-related genetic risk scores, and the type 1 diabetes-susceptible INS gene. Children who developed islet autoantibodies had early elevations in blood glucose concentrations. A sharp and sustained rise in postprandial blood glucose was observed at around 2 months prior to autoantibody seroconversion, with further increases in postprandial and, subsequently, preprandial values after seroconversion. These findings show heterogeneity in blood glucose control in infancy and early childhood and suggest that islet autoimmunity is concurrent or subsequent to insults on the pancreatic islets.

The Kinesin Gene KIF26B Modulates the Severity of Post-Traumatic Heterotopic Ossification.

The formation of pathological bone deposits within soft tissues, termed heterotopic ossification (HO), is common after trauma. However, the severity of HO formation varies substantially between individuals, from relatively isolated small bone islands through to extensive soft tissue replacement by bone giving rise to debilitating symptoms. The aim of this study was to identify novel candidate therapeutic molecular targets for severe HO. We conducted a genome-wide scan in men and women with HO of varying severity following hip replacement for osteoarthritis. HO severity was dichotomized as mild or severe, and association analysis was performed with adjustment for age and sex. We next confirmed expression of the gene encoded by the lead signal in human bone and in primary human mesenchymal stem cells. We then examined the effect of gene knockout in a murine model of osseous trans-differentiation, and finally we explored transcription factor phosphorylation in key pathways perturbed by the gene. Ten independent signals were suggestively associated with HO severity, with KIF26B as the lead. We subsequently confirmed KIF26B expression in human bone and upregulation upon BMP2-induced osteogenic differentiation in primary human mesenchymal stem cells, and also in a rat tendo-Achilles model of post-traumatic HO. CRISPR-Cas9 mediated knockout of Kif26b inhibited BMP2-induced Runx2, Sp7/Osterix, Col1A1, Alp, and Bglap/Osteocalcin expression and mineralized nodule formation in a murine myocyte model of osteogenic trans-differentiation. Finally, KIF26B deficiency inhibited ERK MAP kinase activation during osteogenesis, whilst augmenting p38 and SMAD 1/5/8 phosphorylation. Taken together, these data suggest a role for KIF26B in modulating the severity of post-traumatic HO and provide a potential novel avenue for therapeutic translation.

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.

Using multivariable Mendelian randomization to estimate the causal effect of bone mineral density on osteoarthritis risk, independently of body mass index.

OBJECTIVES: Observational analyses suggest that high bone mineral density (BMD) is a risk factor for osteoarthritis (OA); it is unclear whether this represents a causal effect or shared aetiology and whether these relationships are body mass index (BMI)-independent. We performed bidirectional Mendelian randomization (MR) to uncover the causal pathways between BMD, BMI and OA. METHODS: One-sample (1S)MR estimates were generated by two-stage least-squares regression. Unweighted allele scores instrumented each exposure. Two-sample (2S)MR estimates were generated using inverse-variance weighted random-effects meta-analysis. Multivariable MR (MVMR), including BMD and BMI instruments in the same model, determined the BMI-independent causal pathway from BMD to OA. Latent causal variable (LCV) analysis, using weight-adjusted femoral neck (FN)-BMD and hip/knee OA summary statistics, determined whether genetic correlation explained the causal effect of BMD on OA. RESULTS: 1SMR provided strong evidence for a causal effect of BMD estimated from heel ultrasound (eBMD) on hip and knee OA {odds ratio [OR]hip = 1.28 [95% confidence interval (CI) = 1.05, 1.57], p = 0.02, ORknee = 1.40 [95% CI = 1.20, 1.63], p = 3 × 10-5, OR per standard deviation [SD] increase}. 2SMR effect sizes were consistent in direction. Results suggested that the causal pathways between eBMD and OA were bidirectional (ßhip = 1.10 [95% CI = 0.36, 1.84], p = 0.003, ßknee = 4.16 [95% CI = 2.74, 5.57], p = 8 × 10-9, ß = SD increase per doubling in risk). MVMR identified a BMI-independent causal pathway between eBMD and hip/knee OA. LCV suggested that genetic correlation (i.e. shared genetic aetiology) did not fully explain the causal effects of BMD on hip/knee OA. CONCLUSIONS: These results provide evidence for a BMI-independent causal effect of eBMD on OA. Despite evidence of bidirectional effects, the effect of BMD on OA did not appear to be fully explained by shared genetic aetiology, suggesting a direct action of bone on joint deterioration.

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.

Osteocyte transcriptome mapping identifies a molecular landscape controlling skeletal homeostasis and susceptibility to skeletal disease.

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this programme is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders (P = 2.4 × 10-22) and are associated with the polygenic diseases osteoporosis (P = 1.8 × 10-13) and osteoarthritis (P = 1.6 × 10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease.

