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1.  Genetics of rheumatoid arthritis contributes to biology and drug discovery 
Okada, Yukinori | Wu, Di | Trynka, Gosia | Raj, Towfique | Terao, Chikashi | Ikari, Katsunori | Kochi, Yuta | Ohmura, Koichiro | Suzuki, Akari | Yoshida, Shinji | Graham, Robert R. | Manoharan, Arun | Ortmann, Ward | Bhangale, Tushar | Denny, Joshua C. | Carroll, Robert J. | Eyler, Anne E. | Greenberg, Jeffrey D. | Kremer, Joel M. | Pappas, Dimitrios A. | Jiang, Lei | Yin, Jian | Ye, Lingying | Su, Ding-Feng | Yang, Jian | Xie, Gang | Keystone, Ed | Westra, Harm-Jan | Esko, Tõnu | Metspalu, Andres | Zhou, Xuezhong | Gupta, Namrata | Mirel, Daniel | Stahl, Eli A. | Diogo, Dorothée | Cui, Jing | Liao, Katherine | Guo, Michael H. | Myouzen, Keiko | Kawaguchi, Takahisa | Coenen, Marieke J.H. | van Riel, Piet L.C.M. | van de Laar, Mart A.F.J. | Guchelaar, Henk-Jan | Huizinga, Tom W.J. | Dieudé, Philippe | Mariette, Xavier | Bridges, S. Louis | Zhernakova, Alexandra | Toes, Rene E.M. | Tak, Paul P. | Miceli-Richard, Corinne | Bang, So-Young | Lee, Hye-Soon | Martin, Javier | Gonzalez-Gay, Miguel A. | Rodriguez-Rodriguez, Luis | Rantapää-Dahlqvist, Solbritt | Ärlestig, Lisbeth | Choi, Hyon K. | Kamatani, Yoichiro | Galan, Pilar | Lathrop, Mark | Eyre, Steve | Bowes, John | Barton, Anne | de Vries, Niek | Moreland, Larry W. | Criswell, Lindsey A. | Karlson, Elizabeth W. | Taniguchi, Atsuo | Yamada, Ryo | Kubo, Michiaki | Liu, Jun S. | Bae, Sang-Cheol | Worthington, Jane | Padyukov, Leonid | Klareskog, Lars | Gregersen, Peter K. | Raychaudhuri, Soumya | Stranger, Barbara E. | De Jager, Philip L. | Franke, Lude | Visscher, Peter M. | Brown, Matthew A. | Yamanaka, Hisashi | Mimori, Tsuneyo | Takahashi, Atsushi | Xu, Huji | Behrens, Timothy W. | Siminovitch, Katherine A. | Momohara, Shigeki | Matsuda, Fumihiko | Yamamoto, Kazuhiko | Plenge, Robert M.
Nature  2013;506(7488):376-381.
A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.
doi:10.1038/nature12873
PMCID: PMC3944098  PMID: 24390342
2.  Heritability and Genome-wide Association Study To Assess Genetic Differences Between Advanced Age-Related Macular Degeneration Subtypes  
Ophthalmology  2012;119(9):1874-1885.
Purpose
To investigate whether the two 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 two subtypes.
Design
Sibling correlation study and genome-wide association study (GWAS)
Participants
For the sibling correlation study, we included 209 sibling pairs with advanced AMD. For the GWAS, we included 2594 participants with advanced AMD subtypes and 4134 controls. Replication cohorts included 5383 advanced AMD participants and 15,240 controls.
Methods
Participants had AMD grade assigned based on fundus photography and/or examination. To determine heritability of advanced AMD subtypes, we performed a sibling correlation study. For the GWAS, we conducted genome-wide genotyping and imputed 6,036,699 single nucleotide polymorphism (SNPs). We then analyzed SNPs 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 x 10−5) meaning that siblings of probands with CNV or GA are more likely to develop CNV or GA, respectively. In the analysis comparing participants with CNV to those with GA, we observed a statistically significant association 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 x 10−14 for combined discovery and replication analysis).
