Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Burton, Paul R.; Clayton, David; Cardon, Lon R.; Craddock, Nick; Deloukas, Panos; Duncanson, Audrey; Kwiatkowski, Dominic P.; McCarthy, Mark I.; Ouwehand, Willem H.; Samani, Nilesh J.; Todd, John A.; Donnelly, Peter; Barrett, Jeffrey C.; Davison, Dan; Easton, Doug; Evans, David M.; Leung, H.T.; Marchini, Jonathan; Morris, Andrew P.; Spencer, Chris C. A.; Tobin, Martin D.; Attwood, Antony; Boorman, James P.; Cant, Barbara; Everson, Ursula; Hussey, Judith M.; Jolley, Jennifer; Knight, Alexandra S.; Koch, Kerstin; Meech, Elizabeth; Nutland, Sarah; Prowse, Christopher V.; Stevens, Helen; Taylor, Niall; Walters, G. R.; Walker, Neil; Watkins, Nicholas A.; Winzer, Thilo; Jones, Richard W.; McArdle, Wendy L.; Ring, Susan M.; Strachan, David P.; Pembrey, Marcus; Breen, Gerome; Clair, David St; Caesar, Sian; Gordon‐Smith, Katherine; Jones, Lisa; Fraser, Christine; Green, Elaine K.; Grozeva, Detelina; Hamshere, Marian L.; Holmans, Peter Alan; Jones, Ian; Kirov, George; Moskvina, Valentina; Nikolov, Ivan; O'Donovan, Michael Conlon; Owen, Michael John; Collier, David; Elkin, Amanda; Farmer, Anne; Williamson, Richard; McGuffin, Peter; Young, Allan H.; Ferrier, I. Nicol; Ball, Stephen G.; Balmforth, Anthony J.; Barrett, Jennifer H.; Bishop, D. Timothy; Iles, Mark M.; Maqbool, Azhar; Yuldasheva, Nadira; Hall, Alistair S.; Braund, Peter S.; Dixon, Richard J.; Mangino, Massimo; Stevens, Suzanne; Thompson, John R.; Bredin, Francesca; Tremelling, Mark; Parkes, Miles; Drummond, Hazel E.; Lees, Charlie W; Nimmo, Elaine R.; Satsangi, Jack; Fisher, Sheila; Forbes, Alastair; Lewis, Cathryn M.; Onnie, Clive M.; Prescott, Natalie J.; Sanderson, Jeremy; Mathew, Christopher G.; Barbour, Jamie; Mohiuddin, Mohamed Khalid; Todhunter, Catherine E; Mansfield, John C.; Ahmad, Tariq; Cummings, Fraser; Jewell, D P; Webster, J; Brown, Morris J.; Lathrop, G.M.; Connell, John; Dominiczak, Anna F.; Marcano, Carolina A. Braga; Burke, Beverley; Dobson, Richard; Gungadoo, Johannie; Lee, Kate; Munroe, Patricia B.; Newhouse, Stephen; Onipinla, Abiodun; Wallace, Chris; Xue, Mingzhan; Caulfield, Mark; Farrall, Martin; Barton, Anne; Bruce, Ian N.; Donovan, Hannah; Eyre, Steve; Gilbert, Paul D.; Hider, Samantha L.; Hinks, Anne; John, Sally; Potter, Catherine; Silman, Alan J.; Symmons, Deborah; Thomson, Wendy; Worthington, Jane; Dunger, David B.; Widmer, Barry; Frayling, Timothy M.; Freathy, Rachel M.; Lango, Hana; Perry, John; Shields, Beverley M.; Weedon, Michael N.; Hattersley, Andrew T.; Hitman, G. A.; Walker, Mark; Elliott, Kate; Groves, Christopher J.; Lindgren, Cecilia M.; Rayner, Nigel W.; Timpson, Nicholas J.; Zeggini, Eleftheria; Newport, Melanie J.; Sirugo, Giorgio; Lyons, Emily; Vannberg, Fredrik; Hill, Adrian V. S.; Bradbury, L.; Farrar, C; Pointon, Jennifer J.; Wordsworth, P; Brown, Matthew A.; Franklyn, Jayne A.; Heward, J. M.; Simmonds, Matthew J.; Gough, Stephen; Seal, Sheila; Stratton, Michael R.; Rahman, Nazneen; Ban, Masashi; Goris, An; Sawcer, Stephen; Compston, Alastair; Conway, David J.; Jallow, Muminatou; Rockett, Kirk A.; Bumpstead, Suzannah; Chaney, Amy; Downes, Kate; Ghori, Mohammed J. R.; Gwilliam, Rhian; Hunt, Sarah; Inouye, Michael; Keniry, Andrew; King, Emma; McGinnis, Ralph; Potter, Simon; Ravindrarajah, Rathi; Whittaker, Pamela; Widden, Claire; Withers, David R.; Cardin, Niall J.; Ferreira, Teresa; Pereira-Gale, Joanne; Hallgrímsdóttir, Ingileif B.; Howie, Bryan; Su, Zhan; Teo, Yik Ying; Vukcevic, Damjan; Bentley, David; Compston, A

doi:10.1038/nature05911

Cited by 8,480 publications

(4,331 citation statements)

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“…Shared control samples from Sweden (the Epidemiological Investigation of Rheumatoid Arthritis study), Spain, and The Netherlands were provided by the Rheumatoid Arthritis Consortium International 15, with control samples from the UK provided by the Wellcome Trust Case Control Consortium 16. Control samples from Italy, Norway, Belgium, and France were provided by the International Multiple Sclerosis Genetics Consortium 17.…”

