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Publications by Year: 2012

2012

Dastani Z, Hivert MF, Timpson N, Perry J, Yuan X, Scott R, Henneman P, Heid I, Kizer J, Lyytikäinen LP, Fuchsberger C, Tanaka T, Morris A, Small K, Isaacs A, Beekman M, Coassin S, Lohman K, Qi L, Kanoni S, Pankow J, Uh HW, Wu Y, Bidulescu A, Rasmussen-Torvik L, Greenwood C, Ladouceur M, Grimsby J, Manning A, Liu CT, Kooner J, Mooser V, Vollenweider P, Kapur K, Chambers J, Wareham N, Langenberg C, Frants R, Willems-Vandijk K, Oostra B, Willems S, Lamina C, Winkler T, Psaty B, Tracy R, Brody J, Chen I, Viikari J, Kähönen M, Pramstaller P, Evans D, St Pourcain B, Sattar N, Wood A, Bandinelli S, Carlson O, Egan J, Böhringer S, Heemst D, Kedenko L, Kristiansson K, Nuotio ML, Loo BM, Harris T, Garcia M, Kanaya A, Haun M, Klopp N, Wichmann HE, Deloukas P, Katsareli E, Couper D, Duncan B, Kloppenburg M, Adair L, Borja J, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, Wilson J, Musani S, Guo X, Johnson T, Semple R, Teslovich T, Allison M, Redline S, Buxbaum S, Mohlke K, Meulenbelt I, Ballantyne C, Dedoussis G, Hu F, Liu Y, Paulweber B, Spector T, Slagboom E, Ferrucci L, Jula A, Perola M, Raitakari O, Florez J, Salomaa V, Eriksson J, Frayling T, Hicks A, Lehtimäki T, Smith GD, Siscovick D, Kronenberg F, Duijn C, Loos R, Waterworth D, Meigs J, Dupuis J, Richards B, Voight B, Scott L, Steinthorsdottir V, Dina C, Welch R, Zeggini E, Huth C, Aulchenko Y, Thorleifsson G, McCulloch L, Ferreira T, Grallert H, Amin N, Wu G, Willer C, Raychaudhuri S, McCarroll S, Hofmann O, Segrè A, Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett A, Blagieva R, Boerwinkle E, Bonnycastle L, Bengtsson Boström K, Bravenboer B, Bumpstead S, Burtt N, Charpentier G, Chines P, Cornelis M, Crawford G, Doney A, Elliott K, Elliott A, Erdos M, Fox C, Franklin C, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves C, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson A, Johnson P, Jørgensen T, Kao W, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren C, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken M, Narisu N, Nilsson P, Owen K, Payne F, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner W, Robertson N, Rocheleau G, Roden M, Sampson M, Saxena R, Shields B, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham H, Sun Q, Swift A, Thorand B, Tichet J, Tuomi T, Dam R, Haeften T, Herpt T, Vliet-Ostaptchouk J, Walters B, Weedon M, Wijmenga C, Witteman J, Bergman R, Cauchi S, Collins F, Gloyn A, Gyllensten U, Hansen T, Hide W, Hitman G, Hofman A, Hunter D, Hveem K, Laakso M, Morris A, Palmer C, Rudan I, Sijbrands E, Stein L, Tuomilehto J, Uitterlinden A, Walker M, Watanabe R, Abecasis G, Boehm B, Campbell H, Daly M, Hattersley A, Pedersen O, Barroso I, Groop L, Sladek R, Thorsteinsdottir U, Wilson J, Illig T, Froguel P, Duijn C, Stefansson K, Altshuler D, Boehnke M, McCarthy M, Soranzo N, Wheeler E, Glazer N, Bouatia-Naji N, Mägi R, Randall J, Elliott P, Rybin D, Dehghan A, Hottenga JJ, Song K, Goel A, Lajunen T, Doney A, Cavalcanti-Proença C, Kumari M, Timpson N, Zabena C, Ingelsson E, An P, O’Connell J, Luan J, Elliott A, McCarroll S, Roccasecca RM, Pattou