Follow-up of potential novel Graves' disease susceptibility loci, identified in the UK WTCCC genome-wide nonsynonymous SNP study

Abstract

A recent association scan using a genome-wide set of nonsynonymous coding single-nucleotide polymorphisms (nsSNPs) conducted in four diseases including Graves' disease (GD), identified nine novel possible regions of association with GD. We used a case–control approach in an attempt to replicate association of these nine regions in an independent collection of 1578 British GD patients and 1946 matched Caucasian controls. Although none of these loci showed evidence of association with GD in the independent data set, when combined with the original Wellcome Trust Case–Control Consortium study group, minor differences in allele frequencies (P⩾10⁻³) remained in the combined collection of 5924 subjects for four of the nsSNPs, present within HDLBP, TEKT1, JSRP1 and UTX. An additional 29 Tag SNPs were screened within these four gene regions to determine if further associations could be detected. Similarly, minor differences only (P=0.042–0.002) were detected in two HDLBP and two TEKT1 Tag SNPs in the combined UK GD collection. In conclusion, it is unlikely that the SNPs selected in this replication study have a significant effect on the risk of GD in the United Kingdom. Our study confirms the need for large data sets and stringent analysis criteria when searching for susceptibility loci in common diseases.

Introduction

Autoimmune thyroid diseases (AITD) including Graves' disease (GD) and Hashimoto's thyroiditis are common autoimmune diseases that develop as a result of environmental triggers in individuals with a genetic predisposition. Although a number of replicated genetic associations are emerging, providing insights into the underlying disease mechanisms, a significant component of the genetic contribution to AITD remains unknown.

In keeping with other common diseases, the identification of novel genetic variants conferring susceptibility has proved problematic with genome-wide linkage analysis generally proving disappointing in identifying loci for AITD.^{1, 2} Recent genome-wide association studies are, however, now beginning to reveal a number of novel genetic variants in many common diseases. The Wellcome Trust Case–Control Consortium (WTCCC) in the United Kingdom has recently completed two large association studies in 11 common diseases and reported a number of novel loci for most diseases.^{3, 4} In the smaller of the two studies, the WTCCC investigated 5500 individuals, which included 900 cases with GD, using a genome-wide set of 14 500 nonsynonymous coding single-nucleotide polymorphisms (nsSNPs). Although the strongest association signal was unsurprisingly identified in the human leukocyte antigen (HLA) region (P value<10⁻²⁰), association was also confirmed at the previously reported thyroid stimulating hormone receptor gene (TSHR)⁵ and Fc receptor-like 3 gene (FCRL3),^{4, 6} with a further nine novel regions showing some evidence of association with P value⩽10⁻⁴.⁴ The aim of this study was to try to replicate the association identified in the nine novel regions in an independent UK collection of GD subjects and controls.

Materials and methods

Subjects

A total cohort of 2478 unrelated Caucasian GD patients of UK origin were recruited into the AITD UK National Collection as previously described.⁷ Control samples totaling 3446 were obtained from the 1958 British Birth cohort (http://www.b58cgene.sgul.ac.uk). The WTCCC had previously genotyped 900 GD patients and 1500 control subjects from these data sets in the 14 500 nsSNPs association scan.⁴ In total, therefore, a further 1578 GD patients and 1946 controls, not previously included in the WTCCC nsSNP scan, were incorporated into this replication study. All subjects gave informed written consent and the project was approved by the local research ethics committee.

nsSNP genotyping

Nine novel nsSNP associations detected outside the HLA region that met a point-wise significance level of P⩽10⁻⁴ in the original WTCCC nsSNP scan were genotyped in an independent collection of 3524 samples (see Table 1 for a list of all SNPs investigated). The FCRL and TSHR regions were not examined as association with AITD had been previously reported.^{4, 5, 6} A further tenth SNP, rs3748140, present within the protein phosphatase 1 regulatory inhibitor Subunit 3B gene (PPPIR38) also produced a point-wise significance level of P⩽10⁻⁴ but was excluded from further analysis as it was non-polymorphic in the replication study.

Table 1. nsSNP genotyping of nine novel possible regions of association with GD in the original WTCCC collection (900GD; 1500CO), the independent replication collection (1578GD; 1946CO) and the complete collection (2478GD; 3446CO).

