Introduction The recent advent of personalised assays capable of detecting circulating cell-free tumour DNA (ctDNA) has enabled detection of molecular residual disease (MRD) and recurrence following curative-intent therapy. We conducted LIONESS, a single-centre prospective cohort study to assess ctDNA in patients with HNSCC receiving primary surgery with curative intent to determine whether post-operative ctDNA detection can act as a biomarker for surgical tumour clearance and to evaluate the potential of personalised ctDNA analysis for early molecular-level detection of relapse or prior to clinically confirmed recurrence.

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.

6017 Background: Despite improvements in multimodal treatment options for patients with head and neck squamous cell carcinoma (HNSCC), survival has only improved modestly over the past decades as patients frequently develop recurrences. Detection of cell-free circulating tumor DNA (ctDNA) post-operatively and during clinical follow-up has the potential to identify patients with molecular residual disease (MRD) or who are at an increased risk of relapse, and who may therefore benefit from personalized treatment strategies. Methods: We conducted LIONESS, a single-center prospective cohort study to investigate ctDNA in patients with HNSCC who received primary surgical treatment with curative intent. 58 patients have been recruited as of January 2022, with disease stage I (15.5%), II (13.8%), III (36.2%) and IV (34.5%). Whole exome sequencing was performed on FFPE tissue. RaDaR, a highly sensitive personalized assay using deep sequencing of tumor-specific variants, was used to analyze serial pre- and post-operative plasma samples for evidence of molecular residual disease and recurrence. In addition, pre-operative saliva samples were collected for detection of tumor DNA in saliva. Results: 236 longitudinal plasma samples from 35 patients have been analyzed so far, using personalized panels designed targeting a median 48 (20-60) somatic variants. Preliminary data shows 94.28% ctDNA detection in baseline samples taken prior to surgery. In post-surgery samples, ctDNA could be detected at levels as low as 0.0005% variant allele fraction (eVAF). Survival analysis showed a significant difference (p-value = 7e-7) in recurrence rates between patients who tested ctDNA+ during follow-up (8/11, 72.7%) and those who were negative for ctDNA (0/24, 0%). In all eight cases with clinical recurrence, ctDNA was detected prior to progression, with lead times ranging from 56 to 265 days. Patients were followed for clinical recurrence with a median follow-up of 9.5 months to date, but longer follow-up is necessary for patients who may be at increased risk of recurrence. Work is ongoing for analysis of the full cohort of plasma and saliva samples, which will be presented at the congress. Conclusions: In this prospective observational study, detection of residual disease using ctDNA was associated with poorer progression-free survival and much earlier detection of disease prior to clinical relapse. The implementation of a highly sensitive ctDNA assay such as RaDaR into clinical practice has the potential to tailor personalized treatment strategies for molecular residual disease and recurrence in patients with HNSCC. In future, ctDNA analysis with subsequent ctDNA-guided treatment may reduce morbidity for HNSCC patients.

Cohort 1 of the phase 1B NABUCCO trial showed high pathological complete response (pCR) rates with preoperative ipilimumab plus nivolumab in stage III urothelial cancer (UC). In cohort 2, the aim was dose adjustment to optimize responses. Additionally, we report secondary endpoints, including efficacy and tolerability, in cohort 2 and the association of presurgical absence of circulating tumor DNA (ctDNA) in urine and plasma with clinical outcome in both cohorts. Thirty patients received two cycles of either ipilimumab 3 mg kg −1 plus nivolumab 1 mg kg −1 (cohort 2A) or ipilimumab 1 mg kg −1 plus nivolumab 3 mg kg −1 (cohort 2B), both followed by nivolumab 3 mg kg −1 . We observed a pCR in six (43%) patients in cohort 2A and a pCR in one (7%) patient in cohort 2B. Absence of urinary ctDNA correlated with pCR in the bladder (ypT0Nx) but not with progression-free survival (PFS). Absence of plasma ctDNA correlated with pCR (odds ratio: 45.0; 95% confidence interval (CI): 4.9–416.5) and PFS (hazard ratio: 10.4; 95% CI: 2.9–37.5). Our data suggest that high-dose ipilimumab plus nivolumab is required in stage III UC and that absence of ctDNA in plasma can predict PFS. ClinicalTrials.gov registration: NCT03387761 .

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.

