Eleftheria Zeggini
Wellcome Trust Sanger Institute
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Featured researches published by Eleftheria Zeggini.
Science | 2007
Eleftheria Zeggini; Michael N. Weedon; Cecilia M. Lindgren; Timothy M. Frayling; Katherine S. Elliott; Hana Lango; Nicholas J. Timpson; John Perry; Nigel W. Rayner; Rachel M. Freathy; Jeffrey C. Barrett; Beverley M. Shields; Andrew P. Morris; Sian Ellard; Christopher J. Groves; Lorna W. Harries; Jonathan Marchini; Katharine R. Owen; Beatrice Knight; Lon R. Cardon; M. Walker; Graham A. Hitman; Andrew D. Morris; Alex S. F. Doney; Mark I. McCarthy; Andrew T. Hattersley
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.
Nature Genetics | 2008
Eleftheria Zeggini; Laura J. Scott; Richa Saxena; Benjamin F. Voight; Jonathan Marchini; Tianle Hu; Paul I. W. de Bakker; Gonçalo R. Abecasis; Peter Almgren; Gitte Andersen; Kristin Ardlie; Kristina Bengtsson Boström; Richard N. Bergman; Lori L. Bonnycastle; Knut Borch-Johnsen; Noël P. Burtt; Hong Chen; Peter S. Chines; Mark J. Daly; Parimal Deodhar; Chia-Jen Ding; Alex S. F. Doney; William L. Duren; Katherine S. Elliott; Michael R. Erdos; Timothy M. Frayling; Rachel M. Freathy; Lauren Gianniny; Harald Grallert; Niels Grarup
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 ∼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 × 10−14), CDC123-CAMK1D (P = 1.2 × 10−10), TSPAN8-LGR5 (P = 1.1 × 10−9), THADA (P = 1.1 × 10−9), ADAMTS9 (P = 1.2 × 10−8) and NOTCH2 (P = 4.1 × 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.
Genetic Epidemiology | 2010
Andrew P. Morris; Eleftheria Zeggini
Genome‐wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large‐scale re‐sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population‐based genetic studies when data are available from re‐sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re‐sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010.
Nature Genetics | 2007
Michael N. Weedon; Guillaume Lettre; Rachel M. Freathy; Cecilia M. Lindgren; Benjamin F. Voight; John Perry; Katherine S. Elliott; Rachel Hackett; Candace Guiducci; Beverley M. Shields; Eleftheria Zeggini; Hana Lango; Valeriya Lyssenko; Nicholas J. Timpson; Noël P. Burtt; Nigel W. Rayner; Richa Saxena; Kristin Ardlie; Jonathan H Tobias; Andy R Ness; Susan M. Ring; Colin N. A. Palmer; Andrew D. Morris; Leena Peltonen; Veikko Salomaa; George Davey Smith; Leif Groop; Andrew T. Hattersley; Mark I. McCarthy; Joel N. Hirschhorn
Human height is a classic, highly heritable quantitative trait. To begin to identify genetic variants influencing height, we examined genome-wide association data from 4,921 individuals. Common variants in the HMGA2 oncogene, exemplified by rs1042725, were associated with height (P = 4 × 10−8). HMGA2 is also a strong biological candidate for height, as rare, severe mutations in this gene alter body size in mice and humans, so we tested rs1042725 in additional samples. We confirmed the association in 19,064 adults from four further studies (P = 3 × 10−11, overall P = 4 × 10−16, including the genome-wide association data). We also observed the association in children (P = 1 × 10−6, N = 6,827) and a tall/short case-control study (P = 4 × 10−6, N = 3,207). We estimate that rs1042725 explains ∼0.3% of population variation in height (∼0.4 cm increased adult height per C allele). There are few examples of common genetic variants reproducibly associated with human quantitativetraits; these results represent, to our knowledge, the first consistently replicated association with adult and childhood height.