				WTCCC cohort					Independent replication cohort					Complete cohort (WTCCC and independent replication cohort)
Gene	nsSNP	Minor allele	Chromosome	Case MAF	Control MAF	OR	χ2	P-value	Case MAF	Control MAF	OR	χ2	P-value	Case MAF	Control MAF	OR	χ2	P-value
VWA5B1	rs10916769	G	1	0.17	0.21	0.76	12.10	5.0 × 10−4	0.20	0.19	1.07	1.20	0.273	0.19	0.20	0.95	1.32	0.250
MRPL53	rs1047911	A	2	0.15	0.12	1.34	11.24	8.0 × 10−4	0.13	0.14	0.92	1.25	0.263	0.14	0.13	1.10	2.98	0.085
HDLBP	rs7578199	C	2	0.26	0.22	1.26	11.53	6.9 × 10−4	0.25	0.24	1.01	0.06	0.815	0.25	0.23	1.11	5.48	0.019
ADRA1A	rs1048101	G	8	0.42	0.47	0.82	10.98	9.2 × 10−4	0.45	0.44	1.05	1.07	0.301	0.44	0.45	0.94	2.19	0.139
ZNF268	rs7975069	T	12	0.30	0.35	0.80	12.06	5.2 × 10−4	0.34	0.34	1.01	0.04	0.841	0.33	0.35	0.92	4.71	0.030
TEKT1	rs2271233	T	17	0.07	0.10	0.94	11.32	7.7 × 10−4	0.08	0.09	0.97	0.13	0.720	0.08	0.09	0.84	6.76	0.009
ADCYAP1	rs2856966	G	18	0.19	0.29	0.76	14.00	1.8 × 10−4	0.23	0.22	1.05	0.49	0.482	0.21	0.23	0.92	2.58	0.108
JSRP1	rs7250822	G	19	0.04	0.02	1.97	13.83	2.0 × 10−4	0.03	0.02	1.22	1.54	0.215	0.03	0.02	1.50	11.31	0.001
UTX	rs2230018	A	23	0.14	0.10	1.41	11.55	6.8 × 10−4	0.12	0.11	1.10	1.25	0.263	0.12	0.10	1.21	8.16	0.004

ADCYAP1, adenylate cyclase-activating polypeptide 1; ADRA1A, alpha-1A-adrenergic receptor; GD, Graves' disease; HDLBP, high-density lipoprotein-binding protein; JSRP1, junctional sarcoplasmic reticulum protein 1 (originally reported as the anti-Mullerian hormone gene); MAF, minor allele frequency; MRPL53, mitochondrial ribosomal protein L53; nsSNP, nonsynonymous single-nucleotide polymorphism; OR, odds ratio; TEKT1, Tektin 1; UTX, ubiquitously transcribed tetratricopeptide repeat gene on X chromosome; VWA5B1, Von Willebrand factor A domain containing 5B1; WTCCC, Wellcome Trust Case–Control Consortium; ZNF268, zinc-finger protein 268.

Minor alleles for all SNPs are reported on the forward strand and all ORs are generated from the MAF. Associations are highlighted in bold.

Tag SNP genotyping

To provide greater coverage in selected candidate genes in which nsSNPs showed some evidence for association in the combined WTCCC and replication data sets we also typed a number of Tag SNPs. A total of 29 Tag SNPs were selected (excluding the initially typed nsSNPs within each gene region) to take into account all remaining known common variation within HDLBP, TEKT1, JSRP1 and UTX (see Table 2 for details of Tag selection). These Tag SNPs were genotyped in the complete GD collection of 2478 samples. The control group, however, was reduced to 2690 to ensure geographical matching of cases and controls (756 controls originally used as part of the WTCCC were not screened) and referred to as the geographically matched complete group. Assays for all of the above SNPs were purchased from Applied Biosystems, Warrington, UK and genotyped on an ABI7900HT using Taqman (Applied Biosystems) genotyping technologies.

Table 2. Tag SNP genotyping of HDLBP, TEKT1, JSRP1 and UTX in a case–control collection of 2478 patients with GD and 2690 control subjects.