F, Sethupathy P, Ariyurek Y, Barter P, Beilby J, Ben-Shlomo Y, Bergmann S, Bochud M, Bonnefond A, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead S, Chen YDI, Chines P, Clarke R, Coin L, Cooper M, Crisponi L, Day I, Geus E, Delplanque J, Fedson A, Fischer-Rosinsky A, Forouhi N, Franzosi MG, Galan P, Goodarzi M, Graessler J, Grundy S, Gwilliam R, Hallmans G, Hammond N, Han X, Hartikainen AL, Hayward C, Heath S, Hercberg S, Hillman D, Hingorani A, Hui J, Hung J, Kaakinen M, Kaprio J, Kesaniemi A, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop M, Lawlor D, Le Bacquer O, Lecoeur C, Li Y, Mahley R, Mangino M, Martínez-Larrad MT, McAteer J, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell B, Mukherjee S, Naitza S, Neville M, Orrú M, Pakyz R, Paolisso G, Pattaro C, Pearson D, Peden J, Pedersen N, Pfeiffer A, Pichler I, Polašek O, Posthuma D, Potter S, Pouta A, Province M, Rayner N, Rice K, Ripatti S, Rivadeneira F, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Seedorf U, Sharp S, Shields B, Sigurðsson G, Sijbrands E, Silveira A, Simpson L, Singleton A, Smith N, Sovio U, Swift A, Syddall H, Syvänen AC, Tönjes A, Uitterlinden A, Dijk KW, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner P, Walley A, Ward K, Watkins H, Wild S, Willemsen G, Witteman J, Yarnell J, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens C, DIAGRAM Consortium, GIANT Consortium, Consortium GP, Borecki I, Meneton P, Magnusson P, Nathan D, Williams G, Silander K, Bornstein S, Schwarz P, Spranger J, Karpe F, Shuldiner A, Cooper C, Serrano-Ríos M, Lind L, Palmer L, Hu F, Franks P, Ebrahim S, Marmot M, Kao L, Pramstaller PP, Wright A, Stumvoll M, Hamsten A, Procardis Consortium, Buchanan T, Valle T, Rotter J, Penninx B, Boomsma D, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Peltonen L, Mooser V, Sladek R, MAGIC investigators, GLGC Consortium, Musunuru K, Smith A, Edmondson A, Stylianou I, Koseki M, Pirruccello J, Chasman D, Johansen C, Fouchier S, Peloso G, Barbalic M, Ricketts S, Bis J, Feitosa M, Orho-Melander M, Melander O, Li X, Li M, Cho YS, Go MJ, Kim YJ, Lee JY, Park T, Kim K, Sim X, Ong RTH, Croteau-Chonka D, Lange L, Smith J, Ziegler A, Zhang W, Zee R, Whitfield J, Thompson J, Surakka I, Spector T, Smit J, Sinisalo J, Scott J, Saharinen J, Sabatti C, Rose L, Roberts R, Rieder M, Parker A, Paré G, O’Donnell C, Nieminen M, Nickerson D, Montgomery G, McArdle W, Masson D, Martin N, Marroni F, Lucas G, Luben R, Lokki ML, Lettre G, Launer L, Lakatta E, Laaksonen R, Kyvik K, König I, Khaw KT, Kaplan L, Johansson Å, Janssens C, Igl W, Hovingh K, Hengstenberg C, Havulinna A, Hastie N, Harris T, Haritunians T, Hall A, Groop L, Gonzalez E, Freimer N, Erdmann J, Ejebe K, Döring A, Dominiczak A, Demissie S, Deloukas P, Faire U, Crawford G, Chen YD, Caulfield M, Boekholdt M, Assimes T, Quertermous T, Seielstad M, Wong T, Tai ES, Feranil A, Kuzawa C, Taylor H, Gabriel S, Hólm H, Gudnason V, Krauss R, Ordovas J, Munroe P, Kooner J, Tall A, Hegele R, Kastelein J, Schadt E, Strachan D, Reilly M, Samani N, Schunkert H, Cupples A, Sandhu M, Ridker P, Rader D, Kathiresan S. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 2012;8(3):e1002607.