Diabetes | 2008
Rachel M. Freathy; Nicholas J. Timpson; Debbie A. Lawlor; Anneli Pouta; Yoav Ben-Shlomo; Aimo Ruokonen; Shah Ebrahim; Beverley M. Shields; Eleftheria Zeggini; Michael N. Weedon; Cecilia M. Lindgren; Hana Lango; David Melzer; Luigi Ferrucci; Giuseppe Paolisso; Matthew J. Neville; Fredrik Karpe; Colin N. A. Palmer; Andrew D. Morris; Paul Elliott; Marjo-Riitta Järvelin; George Davey Smith; Mark McCarthy; Andrew T. Hattersley; Timothy M. Frayling
OBJECTIVE—Common variation in the FTO gene is associated with BMI and type 2 diabetes. Increased BMI is associated with diabetes risk factors, including raised insulin, glucose, and triglycerides. We aimed to test whether FTO genotype is associated with variation in these metabolic traits. RESEARCH DESIGN AND METHODS—We tested the association between FTO genotype and 10 metabolic traits using data from 17,037 white European individuals. We compared the observed effect of FTO genotype on each trait to that expected given the FTO-BMI and BMI-trait associations. RESULTS—Each copy of the FTO rs9939609 A allele was associated with higher fasting insulin (0.039 SD [95% CI 0.013–0.064]; P = 0.003), glucose (0.024 [0.001–0.048]; P = 0.044), and triglycerides (0.028 [0.003–0.052]; P = 0.025) and lower HDL cholesterol (0.032 [0.008–0.057]; P = 0.009). There was no evidence of these associations when adjusting for BMI. Associations with fasting alanine aminotransferase, γ-glutamyl-transferase, LDL cholesterol, A1C, and systolic and diastolic blood pressure were in the expected direction but did not reach P < 0.05. For all metabolic traits, effect sizes were consistent with those expected for the per allele change in BMI. FTO genotype was associated with a higher odds of metabolic syndrome (odds ratio 1.17 [95% CI 1.10–1.25]; P = 3 × 10−6). CONCLUSIONS—FTO genotype is associated with metabolic traits to an extent entirely consistent with its effect on BMI. Sample sizes of >12,000 individuals were needed to detect associations at P < 0.05. Our findings highlight the importance of using appropriately powered studies to assess the effects of a known diabetes or obesity variant on secondary traits correlated with these conditions.
Diabetes | 2008
Hana Lango; Colin N. A. Palmer; Andrew D. Morris; Eleftheria Zeggini; Andrew T. Hattersley; Mark McCarthy; Timothy M. Frayling; Michael N. Weedon
OBJECTIVES—Genome-wide association studies have dramatically increased the number of common genetic variants that are robustly associated with type 2 diabetes. A possible clinical use of this information is to identify individuals at high risk of developing the disease, so that preventative measures may be more effectively targeted. Here, we assess the ability of 18 confirmed type 2 diabetes variants to differentiate between type 2 diabetic case and control subjects. RESEARCH DESIGN AND METHODS—We assessed index single nucleotide polymorphisms (SNPs) for the 18 independent loci in 2,598 control subjects and 2,309 case subjects from the Genetics of Diabetes Audit and Research Tayside Study. The discriminatory ability of the combined SNP information was assessed by grouping individuals based on number of risk alleles carried and determining relative odds of type 2 diabetes and by calculating the area under the receiver-operator characteristic curve (AUC). RESULTS—Individuals carrying more risk alleles had a higher risk of type 2 diabetes. For example, 1.2% of individuals with >24 risk alleles had an odds ratio of 4.2 (95% CI 2.11–8.56) against the 1.8% with 10–12 risk alleles. The AUC (a measure of discriminative accuracy) for these variants was 0.60. The AUC for age, BMI, and sex was 0.78, and adding the genetic risk variants only marginally increased this to 0.80. CONCLUSIONS—Currently, common risk variants for type 2 diabetes do not provide strong predictive value at a population level. However, the joint effect of risk variants identified subgroups of the population at substantially different risk of disease. Further studies are needed to assess whether individuals with extreme numbers of risk alleles may benefit from genetic testing.
Diabetes | 2006
Christopher J. Groves; Eleftheria Zeggini; Jayne Minton; Timothy M. Frayling; Michael N. Weedon; N W Rayner; Graham A. Hitman; M. Walker; Steven Wiltshire; Andrew T. Hattersley; Mark I. McCarthy
Recent data suggest that common variation in the transcription factor 7-like 2 (TCF7L2) gene is associated with type 2 diabetes. Evaluation of such associations in independent samples provides necessary replication and a robust assessment of effect size. Using four TCF7L2 single nucleotide polymorphisms (SNPs; including the two most associated in the previous study), we conducted a case-control study in 2,158 type 2 diabetic subjects and 2,574 control subjects and a family-based association analysis in 388 parent-offspring trios all from the U.K. All SNPs showed powerful associations with diabetes in the case-control analysis, with strongest effects at rs7903146 (allele-wise relative risk 1.36 [95% CI 1.24–1.48], P = 1.3 × 10−11). Data were consistent with a multiplicative model. The family-based analyses provided independent evidence for association at all loci (e.g., rs4506565, 62% transmission, P = 7 × 10−5) with no parent-of-origin effects. The frequency of diabetes-associated TCF7L2 genotypes was greater in cases ascertained for positive family history and early onset (rs4606565, P = 0.02); the population-attributable risk, estimated from the least-selected cases, is ∼16%. The overall evidence for association for these variants (P = 4.4 × 10−14 combining case-control and family-based analyses for rs4506565) exceeds genome-wide significance criteria and clearly establishes TCF7L2 as a type 2 diabetes susceptibility gene of substantial importance.