Gene	Tag SNPs	Case MAF	Control MAF	P-value	OR	95% CI
HDLBP	rs6437249	0.30	0.32	0.036	0.91	0.84–0.99
HDLBP	rs11680329	0.21	0.19	0.042	1.11	1.00–1.22
HDLBP	rs3771346	0.25	0.27	0.082	0.92	0.84–1.01
HDLBP	rs6724257	0.38	0.39	0.292	0.96	0.88–1.04
HDLBP	rs2305076	0.09	0.10	0.364	0.94	0.82 -1.07
HDLBP	rs6705421	0.46	0.45	0.411	1.03	0.95–1.12
HDLBP	rs15129	0.18	0.19	0.442	0.96	0.87–1.06
HDLBP	rs7559564	0.20	0.20	0.515	0.97	0.87–1.07
HDLBP	rs4675973	0.28	0.29	0.527	0.97	0.89–1.06
HDLBP	rs4675971	0.17	0.17	0.766	0.98	0.88–1.10
HDLBP	rs2289795	0.28	0.28	0.775	1.01	0.93–1.11
HDLBP	rs3755325	0.48	0.48	0.794	0.99	0.91–1.07
HDLBP	rs6757876	0.17	0.17	0.828	1.01	0.91–1.13
HDLBP	rs10185319	Failed HWE	Failed HWE	—	—	—
TEKT1	rs4796561	0.42	0.45	0.002	0.88	0.81–0.96
TEKT1	rs4796356	0.29	0.27	0.010	1.12	1.03–1.23
TEKT1	rs8078571	0.46	0.48	0.037	0.91	0.85–0.99
TEKT1	rs3744395	0.20	0.21	0.172	0.93	0.85–1.03
TEKT1	rs17804647	0.10	0.10	0.241	1.08	0.95–1.24
TEKT1	rs17731932	0.14	0.14	0.548	0.97	0.86–1.08
JSRP1	rs886363	0.19	0.20	0.053	0.91	0.82–1.00
JSRP1	rs3746158	0.33	0.32	0.682	1.02	0.94–1.11
UTX	rs9781530	0.07	0.06	0.308	1.09	0.92–1.32
UTX	rs5952647	0.19	0.18	0.321	1.06	0.94–1.19
UTX	rs6611063	0.19	0.19	0.353	1.03	0.92–1.22
UTX	rs17215160	0.12	0.12	0.894	1.07	0.93–1.22
UTX	rs6611065	Failed HWE	Failed HWE	—	—	—

CI, confidence intervals; GD, Graves' disease; HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; OR, odds ratio; SNP, single-nucleotide polymorphism. Tag SNP data for HDLBP, TEKT1, JSRP1 and UTX (phase 2, build 36, CEU population) was downloaded from the International Haplotype Mapping Project website (http://www.hapmap.org). Taking into account the initially genotyped SNPs, a further 29 Tag SNPs were chosen with a minimum r² of 0.80 and MAF⩾0.05 to capture the majority of common variation within these genes; including 14 for HDLBP, 8 for TEKT1 (although assays for two of these could not be designed for our genotyping platform and could not be screened), two for JSRP1 and five for UTX (see Supplementary Table 1 for SNPs captured by these 29 Tag SNPs).

All ORs are generated from the MAF. All associations are highlighted in bold.

Statistical analysis

All SNPs in HDLBP, TEKT and JSRP1 were in Hardy–Weinberg equilibrium (HWE) in cases and controls except rs10185319 (HDLBP) (control HWE P=0.02). As UTX was present on the X chromosome HWE was assessed in Haploview Version 3.2 (http://www.broad.mit.edu/mpg/haploview) and the rs6611065 SNP was shown to strongly deviate from HWE (P=8.64 × 10⁻¹⁵) and was excluded from further analysis. Allelic and genotypic analysis of case–control data was performed using the χ²-test within the MINITAB statistical package (MINITAB Release 15.1.2, © 1972–2007, Minitab Inc., State College, PA, USA). As UTX is located on the X chromosome these SNPs were analyzed within Haploview Version 3.2, which can analyze hemizygote males. Odds ratios (OR) with 95% confidence intervals (CI) were calculated by the method of Woolf with Haldane's modification for small numbers where appropriate.⁸ Using the ORs and minor allele frequencies (MAF) generated from the WTCCC nsSNP study, power calculations have shown that the replication collection (1578 GD and 1946 controls) had between 93 and 100% power to detect the size of effect reported from the original data set (900 GD and 1500 controls) for all loci, except TEKT1.

