Dataset Information

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.

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.

SUBMITTER: Wellcome Trust Case Control Consortium

PROVIDER: S-EPMC2892339 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.

Craddock Nick N Hurles Matthew E ME Cardin Niall N Pearson Richard D RD Plagnol Vincent V Robson Samuel S Vukcevic Damjan D Barnes Chris C Conrad Donald F DF Giannoulatou Eleni E Holmes Chris C Marchini Jonathan L JL Stirrups Kathy K Tobin Martin D MD Wain Louise V LV Yau Chris C Aerts Jan J Ahmad Tariq T Andrews T Daniel TD Arbury Hazel H Attwood Anthony A Auton Adam A Ball Stephen G SG Balmforth Anthony J AJ Barrett Jeffrey C JC Barroso Inês I Barton Anne A Bennett Amanda J AJ Bhaskar Sanjeev S Blaszczyk Katarzyna K Bowes John J Brand Oliver J OJ Braund Peter S PS Bredin Francesca F Breen Gerome G Brown Morris J MJ Bruce Ian N IN Bull Jaswinder J Burren Oliver S OS Burton John J Byrnes Jake J Caesar Sian S Clee Chris M CM Coffey Alison J AJ Connell John M C JM Cooper Jason D JD Dominiczak Anna F AF Downes Kate K Drummond Hazel E HE Dudakia Darshna D Dunham Andrew A Ebbs Bernadette B Eccles Diana D Edkins Sarah S Edwards Cathryn C Elliot Anna A Emery Paul P Evans David M DM Evans Gareth G Eyre Steve S Farmer Anne A Ferrier I Nicol IN Feuk Lars L Fitzgerald Tomas T Flynn Edward E Forbes Alistair A Forty Liz L Franklyn Jayne A JA Freathy Rachel M RM Gibbs Polly P Gilbert Paul P Gokumen Omer O Gordon-Smith Katherine K Gray Emma E Green Elaine E Groves Chris J CJ Grozeva Detelina D Gwilliam Rhian R Hall Anita A Hammond Naomi N Hardy Matt M Harrison Pile P Hassanali Neelam N Hebaishi Husam H Hines Sarah S Hinks Anne A Hitman Graham A GA Hocking Lynne L Howard Eleanor E Howard Philip P Howson Joanna M M JM Hughes Debbie D Hunt Sarah S Isaacs John D JD Jain Mahim M Jewell Derek P DP Johnson Toby T Jolley Jennifer D JD Jones Ian R IR Jones Lisa A LA Kirov George G Langford Cordelia F CF Lango-Allen Hana H Lathrop G Mark GM Lee James J Lee Kate L KL Lees Charlie C Lewis Kevin K Lindgren Cecilia M CM Maisuria-Armer Meeta M Maller Julian J Mansfield John J Martin Paul P Massey Dunecan C O DC McArdle Wendy L WL McGuffin Peter P McLay Kirsten E KE Mentzer Alex A Mimmack Michael L ML Morgan Ann E AE Morris Andrew P AP Mowat Craig C Myers Simon S Newman William W Nimmo Elaine R ER O'Donovan Michael C MC Onipinla Abiodun A Onyiah Ifejinelo I Ovington Nigel R NR Owen Michael J MJ Palin Kimmo K Parnell Kirstie K Pernet David D Perry John R B JR Phillips Anne A Pinto Dalila D Prescott Natalie J NJ Prokopenko Inga I Quail Michael A MA Rafelt Suzanne S Rayner Nigel W NW Redon Richard R Reid David M DM Renwick Ring Susan M SM Robertson Neil N Russell Ellie E St Clair David D Sambrook Jennifer G JG Sanderson Jeremy D JD Schuilenburg Helen H Scott Carol E CE Scott Richard R Seal Sheila S Shaw-Hawkins Sue S Shields Beverley M BM Simmonds Matthew J MJ Smyth Debbie J DJ Somaskantharajah Elilan E Spanova Katarina K Steer Sophia S Stephens Jonathan J Stevens Helen E HE Stone Millicent A MA Su Zhan Z Symmons Deborah P M DP Thompson John R JR Thomson Wendy W Travers Mary E ME Turnbull Clare C Valsesia Armand A Walker Mark M Walker Neil M NM Wallace Chris C Warren-Perry Margaret M Watkins Nicholas A NA Webster John J Weedon Michael N MN Wilson Anthony G AG Woodburn Matthew M Wordsworth B Paul BP Young Allan H AH Zeggini Eleftheria E Carter Nigel P NP Frayling Timothy M TM Lee Charles C McVean Gil G Munroe Patricia B PB Palotie Aarno A Sawcer Stephen J SJ Scherer Stephen W SW Strachan David P DP Tyler-Smith Chris C Brown Matthew A MA Burton Paul R PR Caulfield Mark J MJ Compston Alastair A Farrall Martin M Gough Stephen C L SC Hall Alistair S AS Hattersley Andrew T AT Hill Adrian V S AV Mathew Christopher G CG Pembrey Marcus M Satsangi Jack J Stratton Michael R MR Worthington Jane J Deloukas Panos P Duncanson Audrey A Kwiatkowski Dominic P DP McCarthy Mark I MI Ouwehand Willem W Parkes Miles M Rahman Nazneen N Todd John A JA Samani Nilesh J NJ Donnelly Peter P

Nature 20100401 7289

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 CNV ...[more]

PMID: 20360734

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Project description: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.

Project description:Background Patients with rheumatoid arthritis (RA) have a 2- to 10-fold increased risk of cardiovascular disease (CVD), but the biological mechanisms and existence of causality underlying such associations remain to be investigated. We aimed to investigate the genetic associations and underlying mechanisms between RA and CVD by leveraging large-scale genomic data and genetic cross-trait analytic approaches. Methods and Results Within UK Biobank data, we examined the genetic correlation, shared genetics, and potential causality between RA (Ncases=6754, Ncontrols=452 384) and cardiovascular diseases (CVD, Ncases=44 238, Ncontrols=414 900) using linkage disequilibrium score regression, cross-trait meta-analysis, and Mendelian randomization. We observed significant genetic correlations of RA with myocardial infarction (rg:0.40 [95% CI, 0.24-0.56), angina (rg:0.42 [95% CI, 0.28-0.56]), coronary heart diseases (rg:0.41 [95% CI, 0.27-0.55]), and CVD (rg:0.43 [95% CI, 0.29-0.57]) after correcting for multiple testing (P<0.05/5). When stratified by frequent use of analgesics, we found increased genetic correlation between RA and CVD among participants without aspirin usage (rg:0.54 [95% CI, 0.30-0.78] for angina; Pvalue=6.69×10-6) and among participants with paracetamol usage (rg:0.75 [95% CI, 0.20-1.29] for myocardial infarction; Pvalue=8.90×10-3), whereas others remained similar. Cross-trait meta-analysis identified 9 independent shared loci between RA and CVD, including PTPN22 at chr1p13.2, BCL2L11 at chr2q13, and CCR3 at chr3p21.31 (Psingle trait<1×10-3 and Pmeta<5×10-8), highlighting potential shared pathogenesis including accelerating atherosclerosis, upregulating oxidative stress, and vascular permeability. Finally, Mendelian randomization estimates showed limited evidence of causality between RA and CVD. Conclusions Our results supported shared genetic pathogenesis rather than causality in explaining the observed association between RA and CVD. The identified shared genetic factors provided insights into potential novel therapeutic target for RA-CVD comorbidities.

Dataset Information

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.

Publications

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls.

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