Publisher's Version
Publisher's Version
Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p<3×10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10(-3), n = 22,044), increased triglycerides (p = 2.6×10(-14), n = 93,440), increased waist-to-hip ratio (p = 1.8×10(-5), n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10(-3), n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10(-13), n = 96,748) and decreased BMI (p = 1.4×10(-4), n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.
Palmer N, McDonough C, Hicks P, Roh B, Wing M, An S, Hester J, Cooke J, Bostrom M, Rudock M, Talbert M, Lewis J, DIAGRAM Consortium, MAGIC investigators, Ferrara A, Lu L, Ziegler J, Sale M, Divers J, Shriner D, Adeyemo A, Rotimi C, Ng M, Langefeld C, Freedman B, Bowden D, Voight B, Scott L, Steinthorsdottir V, Morris A, Dina C, Welch R, Zeggini E, Huth C, Aulchenko Y, Thorleifsson G, McCulloch L, Ferreira T, Grallert H, Amin N, Wu G, Willer C, Raychaudhuri S, McCarroll S, Langenberg C, Hofmann O, Dupuis J, Qi L, Segrè A, Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett A, Blagieva R, Boerwinkle E, Bonnycastle L, Bengtsson Boström K, Bravenboer B, Bumpstead S, Burtt N, Charpentier G, Chines P, Cornelis M, Couper D, Crawford G, Doney A, Elliott K, Elliott A, Erdos M, Fox C, Franklin C, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves C, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson A, Johnson P, Jørgensen T, Kao W, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren C, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken M, Narisu N, Nilsson P, Owen K, Payne F, Perry J, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner W, Robertson N, Rocheleau G, Roden M, Sampson M, Saxena R, Shields B, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham H, Sun Q, Swift A, Thorand B, Tichet J, Tuomi T, Dam R, Haeften T, Herpt T, Vliet-Ostaptchouk J, Walters B, Weedon M, Wijmenga C, Witteman J, Bergman R, Cauchi S, Collins F, Gloyn A, Gyllensten U, Hansen T, Hide W, Hitman G, Hofman A, Hunter D, Hveem K, Laakso M, Mohlke K, Morris A, Palmer C, Pramstaller P, Rudan I, Sijbrands E, Stein L, Tuomilehto J, Uitterlinden A, Walker M, Wareham N, Watanabe R, Abecasis G, Boehm B, Campbell H, Daly M, Hattersley A, Hu F, Meigs J, Pankow J, Pedersen O, Wichmann HE, Barroso I, Florez J, Frayling T, Groop L, Sladek R, Thorsteinsdottir U, Wilson J, Illig T, Froguel P, Duijn C, Stefansson K, Altshuler D, Boehnke M, McCarthy M, Soranzo N, Wheeler E, Glazer N, Bouatia-Naji N, Mägi R, Randall J, Johnson T, Elliott P, Rybin D, Henneman P, Dehghan A, Hottenga JJ, Song K, Goel A, Egan J, Lajunen T, Doney A, Kanoni S, Cavalcanti-Proença C, Kumari M, Timpson N, Zabena C, Ingelsson E, An P, O’Connell J, Luan J, Elliott A, McCarroll S, Roccasecca RM, Pattou F, Sethupathy P, Ariyurek Y, Barter P, Beilby J, Ben-Shlomo Y, Bergmann S, Bochud M, Bonnefond A, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead S, Chen YDI, Chines P, Clarke R, Coin L, Cooper M, Crisponi L, Day I, Geus E, Delplanque J, Fedson A, Fischer-Rosinsky A, Forouhi N, Frants R, Franzosi MG, Galan P, Goodarzi M, Graessler J, Grundy S, Gwilliam R, Hallmans G, Hammond N, Han X, Hartikainen AL, Hayward C, Heath S, Hercberg S, Hicks A, Hillman D, Hingorani A, Hui J, Hung J, Jula A, Kaakinen M, Kaprio J, Kesaniemi A, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop M, Lawlor D, Le Bacquer O, Lecoeur C, Li Y, Mahley R, Mangino M, Manning A, Martínez-Larrad MT, McAteer J, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell B, Mukherjee S, Naitza S, Neville M, Oostra B, Orrú M, Pakyz R, Paolisso G, Pattaro