Replication of HLA class II locus association with susceptibility to podoconiosis in three Ethiopian ethnic groups.

Podoconiosis, a debilitating lymphoedema of the leg, results from barefoot exposure to volcanic clay soil in genetically susceptible individuals. A previous genome-wide association study (GWAS) conducted in the Wolaita ethnic group from Ethiopia showed association between single nucleotide polymorphisms (SNPs) in the HLA class II region and podoconiosis. We aimed to conduct a second GWAS in a new sample (N = 1892) collected from the Wolaita and two other Ethiopian populations, the Amhara and the Oromo, also affected by podoconiosis. Fourteen SNPs in the HLA class II region showed significant genome-wide association (P < 5.0 × 10-8) with podoconiosis. The lead SNP was rs9270911 (P = 5.51 × 10-10; OR 1.53; 95% CI 1.34-1.74), located near HLA-DRB1. Inclusion of data from the first GWAS (combined N = 2289) identified 47 SNPs in the class II HLA region that were significantly associated with podoconiosis (lead SNP also rs9270911 (P = 2.25 × 10-12). No new loci outside of the HLA class II region were identified in this more highly-powered second GWAS. Our findings confirm the HLA class II association with podoconiosis suggesting HLA-mediated abnormal induction and regulation of immune responses may have a direct role in its pathogenesis.

Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa.

Genomic studies in African populations provide unique opportunities to understand disease etiology, human diversity, and population history. In the largest study of its kind, comprising genome-wide data from 6,400 individuals and whole-genome sequences from 1,978 individuals from rural Uganda, we find evidence of geographically correlated fine-scale population substructure. Historically, the ancestry of modern Ugandans was best represented by a mixture of ancient East African pastoralists. We demonstrate the value of the largest sequence panel from Africa to date as an imputation resource. Examining 34 cardiometabolic traits, we show systematic differences in trait heritability between European and African populations, probably reflecting the differential impact of genes and environment. In a multi-trait pan-African GWAS of up to 14,126 individuals, we identify novel loci associated with anthropometric, hematological, lipid, and glycemic traits. We find that several functionally important signals are driven by Africa-specific variants, highlighting the value of studying diverse populations across the region.

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.

Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data.

Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we performed a genome-wide association study for osteoarthritis (77,052 cases and 378,169 controls), analyzing four phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discovered 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine-mapped to a single variant. We identified putative effector genes by integrating expression quantitative trait loci (eQTL) colocalization, fine-mapping, and human rare-disease, animal-model, and osteoarthritis tissue expression data. We found enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organization biological pathways. Ten of the likely effector genes, including TGFB1 (transforming growth factor beta 1), FGF18 (fibroblast growth factor 18), CTSK (cathepsin K), and IL11 (interleukin 11), have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis.

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.

Author Correction: Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits.

The original version of this Article contained an error in Fig. 2. In panel a, the two legend items "rare" and "common" were inadvertently swapped. This has been corrected in both the PDF and HTML versions of the Article.

Cohort-wide deep whole genome sequencing and the allelic architecture of complex traits.

The role of rare variants in complex traits remains uncharted. Here, we conduct deep whole genome sequencing of 1457 individuals from an isolated population, and test for rare variant burdens across six cardiometabolic traits. We identify a role for rare regulatory variation, which has hitherto been missed. We find evidence of rare variant burdens that are independent of established common variant signals (ADIPOQ and adiponectin, P = 4.2 × 10-8; APOC3 and triglyceride levels, P = 1.5 × 10-26), and identify replicating evidence for a burden associated with triglyceride levels in FAM189B (P = 2.2 × 10-8), indicating a role for this gene in lipid metabolism.

Genome-wide association study of developmental dysplasia of the hip identifies an association with GDF5.

Developmental dysplasia of the hip (DDH) is the most common skeletal developmental disease. However, its genetic architecture is poorly understood. We conduct the largest DDH genome-wide association study to date and replicate our findings in independent cohorts. We find the heritable component of DDH attributable to common genetic variants to be 55% and distributed equally across the autosomal and X-chromosomes. We identify replicating evidence for association between GDF5 promoter variation and DDH (rs143384, effect allele A, odds ratio 1.44, 95% confidence interval 1.34-1.56, P = 3.55 × 10-22). Gene-based analysis implicates GDF5 (P = 9.24 × 10-12), UQCC1 (P = 1.86 × 10- 10), MMP24 (P = 3.18 × 10-9), RETSAT (P = 3.70 × 10- 8) and PDRG1 (P = 1.06 × 10- 7) in DDH susceptibility. We find shared genetic architecture between DDH and hip osteoarthritis, but no predictive power of osteoarthritis polygenic risk score on DDH status, underscoring the complex nature of the two traits. We report a scalable, time-efficient recruitment strategy and establish for the first time to our knowledge a robust DDH genetic association locus at GDF5.

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.