We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10−9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background. Genome-wide association and fine-mapping analyses in ancestrally diverse populations implicate candidate causal genes and mechanisms underlying type 2 diabetes. Trans-ancestry genetic risk scores enhance transferability across populations.

Patients (pts) with stage III (cT3-4aN0M0 or cT1-4aN1-3M0) urothelial cancer (UC) have a poor prognosis. In NABUCCO cohort 1, 24 stage III UC pts were treated with ipilimumab (ipi) plus nivolumab (nivo) followed by radical surgery (day 1: ipi 3 mg/kg; day 22: ipi 3 mg/kg + nivo 1 mg/kg; day 43: nivo 3 mg/kg). 14/24 (58%) of pts showed a pathological response (ypT0N0 or ypTisN0/ypTaN0). Currently, there are no good biomarkers to assess response before surgery, potentially leading to overtreatment and unnecessary surgical complications. Here, we investigated whether detection of circulating tumor DNA (ctDNA) in plasma and urine by the RaDaR™ personalized liquid biopsy assay was associated with treatment response and outcomes. EDTA-plasma and urine supernatant were collected before start of treatment (day 1; “baseline”), before each subsequent treatment cycle (day 22 and 43) and before radical surgery. WES was performed on tumor FFPE and peripheral blood germline DNA to identify somatic variants for designing patient-specific, multiplex PCR-based NGS RaDaR™ panels. Plasma and urine ctDNA was analyzed using these panels to determine ctDNA detection and its estimated variant allele frequency (eVAF). Tissue somatic variants were detected in all patients, a median of 48 variants was used per RaDaR™ panel (range: 43-51). ctDNA was detected in 50/94 plasma samples (53%) and in 74/93 urine samples (80%). Detection levels were higher in urine with a median eVAF of 1.98% (range: 0.00057%-35.85%) vs. 0.049% (range: 0.00026%-18.94%) in plasma. ctDNA was detected in baseline plasma in 10/14 (71%) responding pts (median eVAF: 0.325%) and in 8/10 (80%) non-responders (median eVAF: 0.107%). Changes in ctDNA levels reflected clinical responses. After treatment with ipi plus nivo, ctDNA was undetectable in 13/14 (93%) responding pts, and in only 4/10 (40%) of non-responders (p=0.0088). Of the 17 pts with undetectable ctDNA before surgery, 13 (76%) had a pathological response and 16/17 (94%) pts remained recurrence-free after a median follow-up of 34 months. Urine ctDNA was detected at baseline in 12/14 (86%) responding pts (median eVAF: 9.634%) and in 8/10 (80%) non-responders (median eVAF: 2%). After treatment with ipi plus nivo, urine ctDNA was detected in 8/14 (57%) responding pts (median eVAF: 0.87%), and in 8/10 (80%) non-responders (median eVAF: 0.162%). No association was observed between urine ctDNA detection and response (p=0.39). Detection of plasma ctDNA by RaDaR™ after neoadjuvant treatment was associated with pathological response and clinical outcome. In contrast, ctDNA detection in urine was not associated with outcomes. Absence of plasma ctDNA pre-surgery may predict complete response to ipi plus nivo at surgery and may be helpful in guiding clinical decisions in stage III UC, in particular to select pts for bladder-sparing strategies. Citation Format: Jeroen van Dorp, Christodoulos Pipinikas, Nick van Dijk, Greg Jones, Alberto Gil-Jimenez, Giovanni Marsico, Maurits L. van Montfoort, Sophie Hackinger, Linde Braaf, Kirsten McLay, Daan van den Broek, Bas W. Van Rhijn, Nitzan Rosenfeld, Michiel S. van der Heijden. Predicting pathological response after ipilimumab plus nivolumab in stage III urothelial cancer by liquid-biopsy assessment of plasma and urine ctDNA using the RaDaR assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1273.