C, Pearson D, Peden J, Pedersen N, Perola M, Pfeiffer A, Pichler I, Polašek O, Posthuma D, Potter S, Pouta A, Province M, Psaty B, Rayner N, Rice K, Ripatti S, Rivadeneira F, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Seedorf U, Sharp S, Shields B, Sijbrands E, Silveira A, Simpson L, Singleton A, Smith N, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Tönjes A, Uitterlinden A, Dijk KW, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner P, Walley A, Ward K, Watkins H, Wild S, Willemsen G, Witteman J, Yarnell J, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens C, Borecki I, Loos R, Meneton P, Magnusson P, Nathan D, Williams G, Silander K, Salomaa V, Smith GD, Bornstein S, Schwarz P, Spranger J, Karpe F, Shuldiner A, Cooper C, Dedoussis G, Serrano-Ríos M, Lind L, Palmer L, Franks P, Ebrahim S, Marmot M, Kao L, Pramstaller PP, Wright A, Stumvoll M, Hamsten A, Buchanan T, Valle T, Rotter J, Siscovick D, Penninx B, Boomsma D, Deloukas P, Spector T, Ferrucci L, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth D, Vollenweider P, Peltonen L, Mooser V, Sladek R. A genome-wide association search for type 2 diabetes genes in African Americans. PLoS One. 2012;7(1):e29202.
Publisher's Version
Publisher's Version
African Americans are disproportionately affected by type 2 diabetes (T2DM) yet few studies have examined T2DM using genome-wide association approaches in this ethnicity. The aim of this study was to identify genes associated with T2DM in the African American population. We performed a Genome Wide Association Study (GWAS) using the Affymetrix 6.0 array in 965 African-American cases with T2DM and end-stage renal disease (T2DM-ESRD) and 1029 population-based controls. The most significant SNPs (n = 550 independent loci) were genotyped in a replication cohort and 122 SNPs (n = 98 independent loci) were further tested through genotyping three additional validation cohorts followed by meta-analysis in all five cohorts totaling 3,132 cases and 3,317 controls. Twelve SNPs had evidence of association in the GWAS (P<0.0071), were directionally consistent in the Replication cohort and were associated with T2DM in subjects without nephropathy (P<0.05). Meta-analysis in all cases and controls revealed a single SNP reaching genome-wide significance (P<2.5×10(-8)). SNP rs7560163 (P = 7.0×10(-9), OR (95% CI) = 0.75 (0.67-0.84)) is located intergenically between RND3 and RBM43. Four additional loci (rs7542900, rs4659485, rs2722769 and rs7107217) were associated with T2DM (P<0.05) and reached more nominal levels of significance (P<2.5×10(-5)) in the overall analysis and may represent novel loci that contribute to T2DM. We have identified novel T2DM-susceptibility variants in the African-American population. Notably, T2DM risk was associated with the major allele and implies an interesting genetic architecture in this population. These results suggest that multiple loci underlie T2DM susceptibility in the African-American population and that these loci are distinct from those identified in other ethnic populations.
Stahl E, Raychaudhuri S. Rheumatoid arthritis. Evidence for a genetic component to disease severity in RA. Nat Rev Rheumatol. 2012;8(6):312–3.
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Publisher's Version
Rheumatoid arthritis (RA) is partly heritable; genetic and serological markers are known to confer risk of developing pathology. But given clinical heterogeneity in RA, can we predict who will develop severe disease? Substantial heritability of erosive progression rates has now been identified, but better prognostic biomarkers remain wanting.
