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2013

Gusev A, Bhatia G, Zaitlen N, Vilhjálmsson B, Diogo D, Stahl E, Gregersen P, Worthington J, Klareskog L, Raychaudhuri S, Plenge R, Pasaniuc B, Price A. Quantifying missing heritability at known GWAS loci. PLoS Genet. 2013;9(12):e1003993.
Publisher's Version
Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 x more heritability than GWAS-associated SNPs on average (P=3.3 x 10⁻⁵). For some diseases, this increase was individually significant: 2.07 x for Multiple Sclerosis (MS) (P=6.5 x 10⁻⁹) and 1.48 x for Crohn's Disease (CD) (P = 1.3 x 10⁻³); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 x more MS heritability than known MS SNPs (P < 1.0 x 10⁻¹⁶ and 2.20 x more CD heritability than known CD SNPs (P = 6.1 x 10⁻⁹), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 x more heritability from all SNPs at GWAS loci (P = 2.3 x 10⁻⁶) and 5.33 x more heritability from all autoimmune disease loci (P < 1 x 10⁻¹⁶ compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.
Trynka G, Raychaudhuri S. Using chromatin marks to interpret and localize genetic associations to complex human traits and diseases. Curr Opin Genet Dev. 2013;23(6):635–41.
Publisher's Version
While studies to associate genomic variants to complex traits have gradually become increasingly productive, the molecular mechanisms that underlie these associations are rarely understood. Because only a small fraction of trait-associated variants can be linked to coding sequences, investigators have speculated that many of the underlying causal alleles influence non-coding gene regulatory sites. Recent studies have successfully identified examples of mechanisms for non-coding alleles at individual loci. Now, genome-wide chromatin assays have resulted in maps of dozens of genomic annotations of the non-coding genome across multiple different tissues, cell types and cell lines. This gives a tremendous opportunity to integrate these annotations with complex trait signals to globally interpret associated variants, and prioritize likely causal alleles. Here, we review the examples of mechanisms by which non-coding, common alleles result in phenotypes. We discuss the efforts to integrate common trait-associated variants with genomic annotations. Finally, we highlight some caveats of these approaches and outline future directions for improvement.
Hu X, Kim H, Brennan P, Han B, Baecher-Allan C, De Jager P, Brenner M, Raychaudhuri S. Application of user-guided automated cytometric data analysis to large-scale immunoprofiling of invariant natural killer T cells. Proc Natl Acad Sci U S A. 2013;110(47):19030–5.
Publisher's Version
Defining and characterizing pathologies of the immune system requires precise and accurate quantification of abundances and functions of cellular subsets via cytometric studies. At this time, data analysis relies on manual gating, which is a major source of variability in large-scale studies. We devised an automated, user-guided method, X-Cyt, which specializes in rapidly and robustly identifying targeted populations of interest in large data sets. We first applied X-Cyt to quantify CD4(+) effector and central memory T cells in 236 samples, demonstrating high concordance with manual analysis (r = 0.91 and 0.95, respectively) and superior performance to other available methods. We then quantified the rare mucosal associated invariant T cell population in 35 samples, achieving manual concordance of 0.98. Finally we characterized the population dynamics of invariant natural killer T (iNKT) cells, a particularly rare peripheral lymphocyte, in 110 individuals by assaying 19 markers. We demonstrated that although iNKT cell numbers and marker expression are highly variable in the population, iNKT abundance correlates with sex and age, and the expression of phenotypic and functional markers correlates closely with CD4 expression.
Trynka G, Sandor C, Han B, Xu H, Stranger B, Liu S, Raychaudhuri S. Chromatin marks identify critical cell types for fine mapping complex trait variants. Nat Genet. 2013;45(2):124–30.
Publisher's Version
If trait-associated variants alter regulatory regions, then they should fall within chromatin marks in relevant cell types. However, it is unclear which of the many marks are most useful in defining cell types associated with disease and fine mapping variants. We hypothesized that informative marks are phenotypically cell type specific; that is, SNPs associated with the same trait likely overlap marks in the same cell type. We examined 15 chromatin marks and found that those highlighting active gene regulation were phenotypically cell type specific. Trimethylation of histone H3 at lysine 4 (H3K4me3) was the most phenotypically cell type specific (P < 1 × 10(-6)), driven by colocalization of variants and marks rather than gene proximity (P < 0.001). H3K4me3 peaks overlapped with 37 SNPs for plasma low-density lipoprotein concentration in the liver (P < 7 × 10(-5)), 31 SNPs for rheumatoid arthritis within CD4(+) regulatory T cells (P = 1 × 10(-4)), 67 SNPs for type 2 diabetes in pancreatic islet cells (P = 0.003) and the liver (P = 0.003), and 14 SNPs for neuropsychiatric disease in neuronal tissues (P = 0.007). We show how cell type-specific H3K4me3 peaks can inform the fine mapping of associated SNPs to identify causal variation.

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
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
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.
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.
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.
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.
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.