Introduction Head and neck squamous cell carcinoma (HNSCC) remains a substantial burden to global health. Despite evolving therapies, 5-year survival is less than 50% and unlike other cancers, reliable biomarkers to monitor treatment response do not exist. Cell-free circulating tumor DNA (ctDNA) is an emerging biomarker but has not yet been studied sufficiently for HNSCC. The detection of ctDNA as a marker of minimal residual disease following curative-intent treatment holds promise for identifying patients at an increased risk of relapse, who may benefit from adjuvant radio(chemo)therapy or facilitate close monitoring with repeat resection if needed. Methods We conducted a single-center prospective experimental evidence-generating cohort study to assess ctDNA in 30 patients with p16-negative HNSCC (stages I-IVB) who received primary surgical treatment with curative intent at our institution. Whole exome sequencing (WES) was performed on formalin-fixed paraffin-embedded tumor tissue to a median depth of 250x. For each patient, we selected up to 48 somatic variants for personalized ctDNA assay design. We used the RaDaRTM assay to analyze serial pre- and post-operative plasma samples (range 2-6) for evidence of minimal residual disease or recurrence. Results In a subset of patients analyzed to evaluate the performance of RaDaR, personalized panels were designed with between 34 and 48 somatic variants (median 48). Preliminary data shows 100% ctDNA detection in baseline samples taken prior to surgery at tumor fractions ranging from 312 ppm (equivalent to 0.03% AF) to 7579 ppm (equivalent to 0.76% AF). In post-surgery samples, ctDNA could be detected at levels as low as 26 ppm (equivalent to 0.0026% AF). Analysis of follow-up plasma samples will be presented along with data from the full patient cohort. Conclusions This study illustrates the potential of ctDNA as a biomarker in HNSCC and demonstrates the feasibility of personalized ctDNA assays for the detection of minimal residual disease post-treatment and for monitoring for early detection of relapse. Citation Format: Susanne Flach, Karen Howarth, Sophie Hackinger, Christodoulos Pipinikas, Kirsten McLay, Giovanni Marsico, Christoph Walz, Olivier Gires, Martin Canis, Philipp Baumeister. Personalized circulating tumor DNA analysis in head and neck squamous cell carcinoma: Preliminary results of the Liquid BIOpsy for MiNimal RESidual DiSease Detection in Head and NeckSquamous Cell Carcinoma (LIONESS) study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 553.

Osteoarthritis (OA) is a common complex disease with high public health burden and no curative therapy. High bone mineral density (BMD) is associated with an increased risk of developing OA, suggesting a shared underlying biology. Here, we performed the first systematic overlap analysis of OA and BMD on a genome wide scale. We used summary statistics from the GEFOS consortium for lumbar spine (n=31,800) and femoral neck (n = 32,961) BMD, and from the arcOGEN consortium for three OA phenotypes (hip, ncases=3,498; knee, ncases=3,266; hip and/or knee, ncases=7,410; ncontrols=11,009). Performing LD score regression, we found a significant genetic correlation between the combined OA phenotype (hip and/or knee) and lumbar spine BMD (rg=0.18, P =2.23x10), which may be driven by the presence of spinal osteophytes. We identified 143 variants with evidence for cross-phenotype association which we took forward for replication in independent large-scale OA datasets, and subsequent meta-analysis with arcOGEN for a total sample size of up to 23,425 cases and 236,814 controls. We found robustly replicating evidence for association with OA at rs12901071 (OR 1.08 95% CI 1.05–1.11, Pmeta=3.12x10), an intronic variant in the SMAD3 gene, which is known to play a role in bone remodeling and cartilage maintenance. We were able to confirm expression of SMAD3 in intact and degraded cartilage of the knee and hip. Our findings provide the first systematic evaluation of pleiotropy between OA and BMD, highlight genes with biological relevance to both traits, and establish a robust new OA genetic risk locus at SMAD3. 2 Erasmus Medical Center Rotterdam