Results

nsSNP genotyping results in the independent and complete data sets

None of the nine novel candidate nsSNPs detected within the original WTCCC study were found to be associated with GD in the independent replication collection of 1578 GD cases and 1946 controls (P=0.215–0.841) (Table 1). In the combined collection (consisting of the WTCCC and the independent replication samples, totaling 2478 GD cases and 3446 controls) no significant differences in allele or genotype frequencies of the rs10916769 (VWA5B1), rs1047911 (MRPL53), rs1048101 (ADRA1A) and rs2856966 (ADCYAP1) SNPs were observed between GD cases and controls (see Table 1). However, minor differences in genotype frequencies between GD and controls persisted for, rs7578199 (HDLBP), rs2271233 (TEKT1), rs7250822 (JSRP1) and rs2230018 (UTX) SNPs, producing ORs of 1.11 (95% CI=1.02–1.22), 0.84 (95% CI=0.73–0.96), 1.50 (95% CI=1.18–1.90) and 1.21 (95% CI=1.06–1.38), respectively. Differences in allele frequencies were also observed between GD cases and controls for rs7975069 (ZNF268) although no association between genotypes and GD was observed (P=0.060).

On the basis of the finding of weak association of, HDLBP, TEKT1, JSRP1 and UTX in the combined collection, we then subjected the remainder of these gene regions to Tag SNP screening to capture the majority of the common variation and determine if further GD associations existed within these regions.

Tag SNP genotyping results

None of the Tag SNPs for JSRP1 or UTX showed evidence of association with GD in the geographically matched combined collection of 5168 samples (2478 GD cases and 2690 controls) (Table 2). Out of 14 Tag SNPs for HDLBP; two, rs6437249 and rs11680329, showed some evidence for association with GD producing ORs of 0.91 (95% CI=0.84–0.99) and 1.11 (95% CI=1.00–1.22), respectively. From the six Tag SNPs for TEKT1; two, rs4796561 and rs4796356, also showed some evidence for association with GD producing ORs of 0.88 (95% CI=0.81–0.96) and 1.12 (95% CI=1.03–1.23). A third SNP rs8078571 showed differences in allele frequency but no evidence of genotypic association (P=0.104).

Discussion

In this study, we have failed to replicate, in an adequately powered independent data set, association of nine novel nsSNPs previously reported to be weakly associated with GD in the WTCCC nsSNP study. Although none of the original nsSNPs were found to be associated with GD in the replication study, nsSNPs in HDLBP, TEKT1, JSRP1 and UTX remained weakly associated (P<0.05) in the complete collection of 5924 UK GD cases and controls. Tag SNPs in HDLBP and TEKT1, not originally typed in the nsSNP study also provide a weak signal for association in the complete geographically matched collection.

A number of significant replicated associations have previously been reported for GD, the HLA class I and II regions,^{9, 10} cytotoxic T-lymphocyte-associated protein 4 (CTLA-4),¹¹ protein tyrosine phosphatase non-receptor type 22 (PTPN22)^{12, 13} and TSHR⁵ with ORs for the development of disease ranging from 1.50–3.00. Weaker replicated effects are also likely to be conferred by the interleukin 2 receptor alpha gene (IL2RA),¹⁴ CD40^{15, 16} and FCRL⁴ with lesser ORs of 1.10–1.30. Other loci in Caucasian subjects have been reported although findings are less consistent (thyroglobulin^{17, 18}) or require further replication (including PTPN2 and CD226¹⁹). To help identify further susceptibility loci, large-scale genome-wide association studies (comprising >500 000 SNPs) have been conducted and are at last beginning to deliver novel susceptibility loci for a number of common diseases including the autoimmune diseases. The most comprehensive association study published in GD was conducted by the WTCCC and included 14 500 nsSNPs in 900 GD index cases and 1500 controls.⁴ Reassuringly, association with disease was replicated for the previously identified loci within the HLA region, FCRL3/5 and the TSHR. Other previously identified loci including CTLA-4 and PTPN22 were not detected as these genes were not covered in the nsSNP study, which used a custom-made Infinium array (Illumina, San Diego, CA, USA) based largely on experimentally validated nsSNPs with a MAF>1% in western European samples.⁴ In addition to HLA, FCRL3/5 and the TSHR, nine novel regions were also reported to be weakly associated with GD at a significance level of only P⩽10⁻⁴. The lack of replication reported in the current independent collection of GD index cases and controls vindicates the caution exercised over the interpretation of the weak associations in the original WTCCC nsSNP study.