Conclusions
Whether a patient with AMD develops CNV vs. GA 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 which differ for advanced AMD subtypes and deserve follow-up in additional studies.
doi:10.1016/j.ophtha.2012.03.014
PMCID: PMC3899891  PMID: 22705344
3.  GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies 
Bioinformatics  2012;28(24):3329-3331.
Summary: GWASTools is an R/Bioconductor package for quality control and analysis of genome-wide association studies (GWAS). GWASTools brings the interactive capability and extensive statistical libraries of R to GWAS. Data are stored in NetCDF format to accommodate extremely large datasets that cannot fit within R’s memory limits. The documentation includes instructions for converting data from multiple formats, including variants called from sequencing. GWASTools provides a convenient interface for linking genotypes and intensity data with sample and single nucleotide polymorphism annotation.
Availability and implementation: GWASTools is implemented in R and is available from Bioconductor (http://www.bioconductor.org). An extensive vignette detailing a recommended work flow is included.
Contact: sdmorris@uw.edu
doi:10.1093/bioinformatics/bts610
PMCID: PMC3519456  PMID: 23052040
4.  Association of NCF2, IKZF1, IRF8, IFIH1, and TYK2 with Systemic Lupus Erythematosus 
PLoS Genetics  2011;7(10):e1002341.
Systemic lupus erythematosus (SLE) is a complex trait characterised by the production of a range of auto-antibodies and a diverse set of clinical phenotypes. Currently, ∼8% of the genetic contribution to SLE in Europeans is known, following publication of several moderate-sized genome-wide (GW) association studies, which identified loci with a strong effect (OR>1.3). In order to identify additional genes contributing to SLE susceptibility, we conducted a replication study in a UK dataset (870 cases, 5,551 controls) of 23 variants that showed moderate-risk for lupus in previous studies. Association analysis in the UK dataset and subsequent meta-analysis with the published data identified five SLE susceptibility genes reaching genome-wide levels of significance (Pcomb<5×10−8): NCF2 (Pcomb = 2.87×10−11), IKZF1 (Pcomb = 2.33×10−9), IRF8 (Pcomb = 1.24×10−8), IFIH1 (Pcomb = 1.63×10−8), and TYK2 (Pcomb = 3.88×10−8). Each of the five new loci identified here can be mapped into interferon signalling pathways, which are known to play a key role in the pathogenesis of SLE. These results increase the number of established susceptibility genes for lupus to ∼30 and validate the importance of using large datasets to confirm associations of loci which moderately increase the risk for disease.
Author Summary
Genome-wide association studies have revolutionised our ability to identify common susceptibility alleles for systemic lupus erythematosus (SLE). In complex diseases such as SLE, where many different genes make a modest contribution to disease susceptibility, it is necessary to perform large-scale association studies to combine results from several datasets, to have sufficient power to identify highly significant novel loci (P<5×10−8). Using a large SLE collection of 870 UK SLE cases and 5,551 UK unaffected individuals, we firstly replicated ten moderate-risk alleles (P<0.05) from a US–Swedish study of 3,273 SLE cases and 12,188 healthy controls. Combining our results with the US-Swedish data identified five new loci, which crossed the level for genome-wide significance: NCF2 (neutrophil cytosolic factor 2), IKZF1 (Ikaros family zinc-finger 1), IRF8 (interferon regulatory factor 8), IFIH1 (interferon-induced helicase C domain-containing protein 1), and TYK2 (tyrosine kinase 2). Each of these five genes regulates a different aspect of the immune response and contributes to the production of type-I and type-II interferons. Although further studies will be required to identify the causal alleles within these loci, the confirmation of five new susceptibility genes for lupus makes a significant step forward in our understanding of the genetic contribution to SLE.
doi:10.1371/journal.pgen.1002341
PMCID: PMC3203198  PMID: 22046141
5.  Quality control and quality assurance in genotypic data for genome-wide association studies 
Genetic epidemiology  2010;34(6):591-602.