The Lancet | 2012
Eleftheria Zeggini; Kalliope Panoutsopoulou; Lorraine Southam; N W Rayner; Aaron G. Day-Williams; M C Lopes; Vesna Boraska; T. Esko; Evangelos Evangelou; A Hoffman; Jeanine J. Houwing-Duistermaat; Thorvaldur Ingvarsson; Ingileif Jonsdottir; H Jonnson; Hanneke J. M. Kerkhof; Margreet Kloppenburg; S.D. Bos; Massimo Mangino; Sarah Metrustry; P E Slagboom; Gudmar Thorleifsson; Raine Eva.; Madhushika Ratnayake; M Ricketts; Claude Beazley; Hannah Blackburn; Suzannah Bumpstead; K S Elliott; Sarah Hunt; Simon Potter
Summary Background Osteoarthritis is the most common form of arthritis worldwide and is a major cause of pain and disability in elderly people. The health economic burden of osteoarthritis is increasing commensurate with obesity prevalence and longevity. Osteoarthritis has a strong genetic component but the success of previous genetic studies has been restricted due to insufficient sample sizes and phenotype heterogeneity. Methods We undertook a large genome-wide association study (GWAS) in 7410 unrelated and retrospectively and prospectively selected patients with severe osteoarthritis in the arcOGEN study, 80% of whom had undergone total joint replacement, and 11 009 unrelated controls from the UK. We replicated the most promising signals in an independent set of up to 7473 cases and 42 938 controls, from studies in Iceland, Estonia, the Netherlands, and the UK. All patients and controls were of European descent. Findings We identified five genome-wide significant loci (binomial test p≤5·0×10−8) for association with osteoarthritis and three loci just below this threshold. The strongest association was on chromosome 3 with rs6976 (odds ratio 1·12 [95% CI 1·08–1·16]; p=7·24×10−11), which is in perfect linkage disequilibrium with rs11177. This SNP encodes a missense polymorphism within the nucleostemin-encoding gene GNL3. Levels of nucleostemin were raised in chondrocytes from patients with osteoarthritis in functional studies. Other significant loci were on chromosome 9 close to ASTN2, chromosome 6 between FILIP1 and SENP6, chromosome 12 close to KLHDC5 and PTHLH, and in another region of chromosome 12 close to CHST11. One of the signals close to genome-wide significance was within the FTO gene, which is involved in regulation of bodyweight—a strong risk factor for osteoarthritis. All risk variants were common in frequency and exerted small effects. Interpretation Our findings provide insight into the genetics of arthritis and identify new pathways that might be amenable to future therapeutic intervention. Funding arcOGEN was funded by a special purpose grant from Arthritis Research UK.
American Journal of Human Genetics | 2004
Sally John; Neil Shephard; Guoying Liu; Eleftheria Zeggini; Manqiu Cao; Wenwei Chen; Nisha Vasavda; Tracy Mills; Anne Barton; Anne Hinks; Steve Eyre; Keith W. Jones; William Ollier; A J Silman; Neil James Gibson; Jane Worthington; Giulia C. Kennedy
Despite the theoretical evidence of the utility of single-nucleotide polymorphisms (SNPs) for linkage analysis, no whole-genome scans of a complex disease have yet been published to directly compare SNPs with microsatellites. Here, we describe a whole-genome screen of 157 families with multiple cases of rheumatoid arthritis (RA), performed using 11,245 genomewide SNPs. The results were compared with those from a 10-cM microsatellite scan in the same cohort. The SNP analysis detected HLA*DRB1, the major RA susceptibility locus (P=.00004), with a linkage interval of 31 cM, compared with a 50-cM linkage interval detected by the microsatellite scan. In addition, four loci were detected at a nominal significance level (P<.05) in the SNP linkage analysis; these were not observed in the microsatellite scan. We demonstrate that variation in information content was the main factor contributing to observed differences in the two scans, with the SNPs providing significantly higher information content than the microsatellites. Reducing the number of SNPs in the marker set to 3,300 (1-cM spacing) caused several loci to drop below nominal significance levels, suggesting that decreases in information content can have significant effects on linkage results. In contrast, differences in maps employed in the analysis, the low detectable rate of genotyping error, and the presence of moderate linkage disequilibrium between markers did not significantly affect the results. We have demonstrated the utility of a dense SNP map for performing linkage analysis in a late-age-at-onset disease, where DNA from parents is not always available. The high SNP density allows loci to be defined more precisely and provides a partial scaffold for association studies, substantially reducing the resource requirement for gene-mapping studies.
Nature Methods | 2014
Graham R. S. Ritchie; Ian Dunham; Eleftheria Zeggini; Paul Flicek
Identifying functionally relevant variants against the background of ubiquitous genetic variation is a major challenge in human genetics. For variants in protein-coding regions, our understanding of the genetic code and splicing allows us to identify likely candidates, but interpreting variants outside genic regions is more difficult. Here we present genome-wide annotation of variants (GWAVA), a tool that supports prioritization of noncoding variants by integrating various genomic and epigenomic annotations.