The replication collection of UK GD cases and controls used in this study was adequately powered to detect the size of effect observed in the WTCCC nsSNP study, except TEKT1. More over for the majority of SNPs tested we had >80% power in the replication collection to detect an OR of 1.13–1.18 or higher. Clearly, however, we can not exclude smaller sized effects at these loci, which may be contributing to the overall genetic architecture of GD. Studies in other common autoimmune diseases have revealed loci conferring ORs for disease risk of <1.20 and have indicated the size of cohort required to detect such effects. In type 1 diabetes, wherein results from two different genome-wide studies both using approximately 4000 cases and controls found loci with small effects, including, for example, BACH2 and CTSH, convincing genome-wide statistical significance was only achieved when these effects were tested in 12 971 cases and controls.²⁰

The replication and extension data presented in this study, based on 5924 samples and representing the largest GD association study to date, serve to highlight, the importance of appropriate levels of statistical significance and data interpretation. Our study also highlights the need for collaborative efforts to produce large collections of DNA for genome-wide screening and replication in common diseases such as GD in which individual susceptibility loci are likely to be exerting small effects.

Appendix

Membership of WTCCC

Membership of the Wellcome Trust Case–Control Consortium (WTCCC)

Management Committee: Paul R Burton¹, David G Clayton², Lon R Cardon³, Nick Craddock⁴, Panos Deloukas⁵, Audrey Duncanson⁶, Dominic P Kwiatkowski^3,5, Mark I McCarthy^3,7, Willem H Ouwehand^8,9, Nilesh J Samani¹⁰, John A Todd², Peter Donnelly (Chair)¹¹

Analysis Committee: Jeffrey C Barrett³, Paul R Burton¹, Dan Davison¹¹, Peter Donnelly¹¹, Doug Easton¹², David Evans³, Hin-Tak Leung², Jonathan L Marchini¹¹, Andrew P Morris³, I CA Spencer¹¹, Martin D Tobin¹, Lon R Cardon (co-chair)³, David G Clayton (co-chair)²

UK Blood Services and University of Cambridge Controls: Antony P Attwood^5,8, James P Boorman^8,9, Barbara Cant⁸, Ursula Everson¹³, Judith M Hussey¹⁴, Jennifer D Jolley⁸, Alexandra S Knight⁸, Kerstin Koch⁸, Elizabeth Meech¹⁵, Sarah Nutland², Christopher V Prowse¹⁶, Helen E Stevens², Niall C Taylor⁸, Graham R Walters¹⁷, Neil M Walker², Nicholas A Watkins^8,9, Thilo Winzer⁸, John A Todd², Willem H Ouwehand^8,9

1958 Birth Cohort Controls: Richard W Jones¹⁸, Wendy L McArdle¹⁸, Susan M Ring¹⁸, David P Strachan¹⁹, Marcus Pembrey^18,20

Bipolar Disorder: Aberdeen – Gerome Breen²¹, David St Clair²¹; Birmingham – Sian Caesar²², Katherine Gordon-Smith^22,23, Lisa Jones²²; Cardiff – Christine Fraser²³, Elaine K Green²³, Detelina Grozeva²³, Marian L Hamshere²³, Peter A Holmans²³, Ian R Jones²³, George Kirov²³, Valentina Moskvina²³, Ivan Nikolov²³, Michael C O'Donovan²³, Michael J Owen²³, Nick Craddock²³; London – David A Collier²⁴, Amanda Elkin²⁴, Anne Farmer²⁴, Richard Williamson²⁴, Peter McGuffin²⁴; Newcastle – Allan H Young²⁵, I Nicol Ferrier²⁵