Genome-wide scans of nucleotide variation in human subjects are providing an increasing number of replicated associations with complex disease traits. Most of the variants detected have small effects and, collectively, they account for a small fraction of the total genetic variance. Very large sample sizes are required to identify and validate findings. In this situation, even small sources of systematic or random error can cause spurious results or obscure real effects. The need for careful attention to data quality has been appreciated for some time in this field, and a number of strategies for quality control and quality assurance (QC/QA) have been developed. Here we extend these methods and describe a system of QC/QA for genotypic data in genome-wide association studies. This system includes some new approaches that (1) combine analysis of allelic probe intensities and called genotypes to distinguish gender misidentification from sex chromosome aberrations, (2) detect autosomal chromosome aberrations that may affect genotype calling accuracy, (3) infer DNA sample quality from relatedness and allelic intensities, (4) use duplicate concordance to infer SNP quality, (5) detect genotyping artifacts from dependence of Hardy-Weinberg equilibrium (HWE) test p-values on allelic frequency, and (6) demonstrate sensitivity of principal components analysis (PCA) to SNP selection. The methods are illustrated with examples from the ‘Gene Environment Association Studies’ (GENEVA) program. The results suggest several recommendations for QC/QA in the design and execution of genome-wide association studies.
doi:10.1002/gepi.20516
PMCID: PMC3061487  PMID: 20718045
GWAS; DNA sample quality; genotyping artifact; Hardy-Weinberg equilibrium; chromosome aberration
6.  Common variants near FRK/COL10A1 and VEGFA are associated with advanced age-related macular degeneration 
Human Molecular Genetics  2011;20(18):3699-3709.
Despite significant progress in the identification of genetic loci for age-related macular degeneration (AMD), not all of the heritability has been explained. To identify variants which contribute to the remaining genetic susceptibility, we performed the largest meta-analysis of genome-wide association studies to date for advanced AMD. We imputed 6 036 699 single-nucleotide polymorphisms with the 1000 Genomes Project reference genotypes on 2594 cases and 4134 controls with follow-up replication of top signals in 5640 cases and 52 174 controls. We identified two new common susceptibility alleles, rs1999930 on 6q21-q22.3 near FRK/COL10A1 [odds ratio (OR) 0.87; P = 1.1 × 10−8] and rs4711751 on 6p12 near VEGFA (OR 1.15; P = 8.7 × 10−9). In addition to the two novel loci, 10 previously reported loci in ARMS2/HTRA1 (rs10490924), CFH (rs1061170, and rs1410996), CFB (rs641153), C3 (rs2230199), C2 (rs9332739), CFI (rs10033900), LIPC (rs10468017), TIMP3 (rs9621532) and CETP (rs3764261) were confirmed with genome-wide significant signals in this large study. Loci in the recently reported genes ABCA1 and COL8A1 were also detected with suggestive evidence of association with advanced AMD. The novel variants identified in this study suggest that angiogenesis (VEGFA) and extracellular collagen matrix (FRK/COL10A1) pathways contribute to the development of advanced AMD.
doi:10.1093/hmg/ddr270
PMCID: PMC3159552  PMID: 21665990
7.  Hundreds of variants clustered in genomic loci and biological pathways affect human height 