Invernizzi, Ransom, Raychaudhuri, Kosoy, Lleo, Shigeta, Franke, Bossa, Amos, Gregersen, Siminovitch, Cusi, Bakker, Podda, Gershwin, Seldin, Italian PBC Genetics Study Group. Classical HLA-DRB1 and DPB1 alleles account for HLA associations with primary biliary cirrhosis. Genes Immun. 2012;13(6):461–8.
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Publisher's Version
Susceptibility to primary biliary cirrhosis (PBC) is strongly associated with human leukocyte antigen (HLA)-region polymorphisms. To determine if associations can be explained by classical HLA determinants, we studied Italian, 676 cases and 1440 controls, genotyped with dense single-nucleotide polymorphisms (SNPs) for which classical HLA alleles and amino acids were imputed. Although previous genome-wide association studies and our results show stronger SNP associations near DQB1, we demonstrate that the HLA signals can be attributed to classical DRB1 and DPB1 genes. Strong support for the predominant role of DRB1 is provided by our conditional analyses. We also demonstrate an independent association of DPB1. Specific HLA-DRB1 genes (*08, *11 and *14) account for most of the DRB1 association signal. Consistent with previous studies, DRB1*08 (P=1.59 × 10(-11)) was the strongest predisposing allele, whereas DRB1*11 (P=1.42 × 10(-10)) was protective. Additionally, DRB1*14 and the DPB1 association (DPB1*03:01; P=9.18 × 10(-7)) were predisposing risk alleles. No signal was observed in the HLA class 1 or class 3 regions. These findings better define the association of PBC with HLA and specifically support the role of classical HLA-DRB1 and DPB1 genes and alleles in susceptibility to PBC.
Sobrin L, Ripke S, Yu Y, Fagerness J, Bhangale T, Tan P, Souied E, Buitendijk G, Merriam J, Richardson A, Raychaudhuri S, Reynolds R, Chin K, Lee A, Leveziel N, Zack D, Campochiaro P, Smith T, Barile G, Hogg R, Chakravarthy U, Behrens T, Uitterlinden A, Duijn C, Vingerling J, Brantley M, Baird P, Klaver C, Allikmets R, Katsanis N, Graham R, Ioannidis J, Daly M, Seddon J. Heritability and genome-wide association study to assess genetic differences between advanced age-related macular degeneration subtypes. Ophthalmology. 2012;119(9):1874–85.
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Publisher's Version
PURPOSE: To investigate whether the 2 subtypes of advanced age-related macular degeneration (AMD), choroidal neovascularization (CNV), and geographic atrophy (GA) segregate separately in families and to identify which genetic variants are associated with these 2 subtypes. DESIGN: Sibling correlation study and genome-wide association study (GWAS). PARTICIPANTS: For the sibling correlation study, 209 sibling pairs with advanced AMD were included. For the GWAS, 2594 participants with advanced AMD subtypes and 4134 controls were included. Replication cohorts included 5383 advanced AMD participants and 15 240 controls. METHODS: Participants had the AMD grade assigned based on fundus photography, examination, or both. To determine heritability of advanced AMD subtypes, a sibling correlation study was performed. For the GWAS, genome-wide genotyping was conducted and 6 036 699 single nucleotide polymorphisms (SNPs) were imputed. Then, the SNPs were analyzed with a generalized linear model controlling for genotyping platform and genetic ancestry. The most significant associations were evaluated in independent cohorts. MAIN OUTCOME MEASURES: Concordance of advanced AMD subtypes in sibling pairs and associations between SNPs with GA and CNV advanced AMD subtypes. RESULTS: The difference between the observed and expected proportion of siblings concordant for the same subtype of advanced AMD was different to a statistically significant degree (P = 4.2 × 10(-5)), meaning that in siblings of probands with CNV or GA, the same advanced subtype is more likely to develop. In the analysis comparing participants with CNV to those with GA, a statistically significant association was observed at the ARMS2/HTRA1 locus (rs10490924; odds ratio [OR], 1.47; P = 4.3 × 10(-9)), which was confirmed in the replication samples (OR, 1.38; P = 7.4 × 10(-14) for combined discovery and replication analysis). CONCLUSIONS: Whether CNV versus GA develops in a patient with AMD is determined in part by genetic variation. In this large GWAS meta-analysis and replication analysis, the ARMS2/HTRA1 locus confers increased risk for both advanced AMD subtypes, but imparts greater risk for CNV than for GA. This locus explains a small proportion of the excess sibling correlation for advanced AMD subtype. Other loci were detected with suggestive associations that differ for advanced AMD subtypes and deserve follow-up in additional studies.
