Prof Mark McCarthy
|Research Area:||Genetics and Genomics|
|Technology Exchange:||Biobanking, Bioinformatics, Computational biology, Human genetics, SNP typing, Statistical genetics and Transcript profiling|
|Scientific Themes:||Diabetes, Endocrinology & Metabolism and Genes, Genetics, Epigenetics & Genomics|
|Keywords:||Diabetes, Genetics, Susceptibility-gene, Genomics and Statistical Genetics|
The diabetes research group led by Prof Mark McCarthy (Robert Turner Professor of Diabetes) is based at both the Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM) and the Wellcome Trust Centre for Human Genetics (WTCHG). This multidisciplinary research team includes clinicians, research nurses, laboratory-based research staff and computational biologists.
The research of the group focuses on the following strands:
- Susceptibility gene identification in type 2 diabetes and related phenotypes (including obesity and continuous measures of glycaemia) using large-scale genetic and genomic approaches. We are involved in the major international efforts to use large-scale genome wide association and next-generation sequencing efforts to map variants impacting on these phenotypes
- Translating gene identification into biological insights and clinical advances. WE use a variety of approaches involving human, animal models and cellular systems to define the biological and clinical consequences of these discoveries.
- Genomic epidemiology. We work to integrate genetic and genomic data to advance understanding of basic genomic processes.
- Statistical genetics and bioinformatics. We seek to develop and apply new approaches to tackle analytical challenges.
We are leading members of several international consortia in the field of complex trait genetics including the WTCCC, ENGAGE, INTERACT, DIAGRAM, GIANT, MAGIC, SUMMIT, DIRECT, GoT2D, T2D-GENES and STEMBANCC. Within Oxford, we are active within the NIHR-funded Biomedical Research Centre. Our recent research has made a major contribution to advances in the understanding of the genetic basis of diabetes and obesity: in recent years, our work has featured in over 50 papers published in Science, Nature and Nature Genetics. Our major focus in the years ahead lies in expanding the spectrum of variation implicated in susceptibility to these and related conditions (with colleagues we are generating some of the largest sequence-based data sets worldwide) and in translating this genetic information into advances in functional understanding and clinical management.
|Prof David Altshuler (RDM)||Broad INstitute||United States|
|Prof Mike Boehnke (RDM)||University of Michigan||United States|
|Prof Leif Groop (RDM)||Malmo Diabetes Centre||Sweden|
|Prof Kari Stefansson (RDM)||Decode||Iceland|
|Prof Jorge Ferrer (RDM)||University Clinic||Spain|
|Prof Oluf Pedersen (RDM)||Univ of Copenhagen||Denmark|
|Prof Andrew Hattersley (RDM)||PEninsula MEdical School||United Kingdom|
|Prof Timothy Frayling (RDM)||Peninsula Medical School||United Kingdom|
|Dr Manj Sandhu (RDM)||University of Cambridge||United Kingdom|
|Dr Ines Barroso (RDM)||Wellcome Trust Sanger Institute||United Kingdom|
|Prof Fredrik Karpe (RDM)||OCDEM||University of Oxford||United Kingdom|
|Prof Roger Cox (RDM)||MRC Harwell||United Kingdom|
|Prof Andrew P Morris||Wellcome Trust Centre for Human Genetics||University of Oxford||United Kingdom|
|Graham Hitman (RDM)||Barts and the London||United Kingdom|
|Mark Walker (RDM)||Newcastle University||United Kingdom|
|Nick Wareham (RDM)||University of Cambridge||United Kingdom|
|Panos Deloukas (RDM)||WTSI||United Kingdom|
|Tim Spector (RDM)||King's College||United Kingdom|
|Leena Peltonen (RDM)||Univ of Helsinki||Finland|
|Marjo Riitta Jarvelin (RDM)||Imperial College/Univ of Oulu||Finland|
|Alan Shuldiner (RDM)||Univ of Maryland||United States|
|Clifton Bogardus (RDM)||NIDDK Phoenix||United States|
|Dr Francis S Collins (RDM)||NIH||United States|
|Philippe Froguel (RDM)||Imperial College||United Kingdom|
|Juliana Chan (RDM)||Chinese University of Hong Kong||China|
|Weiping Jia (RDM)||Jiaotong University, Shanghai||China|
|Joel Hirschhorn (RDM)||Broad Institute||United States|
|Eleftheria Zeggini (RDM)||Wellcome Trust Sanger Institute||United Kingdom|
|Alvis Brazma (RDM)||European Bioinformatics Institute||United Kingdom|
|Prof Cecilia Lindgren||Wellcome Trust Centre for Human Genetics||University of Oxford||United Kingdom|
|Manolis Dermitzakis (RDM)||University of Geneva||Switzerland|
|Frances Ashcroft (RDM)||University of Oxford||United Kingdom|
|Per-Olof Berggren (RDM)||Karolinska Institute||Sweden|
|Erik Renstrom (RDM)||Lund University||Sweden|
|Thomas Jentsch (RDM)||Max-Delbruck-Zentrum, Berlin||Germany|
|Franz Hofmann (RDM)||Technishce Universitat Munich||Germany|
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis. Hide abstract