Lango Allen, Hana | Estrada, Karol | Lettre, Guillaume | Berndt, Sonja I. | Weedon, Michael N. | Rivadeneira, Fernando | Willer, Cristen J. | Jackson, Anne U. | Vedantam, Sailaja | Raychaudhuri, Soumya | Ferreira, Teresa | Wood, Andrew R. | Weyant, Robert J. | Segrè, Ayellet V. | Speliotes, Elizabeth K. | Wheeler, Eleanor | Soranzo, Nicole | Park, Ju-Hyun | Yang, Jian | Gudbjartsson, Daniel | Heard-Costa, Nancy L. | Randall, Joshua C. | Qi, Lu | Smith, Albert Vernon | Mägi, Reedik | Pastinen, Tomi | Liang, Liming | Heid, Iris M. | Luan, Jian'an | Thorleifsson, Gudmar | Winkler, Thomas W. | Goddard, Michael E. | Lo, Ken Sin | Palmer, Cameron | Workalemahu, Tsegaselassie | Aulchenko, Yurii S. | Johansson, Åsa | Zillikens, M.Carola | Feitosa, Mary F. | Esko, Tõnu | Johnson, Toby | Ketkar, Shamika | Kraft, Peter | Mangino, Massimo | Prokopenko, Inga | Absher, Devin | Albrecht, Eva | Ernst, Florian | Glazer, Nicole L. | Hayward, Caroline | Hottenga, Jouke-Jan | Jacobs, Kevin B. | Knowles, Joshua W. | Kutalik, Zoltán | Monda, Keri L. | Polasek, Ozren | Preuss, Michael | Rayner, Nigel W. | Robertson, Neil R. | Steinthorsdottir, Valgerdur | Tyrer, Jonathan P. | Voight, Benjamin F. | Wiklund, Fredrik | Xu, Jianfeng | Zhao, Jing Hua | Nyholt, Dale R. | Pellikka, Niina | Perola, Markus | Perry, John R.B. | Surakka, Ida | Tammesoo, Mari-Liis | Altmaier, Elizabeth L. | Amin, Najaf | Aspelund, Thor | Bhangale, Tushar | Boucher, Gabrielle | Chasman, Daniel I. | Chen, Constance | Coin, Lachlan | Cooper, Matthew N. | Dixon, Anna L. | Gibson, Quince | Grundberg, Elin | Hao, Ke | Junttila, M. Juhani | Kaplan, Lee M. | Kettunen, Johannes | König, Inke R. | Kwan, Tony | Lawrence, Robert W. | Levinson, Douglas F. | Lorentzon, Mattias | McKnight, Barbara | Morris, Andrew P. | Müller, Martina | Ngwa, Julius Suh | Purcell, Shaun | Rafelt, Suzanne | Salem, Rany M. | Salvi, Erika | Sanna, Serena | Shi, Jianxin | Sovio, Ulla | Thompson, John R. | Turchin, Michael C. | Vandenput, Liesbeth | Verlaan, Dominique J. | Vitart, Veronique | White, Charles C. | Ziegler, Andreas | Almgren, Peter | Balmforth, Anthony J. | Campbell, Harry | Citterio, Lorena | De Grandi, Alessandro | Dominiczak, Anna | Duan, Jubao | Elliott, Paul | Elosua, Roberto | Eriksson, Johan G. | Freimer, Nelson B. | Geus, Eco J.C. | Glorioso, Nicola | Haiqing, Shen | Hartikainen, Anna-Liisa | Havulinna, Aki S. | Hicks, Andrew A. | Hui, Jennie | Igl, Wilmar | Illig, Thomas | Jula, Antti | Kajantie, Eero | Kilpeläinen, Tuomas O. | Koiranen, Markku | Kolcic, Ivana | Koskinen, Seppo | Kovacs, Peter | Laitinen, Jaana | Liu, Jianjun | Lokki, Marja-Liisa | Marusic, Ana | Maschio, Andrea | Meitinger, Thomas | Mulas, Antonella | Paré, Guillaume | Parker, Alex N. | Peden, John F. | Petersmann, Astrid | Pichler, Irene | Pietiläinen, Kirsi H. | Pouta, Anneli | Ridderstråle, Martin | Rotter, Jerome I. | Sambrook, Jennifer G. | Sanders, Alan R. | Schmidt, Carsten Oliver | Sinisalo, Juha | Smit, Jan H. | Stringham, Heather M. | Walters, G.Bragi | Widen, Elisabeth | Wild, Sarah H. | Willemsen, Gonneke | Zagato, Laura | Zgaga, Lina | Zitting, Paavo | Alavere, Helene | Farrall, Martin | McArdle, Wendy L. | Nelis, Mari | Peters, Marjolein J. | Ripatti, Samuli | van Meurs, Joyce B.J. | Aben, Katja K. | Ardlie, Kristin G | Beckmann, Jacques S. | Beilby, John P. | Bergman, Richard N. | Bergmann, Sven | Collins, Francis S. | Cusi, Daniele | den Heijer, Martin | Eiriksdottir, Gudny | Gejman, Pablo V. | Hall, Alistair S. | Hamsten, Anders | Huikuri, Heikki V. | Iribarren, Carlos | Kähönen, Mika | Kaprio, Jaakko | Kathiresan, Sekar | Kiemeney, Lambertus | Kocher, Thomas | Launer, Lenore J. | Lehtimäki, Terho | Melander, Olle | Mosley, Tom H. | Musk, Arthur W. | Nieminen, Markku