Abstract Background 5%-30% of patients with primary non-metastatic cancer eventually relapse and die of metastatic disease, even though no macroscopic disease remains after initial curative-intent treatment. Adjuvant therapy is often administered to target minimal residual disease (MRD) that may be present but does not change outcome for most patients. Detecting MRD by liquid biopsy analysis after initial treatment and in advance of overt relapse could help identify patients who may benefit from adjuvant therapy or enrolment in clinical trials. Methods We developed InVision®MRD, a highly sensitive and specific method for detection of trace levels of tumor DNA in plasma cell-free DNA. Tumor-specific variants are identified using whole exome sequencing of tumor tissue samples, and a bespoke process designs primer panels targeting mutated loci. A multiplex high-fidelity PCR using InVision® technology captures and amplifies up to 48 patient–specific tumor variants in cell-free DNA, followed by high depth next generation sequencing. Proprietary algorithms compare patient-specific sequencing data to controls,and integrate information across variants and replicates to detect trace levels of tumor-derived signal. The method is currently available for research use only (RUO). Results We evaluated the performance of the InVision®MRD assay using samples from cancer patients and cell-line dilutions. We diluted three different tumor cell-lines into their matched normal counterparts. Primer panels were designed against 48 variants, specific to the tumor-cell line. Dilutions were amplified by multiplex PCR and sequenced on the Illumina NovaSeq to depth >100,000 reads per locus. Using 48 variants, the InVision®MRD assay had a mean sensitivity of 100% at 20 parts per million (20 ppm, equivalent to 0.002%) and sensitivity of 70% at 10 ppm (0.001%) with an overall specificity of 100%. To evaluate the effect of the number of variants on sensitivity and specificity we performed bootstrapping experiments selecting subsets of variants. When using only 16 variants, the I InVision®MRD assay had a mean sensitivity of 95% at 40 ppm (0.004%), 64% at 20 ppm (0.002%) and 22% at 10 ppm (0.001%) with specificity 99.99%. We tested the InVision®MRD assay on samples from cancer patients and detected tumor DNA in patient plasma and diluted samples with estimated tumor fractions at and below 20 ppm (0.002%). Conclusions The InVision®MRD assay provides a highly sensitive and specific method to detect MRD in plasma samples of cancer patients. Our results demonstrate excellent sensitivity with analysis of 16 variants which is further enhanced when 48 variants are analyzed. The high sensitivity of our approach is key to providing early information to guide treatment decisions after initial treatment by surgery or chemo/radiotherapy. The approach is generalisable to multiple tumor types and specimen types. Citation Format: Giovanni Marsico, Garima Sharma, Malcolm Perry, Sophie Hackinger, Tim Forshew, Karen Howarth, Jamie Platt, Nitzan Rosenfeld, Robert Osborne. Analytical development of the RaDaRTM assay, a highly sensitive and specific assay for the monitoring of minimal residual disease [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3097.

The epidemiologic link between schizophrenia (SCZ) and type 2 diabetes (T2D) remains poorly understood. Here, we investigate the presence and extent of a shared genetic background between SCZ and T2D using genome-wide approaches. We performed a genome-wide association study (GWAS) and polygenic risk score analysis in a Greek sample collection (GOMAP) comprising three patient groups: SCZ only (n = 924), T2D only (n = 822), comorbid SCZ and T2D (n = 505); samples from two separate Greek cohorts were used as population-based controls (n = 1,125). We used genome-wide summary statistics from two large-scale GWAS of SCZ and T2D from the PGC and DIAGRAM consortia, respectively, to perform genetic overlap analyses, including a regional colocalisation test. We show for the first time that patients with comorbid SCZ and T2D have a higher genetic predisposition to both disorders compared to controls. We identify five genomic regions with evidence of colocalising SCZ and T2D signals, three of which contain known loci for both diseases. We also observe a significant excess of shared association signals between SCZ and T2D at nine out of ten investigated p value thresholds. Finally, we identify 29 genes associated with both T2D and SCZ, several of which have been implicated in biological processes relevant to these disorders. Together our results demonstrate that the observed comorbidity between SCZ and T2D is at least in part due to shared genetic mechanisms.

We aggregated genome-wide genotyping data from 32 European-descent GWAS (74,124 T2D cases, 824,006 controls) imputed to high-density reference panels of >30,000 sequenced haplotypes. Analysis of ˜27M variants (˜21M with minor allele frequency [MAF]<5%), identified 243 genome-wide significant loci (p<5×10−8; MAF 0.02%-50%; odds ratio [OR] 1.04-8.05), 135 not previously-implicated in T2D-predisposition. Conditional analyses revealed 160 additional distinct association signals (p<10−5) within the identified loci. The combined set of 403 T2D-risk signals includes 56 low-frequency (0.5%≤MAF<5%) and 24 rare (MAF<0.5%) index SNPs at 60 loci, including 14 with estimated allelic OR>2. Forty-one of the signals displayed effect-size heterogeneity between BMI-unadjusted and adjusted analyses. Increased sample size and improved imputation led to substantially more precise localisation of causal variants than previously attained: at 51 signals, the lead variant after fine-mapping accounted for >80% posterior probability of association (PPA) and at 18 of these, PPA exceeded 99%. Integration with islet regulatory annotations enriched for T2D association further reduced median credible set size (from 42 variants to 32) and extended the number of index variants with PPA>80% to 73. Although most signals mapped to regulatory sequence, we identified 18 genes as human validated therapeutic targets through coding variants that are causal for disease. Genome wide chip heritability accounted for 18% of T2D-risk, and individuals in the 2.5% extremes of a polygenic risk score generated from the GWAS data differed >9-fold in risk. Our observations highlight how increases in sample size and variant diversity deliver enhanced discovery and single-variant resolution of causal T2D-risk alleles, and the consequent impact on mechanistic insights and clinical translation.