Coronary Artery Disease: Leeds – Stephen G Ball²⁶, Anthony J Balmforth²⁶, Jennifer H Barrett²⁶, D Timothy Bishop²⁶, Mark M Iles²⁶, Azhar Maqbool²⁶, Nadira Yuldasheva²⁶, Alistair S Hall²⁶; Leicester – Peter S Braund¹⁰, Paul R Burton¹, Richard J Dixon¹⁰, Massimo Mangino¹⁰, Suzanne Stevens¹⁰, Martin D Tobin¹, John R Thompson¹, Nilesh J Samani¹⁰

Crohn's Disease: Cambridge – Francesca Bredin²⁷, Mark Tremelling²⁷, Miles Parkes²⁷; Edinburgh – Hazel Drummond²⁸, Charles W Lees²⁸, Elaine R Nimmo²⁸, Jack Satsangi²⁸; London – Sheila A Fisher²⁹, Alastair Forbes³⁰, Cathryn M Lewis²⁹, Clive M Onnie²⁹, Natalie J Prescott²⁹, Jeremy Sanderson³¹, Christopher G Mathew²⁹; Newcastle – Jamie Barbour³², M Khalid Mohiuddin³², Catherine E Todhunter³², John C Mansfield³²; Oxford – Tariq Ahmad³³, Fraser R Cummings³³, Derek P Jewell³³

Hypertension: Aberdeen – John Webster³⁴; Cambridge – Morris J Brown³⁵, David G Clayton²; Evry, France – G Mark Lathrop³⁶; Glasgow – John Connell³⁷, Anna Dominiczak³⁷; Leicester – Nilesh J Samani¹⁰; London – Carolina A Braga Marcano³⁸, Beverley Burke³⁸, Richard Dobson³⁸, Johannie Gungadoo³⁸, Kate L Lee³⁸, Patricia B Munroe³⁸, Stephen J Newhouse³⁸, Abiodun Onipinla³⁸, I Wallace³⁸, Mingzhan Xue³⁸, Mark Caulfield³⁸; Oxford – Martin Farrall³⁹

Rheumatoid Arthritis: Anne Barton⁴⁰, The Biologics in RA Genetics and Genomics Study Syndicate (BRAGGS) Steering Committee, Ian N Bruce⁴⁰, Hannah Donovan⁴⁰, Steve Eyre⁴⁰, Paul D Gilbert⁴⁰, Samantha L Hider⁴⁰, Anne M Hinks⁴⁰, Sally L John⁴⁰, Catherine Potter⁴⁰, Alan J Silman⁴⁰, Deborah PM Symmons⁴⁰, Wendy Thomson⁴⁰, Jane Worthington⁴⁰

Type 1 Diabetes: David G Clayton², David B Dunger^2,41, Sarah Nutland², Helen E Stevens², Neil M Walker², Barry Widmer^2,41, John A Todd²

Type 2 Diabetes: Exeter – Timothy M Frayling^42,43, Rachel M Freathy^42,43, Hana Lango^42,43, John R B Perry^42,43, Beverley M Shields⁴³, Michael N Weedon^42,43, Andrew T Hattersley^42,43; London – Graham A Hitman⁴⁴; Newcastle – Mark Walker⁴⁵; Oxford – Kate S Elliott^3,7, Christopher J Groves⁷, Cecilia M Lindgren^3,7, Nigel W Rayner^3,7, Nicholas J Timpson^3,46, Eleftheria Zeggini^3,7, Mark I McCarthy^3,7

Tuberculosis: Gambia – Melanie Newport⁴⁷, Giorgio Sirugo⁴⁷; Oxford – Emily Lyons³, Fredrik Vannberg³, Adrian VS Hill³

Ankylosing Spondylitis: Linda A Bradbury⁴⁸, Claire Farrar⁴⁹, Jennifer J Pointon⁴⁸, Paul Wordsworth⁴⁹, Matthew A Brown^48,49

AutoImmune Thyroid Disease: Jayne A Franklyn⁵⁰, Joanne M Heward⁵⁰, Matthew J Simmonds⁵⁰, Stephen CL Gough⁵⁰

Breast Cancer: Sheila Seal⁵¹, Breast Cancer Susceptibility Collaboration (UK)^*, Michael R Stratton^51,52, Nazneen Rahman⁵¹

Multiple Sclerosis: Maria Ban⁵³, An Goris⁵³, Stephen J Sawcer⁵³, Alastair Compston⁵³

