Prof Mark McCarthy
| Research Area: | Genetics and Genomics |
|---|---|
| Technology Exchange: | Bioinformatics, Computational biology, SNP typing, Statistical genetics and Transcript profiling |
| 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: www.ocdem.ox.ac.uk) 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.
• Translating gene identification into biological insights and clinical advances.
• Genomic epidemiology.
• Statistical genetics and bioinformatics.
We are leading members of several international consortia in the field of complex trait genetics including the UK Type 2 diabetes genetics consortium, MolPAGE, WTCCC, ENGAGE, INTERACT, the International 1q consortium, EURODIA, DIAGRAM, GIANT and MAGIC. 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: during 2007 and 2008, our work was published in Science (two papers), Nature (one paper) and Nature Genetics (ten papers so far). Our major focus in the years ahead lies in expanding the spectrum of variation implicated in susceptibility to these and related conditions, and in translating this genetic information into advances in functional understanding and clinical management.
| Name | Institution | Country |
|---|---|---|
| Prof. Andrew Hattersley | Peninsula Medical School | UK |
| Dr. Tim Frayling | Peninsula Medical SChool | UK |
| Prof. Graham Hitman | Barts and the London | UK |
| Prof. Mark Walker | Univ of Newcastle | UK |
| Prof. Andrew Morris | Ninewells Medical School, Dundee | UK |
| Prof. Nick Wareham | Univ of Cambridge | UK |
| Dr. Panos Deloukas | WTSI | UK |
| Prof. Tim Spector | Kings College, London | UK |
| Prof. Leena Peltonen | Univ of Helsinki | FInland |
| Prof. Marjo Riitta Jarvelin | Imperial College/Univ of Oulu | UK/Finland |
| Prof. Leif Groop | Univ of Lund | Sweden |
| Dr. David Altshuler | Broad Institute of Harvard and MIT | USA |
| Dr. Alan Shuldiner | Univ of Maryland | USA |
| Dr. Steven Elbein | Univ of Arkansas Medical School | USA |
| Dr. Clifton Bogardus | NIDDK Phoenix | USA |
| Dr. Francis Collins | NHGRI, Bethesda, MD | USA |
| Dr. Michael Boehnke | U of Michigan | USA |
| Dr. Lincoln Stein | Ontario Cancer Institute | Canada |
| Prof. Philippe Froguel | Imperial College | UK |
| Prof. Juliana Chan | Chinese University of Hong Kong | Hong Kong SAR, China |
| Prof. Weiping Jia | Jiaotong University, Shanghai | China |
| Dr. Joel Hirschhorn | Broad Institute | USA |
| Dr. Eleftheria Zeggini | Wellcome Trust Sanger Institute | UK |
| Dr. Kari Stefansson | Decode Genetics | Iceland |
| Dr. Alvis Brazma | European Bioinformatics Institute | UK |
2007. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447 (7145), pp. 661-78. Read abstract | View on PubMed
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
2008. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nature genetics, 40 (5), pp. 638-45. Read abstract | View on PubMed
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
2008. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature reviews. Genetics, 9 (5), pp. 356-69. Read abstract | View on PubMed
The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management. Hide abstract
2008. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nature genetics, 40 (6), pp. 768-75. Read abstract | View on PubMed
To identify common variants influencing body mass index (BMI), we analyzed genome-wide association data from 16,876 individuals of European descent. After previously reported variants in FTO, the strongest association signal (rs17782313, P = 2.9 x 10(-6)) mapped 188 kb downstream of MC4R (melanocortin-4 receptor), mutations of which are the leading cause of monogenic severe childhood-onset obesity. We confirmed the BMI association in 60,352 adults (per-allele effect = 0.05 Z-score units; P = 2.8 x 10(-15)) and 5,988 children aged 7-11 (0.13 Z-score units; P = 1.5 x 10(-8)). In case-control analyses (n = 10,583), the odds for severe childhood obesity reached 1.30 (P = 8.0 x 10(-11)). Furthermore, we observed overtransmission of the risk allele to obese offspring in 660 families (P (pedigree disequilibrium test average; PDT-avg) = 2.4 x 10(-4)). The SNP location and patterns of phenotypic associations are consistent with effects mediated through altered MC4R function. Our findings establish that common variants near MC4R influence fat mass, weight and obesity risk at the population level and reinforce the need for large-scale data integration to identify variants influencing continuous biomedical traits. Hide abstract
2007. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science (New York, N.Y.), 316 (5829), pp. 1336-41. Read abstract | View on PubMed
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
2007. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science (New York, N.Y.), 316 (5826), pp. 889-94. Read abstract | View on PubMed
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
2008. Genome-wide association analysis identifies 20 loci that influence adult height. Nature genetics, 40 (5), pp. 575-83. Read abstract | View on PubMed
Adult height is a model polygenic trait, but there has been limited success in identifying the genes underlying its normal variation. To identify genetic variants influencing adult human height, we used genome-wide association data from 13,665 individuals and genotyped 39 variants in an additional 16,482 samples. We identified 20 variants associated with adult height (P < 5 x 10(-7), with 10 reaching P < 1 x 10(-10)). Combined, the 20 SNPs explain approximately 3% of height variation, with a approximately 5 cm difference between the 6.2% of people with 17 or fewer 'tall' alleles compared to the 5.5% with 27 or more 'tall' alleles. The loci we identified implicate genes in Hedgehog signaling (IHH, HHIP, PTCH1), extracellular matrix (EFEMP1, ADAMTSL3, ACAN) and cancer (CDK6, HMGA2, DLEU7) pathways, and provide new insights into human growth and developmental processes. Finally, our results provide insights into the genetic architecture of a classic quantitative trait. Hide abstract
2005. An evaluation of HapMap sample size and tagging SNP performance in large-scale empirical and simulated data sets. Nature genetics, 37 (12), pp. 1320-2. Read abstract | View on PubMed
A substantial investment has been made in the generation of large public resources designed to enable the identification of tag SNP sets, but data establishing the adequacy of the sample sizes used are limited. Using large-scale empirical and simulated data sets, we found that the sample sizes used in the HapMap project are sufficient to capture common variation, but that performance declines substantially for variants with minor allele frequencies of <5%. Hide abstract
Low frequency DNA variants and their contribution to individual predisposition to common diseases.
The recent wave of genome wide association studies has been hugely successful in defining common variants influencing many complex traits. The McCarthy group, with colleagues in Oxford and elsewhere, has played a leading role in such studies, with a particular emphasis on the characterisation of variants influencing risk of type 2 diabetes and obesity (see publications below). These studies have provided many novel insights into the biology of these traits, though the proportion of the genetic ...