Jostins L, Ripke S, Weersma R, Duerr R, McGovern D, Hui K, Lee J, Schumm P, Sharma Y, Anderson C, Essers J, Mitrovic M, Ning K, Cleynen I, Theatre E, Spain S, Raychaudhuri S, Goyette P, Wei Z, Abraham C, Achkar JP, Ahmad T, Amininejad L, Ananthakrishnan A, Andersen V, Andrews J, Baidoo L, Balschun T, Bampton P, Bitton A, Boucher G, Brand S, Büning C, Cohain A, Cichon S, D’Amato M, De Jong D, Devaney K, Dubinsky M, Edwards C, Ellinghaus D, Ferguson L, Franchimont D, Fransen K, Gearry R, Georges M, Gieger C, Glas J, Haritunians T, Hart A, Hawkey C, Hedl M, Hu X, Karlsen T, Kupcinskas L, Kugathasan S, Latiano A, Laukens D, Lawrance I, Lees C, Louis E, Mahy G, Mansfield J, Morgan A, Mowat C, Newman W, Palmieri O, Ponsioen C, Potocnik U, Prescott N, Regueiro M, Rotter J, Russell R, Sanderson J, Sans M, Satsangi J, Schreiber S, Simms L, Sventoraityte J, Targan S, Taylor K, Tremelling M, Verspaget H, De Vos M, Wijmenga C, Wilson D, Winkelmann J, Xavier R, Zeissig S, Zhang B, Zhang C, Zhao H, International IBD Genetics Consortium (IIBDGC), Silverberg M, Annese V, Hakonarson H, Brant S, Radford-Smith G, Mathew C, Rioux J, Schadt E, Daly M, Franke A, Parkes M, Vermeire S, Barrett J, Cho J. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491(7422):119–24.
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Publisher's Version
Crohn's disease and ulcerative colitis, the two common forms of inflammatory bowel disease (IBD), affect over 2.5 million people of European ancestry, with rising prevalence in other populations. Genome-wide association studies and subsequent meta-analyses of these two diseases as separate phenotypes have implicated previously unsuspected mechanisms, such as autophagy, in their pathogenesis and showed that some IBD loci are shared with other inflammatory diseases. Here we expand on the knowledge of relevant pathways by undertaking a meta-analysis of Crohn's disease and ulcerative colitis genome-wide association scans, followed by extensive validation of significant findings, with a combined total of more than 75,000 cases and controls. We identify 71 new associations, for a total of 163 IBD loci, that meet genome-wide significance thresholds. Most loci contribute to both phenotypes, and both directional (consistently favouring one allele over the course of human history) and balancing (favouring the retention of both alleles within populations) selection effects are evident. Many IBD loci are also implicated in other immune-mediated disorders, most notably with ankylosing spondylitis and psoriasis. We also observe considerable overlap between susceptibility loci for IBD and mycobacterial infection. Gene co-expression network analysis emphasizes this relationship, with pathways shared between host responses to mycobacteria and those predisposing to IBD.
Kurreeman F, Stahl E, Okada Y, Liao K, Diogo D, Raychaudhuri S, Freudenberg J, Kochi Y, Patsopoulos N, Gupta N, CLEAR investigators, Sandor C, Bang SY, Lee HS, Padyukov L, Suzuki A, Siminovitch K, Worthington J, Gregersen P, Hughes L, Reynolds R, Bridges L, Bae SC, Yamamoto K, Plenge R. Use of a multiethnic approach to identify rheumatoid- arthritis-susceptibility loci, 1p36 and 17q12. Am J Hum Genet. 2012;90(3):524–32.