S. | O'Donnell, Christopher J. | Ohlsson, Claes | Oostra, Ben | Palmer, Lyle J. | Raitakari, Olli | Ridker, Paul M. | Rioux, John D. | Rissanen, Aila | Rivolta, Carlo | Schunkert, Heribert | Shuldiner, Alan R. | Siscovick, David S. | Stumvoll, Michael | Tönjes, Anke | Tuomilehto, Jaakko | van Ommen, Gert-Jan | Viikari, Jorma | Heath, Andrew C. | Martin, Nicholas G. | Montgomery, Grant W. | Province, Michael A. | Kayser, Manfred | Arnold, Alice M. | Atwood, Larry D. | Boerwinkle, Eric | Chanock, Stephen J. | Deloukas, Panos | Gieger, Christian | Grönberg, Henrik | Hall, Per | Hattersley, Andrew T. | Hengstenberg, Christian | Hoffman, Wolfgang | Lathrop, G.Mark | Salomaa, Veikko | Schreiber, Stefan | Uda, Manuela | Waterworth, Dawn | Wright, Alan F. | Assimes, Themistocles L. | Barroso, Inês | Hofman, Albert | Mohlke, Karen L. | Boomsma, Dorret I. | Caulfield, Mark J. | Cupples, L.Adrienne | Erdmann, Jeanette | Fox, Caroline S. | Gudnason, Vilmundur | Gyllensten, Ulf | Harris, Tamara B. | Hayes, Richard B. | Jarvelin, Marjo-Riitta | Mooser, Vincent | Munroe, Patricia B. | Ouwehand, Willem H. | Penninx, Brenda W. | Pramstaller, Peter P. | Quertermous, Thomas | Rudan, Igor | Samani, Nilesh J. | Spector, Timothy D. | Völzke, Henry | Watkins, Hugh | Wilson, James F. | Groop, Leif C. | Haritunians, Talin | Hu, Frank B. | Kaplan, Robert C. | Metspalu, Andres | North, Kari E. | Schlessinger, David | Wareham, Nicholas J. | Hunter, David J. | O'Connell, Jeffrey R. | Strachan, David P. | Wichmann, H.-Erich | Borecki, Ingrid B. | van Duijn, Cornelia M. | Schadt, Eric E. | Thorsteinsdottir, Unnur | Peltonen, Leena | Uitterlinden, André | Visscher, Peter M. | Chatterjee, Nilanjan | Loos, Ruth J.F. | Boehnke, Michael | McCarthy, Mark I. | Ingelsson, Erik | Lindgren, Cecilia M. | Abecasis, Gonçalo R. | Stefansson, Kari | Frayling, Timothy M. | Hirschhorn, Joel N
Nature  2010;467(7317):832-838.
Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence phenotype. Genome-wide association (GWA) studies have identified >600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the utility of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P=0.016), and that underlie skeletal growth defects (P<0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants, and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented amongst variants that alter amino acid structure of proteins and expression levels of nearby genes. Our data explain ∼10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to ∼16% of phenotypic variation (∼20% of heritable variation). Although additional approaches are needed to fully dissect the genetic architecture of polygenic human traits, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.
doi:10.1038/nature09410
PMCID: PMC2955183  PMID: 20881960
8.  Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies 
PLoS ONE  2008;3(10):e3583.
A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS). True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses. We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes. The array utilizes a “cosmopolitan” tagging approach to capture the genetic diversity across ∼2,000 loci in populations represented in the HapMap and SeattleSNPs projects. The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching. The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples. We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations. DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations. These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions.
doi:10.1371/journal.pone.0003583
PMCID: PMC2571995  PMID: 18974833

Results 1-8 (8)