OBJECTIVE: Misdiagnosis of maturity-onset diabetes of the young (MODY) remains widespread, despite the benefits of optimized management. This cross-sectional study examined diagnostic misclassification of MODY in subjects with clinically labeled young adult-onset type 1 and type 2 diabetes by extending genetic testing beyond current guidelines. RESEARCH DESIGN AND METHODS: Individuals were selected for diagnostic sequencing if they displayed features atypical for their diagnostic label. From 247 case subjects with clinically labeled type 1 diabetes, we sequenced hepatocyte nuclear factor 1 α (HNF1A) and hepatocyte nuclear factor 4 α (HNF4A) in 20 with residual β-cell function ≥ 3 years from diagnosis (random or glucagon-stimulated C-peptide ≥ 0.2 nmol/L). From 322 with clinically labeled type 2 diabetes, we sequenced HNF1A and HNF4A in 80 with diabetes diagnosed ≤ 30 years and/or diabetes diagnosed ≤ 45 years without metabolic syndrome. We also sequenced the glucokinase (GCK) in 40 subjects with mild fasting hyperglycemia. RESULTS: In the type 1 diabetic group, two HNF1A mutations were found (0.8% prevalence). In type 2 diabetic subjects, 10 HNF1A, two HNF4A, and one GCK mutation were identified (4.0%). Only 47% of MODY case subjects identified met current guidelines for diagnostic sequencing. Follow-up revealed a further 12 mutation carriers among relatives. Twenty-seven percent of newly identified MODY subjects changed treatment, all with improved glycemic control (HbA(1c) 8.8 vs. 7.3% at 3 months; P = 0.02). CONCLUSIONS: The systematic use of widened diagnostic testing criteria doubled the numbers of MODY case subjects identified compared with current clinical practice. The yield was greatest in young adult-onset type 2 diabetes. We recommend that all patients diagnosed before age 30 and with presence of C-peptide at 3 years' duration are considered for molecular diagnostic analysis. Hide abstract
To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association studies (GWAS) (N = 10,768 individuals of European ancestry enrolled in pregnancy and/or birth cohorts) and followed up three lead signals in six replication studies (combined N = 19,089). rs7980687 on chromosome 12q24 (P = 8.1 × 10(-9)) and rs1042725 on chromosome 12q15 (P = 2.8 × 10(-10)) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height, their effects on infant head circumference were largely independent of height (P = 3.8 × 10(-7) for rs7980687 and P = 1.3 × 10(-7) for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P = 3.9 × 10(-6)). SNPs correlated to the 17q21 signal have shown genome-wide association with adult intracranial volume, Parkinson's disease and other neurodegenerative diseases, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life. Hide abstract
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control. © 2012 Nature America, Inc. All rights reserved. Hide abstract
Defining the genetic contribution of rare variants to common diseases is a major basic and clinical science challenge that could offer new insights into disease etiology and provide potential for directed gene- and pathway-based prevention and treatment. Common and rare nonsynonymous variants in the GCKR gene are associated with alterations in metabolic traits, most notably serum triglyceride levels. GCKR encodes glucokinase regulatory protein (GKRP), a predominantly nuclear protein that inhibits hepatic glucokinase (GCK) and plays a critical role in glucose homeostasis. The mode of action of rare GCKR variants remains unexplored. We identified 19 nonsynonymous GCKR variants among 800 individuals from the ClinSeq medical sequencing project. Excluding the previously described common missense variant p.Pro446Leu, all variants were rare in the cohort. Accordingly, we functionally characterized all variants to evaluate their potential phenotypic effects. Defects were observed for the majority of the rare variants after assessment of cellular localization, ability to interact with GCK, and kinetic activity of the encoded proteins. Comparing the individuals with functional rare variants to those without such variants showed associations with lipid phenotypes. Our findings suggest that, while nonsynonymous GCKR variants, excluding p.Pro446Leu, are rare in individuals of mixed European descent, the majority do affect protein function. In sum, this study utilizes computational, cell biological, and biochemical methods to present a model for interpreting the clinical significance of rare genetic variants in common disease. Hide abstract
Metformin is the most commonly used pharmacological therapy for type 2 diabetes. We report a genome-wide association study for glycemic response to metformin in 1,024 Scottish individuals with type 2 diabetes with replication in two cohorts including 1,783 Scottish individuals and 1,113 individuals from the UK Prospective Diabetes Study. In a combined meta-analysis, we identified a SNP, rs11212617, associated with treatment success (n = 3,920, P = 2.9 × 10(-9), odds ratio = 1.35, 95% CI 1.22-1.49) at a locus containing ATM, the ataxia telangiectasia mutated gene. In a rat hepatoma cell line, inhibition of ATM with KU-55933 attenuated the phosphorylation and activation of AMP-activated protein kinase in response to metformin. We conclude that ATM, a gene known to be involved in DNA repair and cell cycle control, plays a role in the effect of metformin upstream of AMP-activated protein kinase, and variation in this gene alters glycemic response to metformin. Hide abstract