Section: Methodsmentioning

confidence: 99%

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Rothwell

Cooper

Lundberg

et al. 2017

Arthritis & Rheumatology

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ObjectiveInclusion body myositis (IBM) is characterized by a combination of inflammatory and degenerative changes affecting muscle. While the primary cause of IBM is unknown, genetic factors may influence disease susceptibility. To determine genetic factors contributing to the etiology of IBM, we conducted the largest genetic association study of the disease to date, investigating immune‐related genes using the Immunochip.MethodsA total of 252 Caucasian patients with IBM were recruited from 11 countries through the Myositis Genetics Consortium and compared with 1,008 ethnically matched controls. Classic HLA alleles and amino acids were imputed using SNP2HLA.ResultsThe HLA region was confirmed as the most strongly associated region in IBM (P = 3.58 × 10−33). HLA imputation identified 3 independent associations (with HLA–DRB1*03:01, DRB1*01:01, and DRB1*13:01), although the strongest association was with amino acid positions 26 and 11 of the HLA–DRB1 molecule. No association with anti–cytosolic 5′‐nucleotidase 1A–positive status was found independent of HLA–DRB1*03:01. There was no association of HLA genotypes with age at onset of IBM. Three non‐HLA regions reached suggestive significance, including the chromosome 3 p21.31 region, an established risk locus for autoimmune disease, where a frameshift mutation in CCR5 is thought to be the causal variant.ConclusionThis is the largest, most comprehensive genetic association study to date in IBM. The data confirm that HLA is the most strongly associated region and identifies novel amino acid associations that may explain the risk in this locus. These amino acid associations differentiate IBM from polymyositis and dermatomyositis and may determine properties of the peptide‐binding groove, allowing it to preferentially bind autoantigenic peptides. A novel suggestive association within the chromosome 3 p21.31 region suggests a role for CCR5.

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Section: Methodsmentioning

confidence: 99%

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Rothwell

Cooper

Lundberg

et al. 2017

Arthritis & Rheumatology

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“…It has previously been reported that there is very little population stratification within the UK 27, and when we compared the MAFs of the 15 SNPs of interest between the WTCCC2 and arcOGEN samples, only 1 SNP, rs4736558 on chromosome 8, showed any difference in MAF (uncorrected P = 0.010) (Table 4), and that SNP was removed. Therefore, we concluded that the WTCCC2 and arcOGEN samples could be merged and used together as controls.…”

Section: Resultsmentioning

confidence: 99%

Replication of Associations of Genetic Loci Outside the HLA Region With Susceptibility to Anti–Cyclic Citrullinated Peptide–Negative Rheumatoid Arthritis

Massey

Duffus

Eyre

et al. 2016

Arthritis & Rheumatology

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ObjectiveGenetic polymorphisms within the HLA region explain only a modest proportion of anti–cyclic citrullinated peptide (anti‐CCP)–negative rheumatoid arthritis (RA) heritability. However, few non‐HLA markers have been identified so far. This study was undertaken to replicate the associations of anti‐CCP–negative RA with non‐HLA genetic polymorphisms demonstrated in a previous study.Methods The Rheumatoid Arthritis Consortium International densely genotyped 186 autoimmune‐related regions in 3,339 anti‐CCP–negative RA patients and 15,870 controls across 6 different populations using the Illumina ImmunoChip array. We performed a case–control replication study of the anti‐CCP–negative markers with the strongest associations in that discovery study, in an independent cohort of anti‐CCP–negative UK RA patients. Individuals from the arcOGEN Consortium and Wellcome Trust Case Control Consortium were used as controls. Genotyping in cases was performed using Sequenom MassArray technology. Genome‐wide data from controls were imputed using the 1000 Genomes Phase I integrated variant call set release version 3 as a reference panel. Results After genotyping and imputation quality control procedures, data were available for 15 non‐HLA single‐nucleotide polymorphisms in 1,024 cases and 6,348 controls. We confirmed the known markers ANKRD55 (meta‐analysis odds ratio [OR] 0.80; P = 2.8 × 10−13) and BLK (OR 1.13; P = 7.0 × 10−6) and identified new and specific markers of anti‐CCP–negative RA (prolactin [PRL] [OR 1.13; P = 2.1 × 10−6] and NFIA [OR 0.85; P = 2.5 × 10−6]). Neither of these loci is associated with other common, complex autoimmune diseases. Conclusion Anti‐CCP–negative RA and anti‐CCP–positive RA are genetically different disease subsets that only partially share susceptibility factors. Genetic polymorphisms located near the PRL and NFIA genes represent examples of genetic susceptibility factors specific for anti‐CCP–negative RA.

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“…The WTCCC [3] GWAS dataset contains 500,567 SNP markers from 1,999 T2D cases and 3,004 controls. Quality control (QC) is applied for single SNP association analysis.…”

Section: Methodsmentioning

confidence: 99%

“…To rank SNPs and find SNP combinations, various methods are applied: Bayes factors [3], logistic regression [4,5], Hidden Markov Model (HMM) [6], Support Vector Machine (SVM), [7,8] and Random Forests (RF) [8-12]. Among the applied standard statistical methods and the machine learning-based methods, RF effectively ranks causal SNPs to detect SNP interactions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

Kang

2013

BMC Med Inform Decis Mak

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BackgroundDue to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity.MethodsWe aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA.ResultsA T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration.ConclusionsWe propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.

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Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Cited by 8,480 publications

References 144 publications

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Immune‐Array Analysis in Sporadic Inclusion Body Myositis Reveals HLA–DRB1 Amino Acid Heterogeneity Across the Myositis Spectrum

Replication of Associations of Genetic Loci Outside the HLA Region With Susceptibility to Anti–Cyclic Citrullinated Peptide–Negative Rheumatoid Arthritis

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

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