Publisher's Version
Publisher's Version
We have previously shown that rheumatoid arthritis (RA) risk alleles overlap between different ethnic groups. Here, we utilize a multiethnic approach to show that we can effectively discover RA risk alleles. Thirteen putatively associated SNPs that had not yet exceeded genome-wide significance (p < 5 × 10(-8)) in our previous RA genome-wide association study (GWAS) were analyzed in independent sample sets consisting of 4,366 cases and 17,765 controls of European, African American, and East Asian ancestry. Additionally, we conducted an overall association test across all 65,833 samples (a GWAS meta-analysis plus the replication samples). Of the 13 SNPs investigated, four were significantly below the study-wide Bonferroni corrected p value threshold (p < 0.0038) in the replication samples. Two SNPs (rs3890745 at the 1p36 locus [p = 2.3 × 10(-12)] and rs2872507 at the 17q12 locus [p = 1.7 × 10(-9)]) surpassed genome-wide significance in all 16,659 RA cases and 49,174 controls combined. We used available GWAS data to fine map these two loci in Europeans and East Asians, and we found that the same allele conferred risk in both ethnic groups. A series of bioinformatic analyses identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as the genes most likely associated with RA. These findings demonstrate empirically that a multiethnic approach is an effective strategy for discovering RA risk loci, and they suggest that combining GWASs across ethnic groups represents an efficient strategy for gaining statistical power.
Raychaudhuri S, Sandor C, Stahl E, Freudenberg J, Lee HS, Jia X, Alfredsson L, Padyukov L, Klareskog L, Worthington J, Siminovitch K, Bae SC, Plenge R, Gregersen P, Bakker P. Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat Genet. 2012;44(3):291–6.
Publisher's Version
Publisher's Version
The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to alleles in HLA-DRB1. However, debate persists about the identity of the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 individuals with seropositive rheumatoid arthritis (cases) and 14,974 unaffected controls, we imputed and tested classical alleles and amino acid polymorphisms in HLA-A, HLA-B, HLA-C, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1 and HLA-DRB1, as well as 3,117 SNPs across the MHC. Conditional and haplotype analyses identified that three amino acid positions (11, 71 and 74) in HLA-DRβ1 and single-amino-acid polymorphisms in HLA-B (at position 9) and HLA-DPβ1 (at position 9), which are all located in peptide-binding grooves, almost completely explain the MHC association to rheumatoid arthritis risk. This study shows how imputation of functional variation from large reference panels can help fine map association signals in the MHC.
Stahl E, Wegmann D, Trynka G, Gutierrez-Achury J, Do R, Voight B, Kraft P, Chen R, Kallberg H, Kurreeman F, Consortium DGRM analysis, Myocardial Infarction Genetics Consortium, Kathiresan S, Wijmenga C, Gregersen P, Alfredsson L, Siminovitch K, Worthington J, Bakker P, Raychaudhuri S, Plenge R. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet. 2012;44(5):483–9.
Publisher's Version
Publisher's Version
The genetic architectures of common, complex diseases are largely uncharacterized. We modeled the genetic architecture underlying genome-wide association study (GWAS) data for rheumatoid arthritis and developed a new method using polygenic risk-score analyses to infer the total liability-scale variance explained by associated GWAS SNPs. Using this method, we estimated that, together, thousands of SNPs from rheumatoid arthritis GWAS explain an additional 20% of disease risk (excluding known associated loci). We further tested this method on datasets for three additional diseases and obtained comparable estimates for celiac disease (43% excluding the major histocompatibility complex), myocardial infarction and coronary artery disease (48%) and type 2 diabetes (49%). Our results are consistent with simulated genetic models in which hundreds of associated loci harbor common causal variants and a smaller number of loci harbor multiple rare causal variants. These analyses suggest that GWAS will continue to be highly productive for the discovery of additional susceptibility loci for common diseases.