Gambian Controls: Gambia – David Conway⁴⁷, Muminatou Jallow⁴⁷, Melanie Newport⁴⁷, Giorgio Sirugo⁴⁷; Oxford – Kirk A Rockett³, Dominic P Kwiatkowski^3,5

DNA, Genotyping, Data QC and Informatics: Wellcome Trust Sanger Institute, Hinxton – Claire Bryan⁵, Suzannah J Bumpstead⁵, Amy Chaney⁵, Kate Downes^2,5, Jilur Ghori⁵, Rhian Gwilliam⁵, Sarah E Hunt⁵, Michael Inouye⁵, Andrew Keniry⁵, Emma King⁵, Ralph McGinnis⁵, Simon Potter⁵, Rathi Ravindrarajah⁵, Pamela Whittaker⁵, David Withers⁵, Panos Deloukas⁵; Cambridge – Hin-Tak Leung², Sarah Nutland², Helen E Stevens², Neil M Walker², John A Todd²

Statistics: Cambridge – Doug Easton¹², David G Clayton²; Leicester – Paul R Burton¹, Martin D Tobin¹; Oxford – Jeffrey C Barrett³, David Evans³, Andrew P Morris³, Lon R Cardon³; Oxford – Niall J Cardin¹¹, Dan Davison¹¹, Teresa Ferreira¹¹, Joanne Pereira-Gale¹¹, Ingeleif B Hallgrimsdóttir¹¹, Bryan N Howie¹¹, Jonathan L Marchini¹¹, I CA Spencer¹¹, Zhan Su¹¹, Yik Ying Teo^3,11, Damjan Vukcevic¹¹, Peter Donnelly¹¹

PIs: David Bentley^5,54, Matthew A Brown^48,49, Lon R Cardon³, Mark Caulfield³⁸, David G Clayton², Alistair Compston⁵³, Nick Craddock²³, Panos Deloukas⁵, Peter Donnelly¹¹, Martin Farrall³⁹, Stephen CL Gough⁵⁰, Alistair S Hall²⁶, Andrew T Hattersley^42,43, Adrian VS Hill³, Dominic P Kwiatkowski^3,5, Christopher G Mathew²⁹, Mark I McCarthy^3,7, Willem H Ouwehand^8,9, Miles Parkes²⁷, Marcus Pembrey^18,20, Nazneen Rahman⁵¹, Nilesh J Samani¹⁰, Michael R Stratton^51,52, John A Todd², Jane Worthington⁴⁰