Nature Genetics,2011. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci
Genome-wide association studies have identified many genetic variants associated with complex traits. However, at only a minority of loci have the molecular mechanisms mediating these associations been characterized. In parallel, whereas cis regulatory patterns of gene expression have been extensively explored, the identification of trans regulatory effects in humans has attracted less attention. Here we show that the type 2 diabetes and high-density lipoprotein cholesterol-associated cis-acting expression quantitative trait locus (eQTL) of the maternally expressed transcription factor KLF14 acts as a master trans regulator of adipose gene expression. Expression levels of genes regulated by this trans-eQTL are highly correlated with concurrently measured metabolic traits, and a subset of the trans-regulated genes harbor variants directly associated with metabolic phenotypes. This trans-eQTL network provides a mechanistic understanding of the effect of the KLF14 locus on metabolic disease risk and offers a potential model for other complex traits. Hide abstract
N Engl J Med, 363 (24), pp. 2339-2350. | Read more2010. Genomics, type 2 diabetes, and obesity.
Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways. Hide abstract
Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation. Hide abstract
OBJECTIVE: Despite the clinical importance of an accurate diagnosis in individuals with monogenic forms of diabetes, restricted access to genetic testing leaves many patients with undiagnosed diabetes. Recently, common variation near the HNF1 homeobox A (HNF1A) gene was shown to influence C-reactive protein levels in healthy adults. We hypothesized that serum levels of high-sensitivity C-reactive protein (hs-CRP) could represent a clinically useful biomarker for the identification of HNF1A mutations causing maturity-onset diabetes of the young (MODY). RESEARCH DESIGN AND METHODS: Serum hs-CRP was measured in subjects with HNF1A-MODY (n = 31), autoimmune diabetes (n = 316), type 2 diabetes (n = 240), and glucokinase (GCK) MODY (n = 24) and in nondiabetic individuals (n = 198). The discriminative accuracy of hs-CRP was evaluated through receiver operating characteristic (ROC) curve analysis, and performance was compared with standard diagnostic criteria. Our primary analyses excluded approximately 11% of subjects in whom the single available hs-CRP measurement was >10 mg/l. RESULTS: Geometric mean (SD range) hs-CRP levels were significantly lower (P <or= 0.009) for HNF1A-MODY individuals, 0.20 (0.03-1.14) mg/l, than for any other group: autoimmune diabetes 0.58 (0.10-2.75) mg/l, type 2 diabetes 1.33 (0.28-6.14) mg/l, GCK-MODY 1.01 (0.19-5.33) mg/l, and nondiabetic 0.48 (0.10-2.42) mg/l. The ROC-derived C-statistic for discriminating HNF1A-MODY and type 2 diabetes was 0.8. Measurement of hs-CRP, either alone or in combination with current diagnostic criteria, was superior to current diagnostic criteria alone. Sensitivity and specificity for the combined criteria approached 80%. CONCLUSIONS: Serum hs-CRP levels are markedly lower in HNF1A-MODY than in other forms of diabetes. hs-CRP has potential as a widely available, cost-effective screening test to support more precise targeting of MODY diagnostic testing. Hide abstract
Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD. Hide abstract
By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P 5 × 10 8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits. © 2010 Nature America, Inc. All rights reserved. Hide abstract
To identify genetic variants associated with birth weight, we meta-analyzed six genome-wide association (GWA) studies (n = 10,623 Europeans from pregnancy/birth cohorts) and followed up two lead signals in 13 replication studies (n = 27,591). rs900400 near LEKR1 and CCNL1 (P = 2 x 10(-35)) and rs9883204 in ADCY5 (P = 7 x 10(-15)) were robustly associated with birth weight. Correlated SNPs in ADCY5 were recently implicated in regulation of glucose levels and susceptibility to type 2 diabetes, providing evidence that the well-described association between lower birth weight and subsequent type 2 diabetes has a genetic component, distinct from the proposed role of programming by maternal nutrition. Using data from both SNPs, we found that the 9% of Europeans carrying four birth weight-lowering alleles were, on average, 113 g (95% CI 89-137 g) lighter at birth than the 24% with zero or one alleles (P(trend) = 7 x 10(-30)). The impact on birth weight is similar to that of a mother smoking 4-5 cigarettes per day in the third trimester of pregnancy. Hide abstract
Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed approximately 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated approximately 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases. Hide abstract
Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958-30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, beta (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 x 10(-15)). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 x 10(-17); ratio of insulin to glucose area under the curve, P = 1.3 x 10(-16)) and diminished incretin effect (n = 804; P = 4.3 x 10(-4)). We also identified variants at ADCY5 (rs2877716, P = 4.2 x 10(-16)), VPS13C (rs17271305, P = 4.1 x 10(-8)), GCKR (rs1260326, P = 7.1 x 10(-11)) and TCF7L2 (rs7903146, P = 4.2 x 10(-10)) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09-1.15, P = 4.8 x 10(-18)). Hide abstract
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes. Hide abstract
Nature Genetics,2010. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution
Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and approximately 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 x 10(-14)), CDC123-CAMK1D (P = 1.2 x 10(-10)), TSPAN8-LGR5 (P = 1.1 x 10(-9)), THADA (P = 1.1 x 10(-9)), ADAMTS9 (P = 1.2 x 10(-8)) and NOTCH2 (P = 4.1 x 10(-8)) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D. Hide abstract
There is increasing evidence that genome-wide association (GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study (using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined approximately 2,000 individuals for each of 7 major diseases and a shared set of approximately 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 x 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals (including 58 loci with single-point P values between 10(-5) and 5 x 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research. Hide abstract
The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1924 diabetic cases and 2938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3757 additional cases and 5346 controls and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B, and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insight into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes. Hide abstract
Obesity is a serious international health problem that increases the risk of several common diseases. The genetic factors predisposing to obesity are poorly understood. A genome-wide search for type 2 diabetes-susceptibility genes identified a common variant in the FTO (fat mass and obesity associated) gene that predisposes to diabetes through an effect on body mass index (BMI). An additive association of the variant with BMI was replicated in 13 cohorts with 38,759 participants. The 16% of adults who are homozygous for the risk allele weighed about 3 kilograms more and had 1.67-fold increased odds of obesity when compared with those not inheriting a risk allele. This association was observed from age 7 years upward and reflects a specific increase in fat mass. Hide abstract
Defining novel biomarkers and therapeutic targets for type 2 diabetes and obesity through genetic analysis in large-scale human data sets.
Background: T2D and obesity are major contributors to ill health globally. There is a critical need for novel preventative and therapeutic strategies against these diseases supported by robust stratification of individual risk and disease subtype. Progress to date has been hampered by poor understanding of the mechanisms responsible for disease development and progression. Human genetics provides a powerful means to highlight pathways of relevance to human disease, and there has been a ...
Finding pathways central to the pathogenesis of T2D through the integration of genetic and genomic data.
Background:Novel therapeutic strategies need to be informed by a more complete understanding of the molecular and physiological basis of type 2 diabetes, designed to deliver validated interventional targets and biomarkers that can be used to define disease risk, progression, and subtype. We are using human genetics to deliver this understanding, and, working as part of large global consortia have interrogated several large genetic resources (~800,000 samples with GWAS and exome array data; ...
Genome Editing – manipulation of stem cells by CRISPR nucleases
Site specific modification of the genome represents a powerful tool to investigate the functional consequences of DNA sequence mutation and variation. Techniques, which enable specific nucleotides to be precisely modified, facilitate the generation of disease models harbouring pathogenic human mutations. These models can be used to explore the biological underlying the disease process and may also serve as a resource for therapeutic approaches. Genome Wide Association Studies (GWAS) and ...