Pleiotropy in complex traits

Genome-wide association studies (GWAS) have uncovered thousands of complex trait loci, many of which are associated with multiple phenotypes. The dedicated study of these pleiotropic effects is becoming increasingly common due to the availability of sample collections with high-dimensional phenotype data, such as the UK Biobank, and can yield important insights into the aetiology underlying complex disorders. In my PhD, I performed multi-trait analyses of medically relevant complex phenotypes to identify shared genetic factors. My first project involved a genome-wide overlap analysis of osteoarthritis (OA) and bone-mineral density (BMD), using summary statistics from two large-scale GWAS. OA and BMD are known to be inversely correlated, yet the genetics underlying this link remain poorly understood. I found robust evidence for association with OA at the SMAD3 locus, which is known to play a role in bone remodeling and cartilage maintenance. My second project aimed to elucidate the increased prevalence of type 2 diabetes (T2D) in schizophrenia (SCZ) patients. I used GWAS summary statistics of SCZ and T2D from the PGC and DIAGRAM consortia, respectively, to perform polygenic risk score analyses in three patient groups (SCZ only, T2D only, comorbid SCZ and T2D) and population-based controls. I find that the comorbid patient group have a higher genetic risk for both T2D and SCZ compared to controls, supporting the hypothesis that the epidemiologic link between these disorders is at least in part due to genetic factors. In my third project, I leveraged the correlation structure of over 274 protein biomarkers and 57 quantitative traits to perform multivariate GWAS on correlated trait clusters in a Greek isolated population. This approach uncovered several novel cis-associations not identified in single-trait GWAS, and highlights the power advantage of multivariate analysis. An important consideration for future studies will be the interpretation and follow-up of cross-phenotype associations, and the translation of these insights into clinical use

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

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

Evaluating the glucose raising effect of established loci via a genetic risk score.

Recent genome-wide association studies have identified several single nucleotide polymorphisms (SNPs) associated with glucose levels. We tested the hypothesis here whether the cumulative effect of glucose raising SNPs, assessed via a score, is associated with glucose levels. A total of 1,434 participants of Greek descent from the THISEAS study and 1,160 participants form the GOMAP study were included in this analysis. We developed a genetic risk score (GRS), based on the known glucose-raising loci, in order to investigate the cumulative effect of known glucose loci on glucose levels. In the THISEAS study, the GRS score was significantly associated with increased glucose levels (mmol/L) (β ± SE: 0.024 ± 0.004, P = 8.27e-07). The effect of the genetic risk score was also significant in the GOMAP study (β ± SE: 0.011 ± 0.005, P = 0.031). In the meta-analysis of the two studies both scores were significantly associated with higher glucose levels GRS: β ± SE: 0.019 ± 0.003, P = 1.41e-09. Also, variants at the SLC30A8, PROX1, MTNR1B, ADRA2A, G6PC2, LPIN3 loci indicated nominal evidence for association with glucose levels (p < 0.05). We replicate associations of the established glucose raising variants in the Greek population and confirm directional consistency of effects (binomial sign test p = 6.96e-05). We also demonstrate that the cumulative effect of the established glucose loci yielded a significant association with increasing glucose levels

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 \u3c 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

Evaluation of shared genetic aetiology between osteoarthritis and bone mineral density identifies SMAD3 as a novel osteoarthritis risk locus

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 = 2.23 × 10-2), 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=3.12 × 10-10), 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.This work was funded by the Wellcome Trust (WT098051)

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.</p

Statistical methods to detect pleiotropy in human complex traits

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.</jats:p

Abstract 553: 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 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.</jats:p

A novel variant in <i>GLIS3</i> is associated with osteoarthritis

ObjectivesOsteoarthritis (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.MethodsWe 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.ResultsWe 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.ConclusionsWe identify a novel susceptibility locus for OA that has been previously implicated in diabetes and glycaemic traits.</jats:sec