¹Genetic Epidemiology Group, Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, UK; ²Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Cambridge CB2 0XY, UK; ³Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK; ⁴Department of Psychological Medicine, Henry Wellcome Building, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK; ⁵The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; ⁶The Wellcome Trust, Gibbs Building, 215 Euston Road, London NW1 2BE, UK; ⁷Oxford Centre for Diabetes, Endocrinology and Medicine, University of Oxford, Churchill Hospital, Oxford OX3 7LJ, UK; ⁸Department of Haematology, University of Cambridge, Long Road, Cambridge CB2 2PT, UK; ⁹National Health Service Blood and Transplant, Cambridge Centre, Long Road, Cambridge CB2 2PT, UK; ¹⁰Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK; ¹¹Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK; ¹²Cancer Research UK Genetic Epidemiology Unit, Strangeways Research Laboratory, Worts Causeway, Cambridge CB1 8RN, UK;¹³National Health Service Blood and Transplant, Sheffield Centre, Longley Lane, Sheffield S5 7JN, UK; ¹⁴National Health Service Blood and Transplant, Brentwood Centre, Crescent Drive, Brentwood CM15 8DP, UK; ¹⁵The Welsh Blood Service, Ely Valley Road, Talbot Green, Pontyclun CF72 9WB, UK; ¹⁶The Scottish National Blood Transfusion Service, Ellen's Glen Road, Edinburgh EH17 7QT, UK; ¹⁷National Health Service Blood and Transplant, Southampton Centre, Coxford Road, Southampton SO16 5AF, UK; ¹⁸Avon Longitudinal Study of Parents and Children, University of Bristol, 24 Tyndall Avenue, Bristol BS8 1TQ, UK; ¹⁹Division of Community Health Services, St George's University of London, Cranmer Terrace, London SW17 0RE, UK; ²⁰Institute of Child Health, University College London, 30 Guilford St, London WC1N 1EH, UK; ²¹University of Aberdeen, Institute of Medical Sciences, Foresterhill, Aberdeen AB25 2ZD, UK; ²²Division of Neuroscience, Department of Psychiatry, Birmingham University, Birmingham B15 2QZ, UK; ²³Department of Psychological Medicine, Henry Wellcome Building, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, UK; ²⁴King's College London, SGDP, The Institute of Psychiatry, De Crespigny Park Denmark Hill London SE5 8AF, UK; ²⁵School of Neurology, Neurobiology and Psychiatry, Royal Victoria Infirmary, Queen Victoria Road, Newcastle upon Tyne NE1 4LP, UK; ²⁶Faculty of Medicine and Health, LIGHT and LIMM Research Institutes, University of Leeds, Leeds LS1 3EX, UK; ²⁷IBD Research Group, Addenbrooke's Hospital, University of Cambridge, Cambridge CB2 2QQ, UK; ²⁸Gastrointestinal Unit, School of Molecular and Clinical Medicine, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK; ²⁹King's College London School of Medicine, Department of Medical and Molecular Genetics, 8th Floor Guy's Tower, Guy's Hospital, London SE1 9RT, UK; ³⁰Institute for Digestive Diseases, University College London Hospitals Trust, London NW1 2BU, UK; ³¹Department of Gastroenterology, Guy's and St Thomas' NHS Foundation Trust, London SE1 7EH, UK; ³²Department of Gastroenterology and Hepatology, University of Newcastle upon Tyne, Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK; ³³Gastroenterology Unit, Radcliffe Infirmary, University of Oxford, Oxford OX2 6HE, UK^{; 34}Medicine and Therapeutics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, Grampian AB9 2ZB, UK; ³⁵Clinical Pharmacology Unit and the Diabetes and Inflammation Laboratory, University of Cambridge, Addenbrookes Hospital, Hills Road, Cambridge CB2 2QQ, UK; ³⁶Centre National de Genotypage, 2, Rue Gaston Cremieux, Evry, Paris 91057; ³⁷BHF Glasgow Cardiovascular Research Centre, University of Glasgow, 126 University Place, Glasgow G12 8TA, UK; ³⁸Clinical Pharmacology and Barts and The London Genome Centre, William Harvey Research Institute, Barts and The London, Queen Mary's School of Medicine, Charterhouse Square, London EC1M 6BQ, UK; ³⁹Cardiovascular Medicine, University of Oxford, Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK; ⁴⁰arc Epidemiology Research Unit, University of Manchester, Stopford Building, Oxford Rd, Manchester M13 9PT, UK; ⁴¹Department of Paediatrics, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK; ⁴²Genetics of Complex Traits, Institute of Biomedical and Clinical Science, Peninsula Medical School, Magdalen Road, Exeter EX1 2LU UK; ⁴³Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula Medical School, Barrack Road, Exeter EX2 5DU UK; ⁴⁴Centre for Diabetes and Metabolic Medicine, Barts and The London, Royal London Hospital, Whitechapel, London E1 1BB UK; ⁴⁵Diabetes Research Group, School of Clinical Medical Sciences, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK; ⁴⁶The MRC Centre for Causal Analyses in Translational Epidemiology, Bristol University, Canynge Hall, Whiteladies Rd, Bristol BS2 8PR, UK; ⁴⁷MRC Laboratories, Fajara, The Gambia; ⁴⁸Diamantina Institute for Cancer, Immunology and Metabolic Medicine, Princess Alexandra Hospital, University of Queensland, Woolloongabba, Qld 4102, Australia; ⁴⁹Botnar Research Centre, University of Oxford, Headington, Oxford OX3 7BN, UK; ⁵⁰Division of Medical Sciences, Department of Medicine, Institute of Biomedical Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; ⁵¹Section of Cancer Genetics, Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5 NG, UK; ⁵²Cancer Genome Project, The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK; ⁵³Department of Clinical Neurosciences, University of Cambridge, Addenbrooke's Hospital, Hills Road, Cambridge CB2 2QQ, UK; ⁵⁴PRESENT ADDRESS: Illumina Cambridge, Chesterford Research Park, Little Chesterford, Nr Saffron Walden, Essex CB10 1XL, UK.

The authors declare no conflict of interest.