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.

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

Objectives Osteoarthritis (OA) is a complex disease, but its genetic aetiology remains poorly characterised. To identify novel susceptibility loci for OA, we carried out a genome-wide association study (GWAS) in individuals from the largest UK-based OA collections to date. Methods We carried out a discovery GWAS in 5414 OA individuals with knee and/or hip total joint replacement (TJR) and 9939 population-based controls. We followed-up prioritised variants in OA subjects from the interim release of the UK Biobank resource (up to 12 658 cases and 50 898 controls) and our lead finding in operated OA subjects from the full release of UK Biobank (17 894 cases and 89 470 controls). We investigated its functional implications in methylation, gene expression and proteomics data in primary chondrocytes from 12 pairs of intact and degraded cartilage samples from patients undergoing TJR. Results We detect a genome-wide significant association at rs10116772 with TJR (P=3.7×10 −8 ; for allele A: OR (95% CI) 0.97 (0.96 to 0.98)), an intronic variant in GLIS3 , which is expressed in cartilage. Variants in strong correlation with rs10116772 have been associated with elevated plasma glucose levels and diabetes. Conclusions We identify a novel susceptibility locus for OA that has been previously implicated in diabetes and glycaemic traits.

Osteoarthritis is a common complex disease imposing a large public-health burden. Here, we performed a genome-wide association study for osteoarthritis, using data across 16.5 million variants from the UK Biobank resource. After performing replication and meta-analysis in up to 30,727 cases and 297,191 controls, we identified nine new osteoarthritis loci, in all of which the most likely causal variant was noncoding. For three loci, we detected association with biologically relevant radiographic endophenotypes, and in five signals we identified genes that were differentially expressed in degraded compared with intact articular cartilage from patients with osteoarthritis. We established causal effects on osteoarthritis for higher body mass index but not for triglyceride levels or genetic predisposition to type 2 diabetes.

Identification of coding variant associations for complex diseases offers a direct route to biological insight, but is dependent on appropriate inference concerning the causal impact of those variants on disease risk. We aggregated coding variant data for 81,412 type 2 diabetes (T2D) cases and 370,832 controls of diverse ancestry, identifying 40 distinct coding variant association signals (at 38 loci) reaching significance ( p <2.2×10 −7 ). Of these, 16 represent novel associations mapping outside known genome-wide association study (GWAS) signals. We make two important observations. First, despite a threefold increase in sample size over previous efforts, only five of the 40 signals are driven by variants with minor allele frequency <5%, and we find no evidence for low-frequency variants with allelic odds ratio >1.29. Second, we used GWAS data from 50,160 T2D cases and 465,272 controls of European ancestry to fine-map these associated coding variants in their regional context, with and without additional weighting to account for the global enrichment of complex trait association signals in coding exons. At the 37 signals for which we attempted fine-mapping, we demonstrate convincing support (posterior probability >80% under the “annotation-weighted” model) that coding variants are causal for the association at 16 (including novel signals involving POC5 p.His36Arg, ANKH p.Arg187Gln, WSCD2 p.Thr113Ile, PLCB3 p.Ser778Leu, and PNPLA3 p.Ile148Met). However, at 13 of the 37 loci, the associated coding variants represent “false leads” and naïve analysis could have led to an erroneous inference regarding the effector transcript mediating the signal. Accurate identification of validated targets is dependent on correct specification of the contribution of coding and non-coding mediated mechanisms at associated loci.

In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.

Osteoarthritis is a common complex disease with huge public health burden. Here we perform a genome-wide association study for osteoarthritis using data across 16.5 million variants from the UK Biobank resource. Following replication and meta-analysis in up to 30,727 cases and 297,191 controls, we report 9 new osteoarthritis loci, in all of which the most likely causal variant is non-coding. For three loci, we detect association with biologically-relevant radiographic endophenotypes, and in five signals we identify genes that are differentially expressed in degraded compared to intact articular cartilage from osteoarthritis patients. We establish causal effects for higher body mass index, but not for triglyceride levels or type 2 diabetes liability, on osteoarthritis.