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Genetics of rheumatoid arthritis contributes to biology and drug discovery.


ABSTRACT: A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study 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. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2 - 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation, cis-acting expression quantitative trait loci and pathway analyses--as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout 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.

SUBMITTER: Okada Y 

PROVIDER: S-EPMC3944098 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Genetics of rheumatoid arthritis contributes to biology and drug discovery.

Okada Yukinori Y   Wu Di D   Trynka Gosia G   Raj Towfique T   Terao Chikashi C   Ikari Katsunori K   Kochi Yuta Y   Ohmura Koichiro K   Suzuki Akari A   Yoshida Shinji S   Graham Robert R RR   Manoharan Arun A   Ortmann Ward W   Bhangale Tushar T   Denny Joshua C JC   Carroll Robert J RJ   Eyler Anne E AE   Greenberg Jeffrey D JD   Kremer Joel M JM   Pappas Dimitrios A DA   Jiang Lei L   Yin Jian J   Ye Lingying L   Su Ding-Feng DF   Yang Jian J   Xie Gang G   Keystone Ed E   Westra Harm-Jan HJ   Esko Tõnu T   Metspalu Andres A   Zhou Xuezhong X   Gupta Namrata N   Mirel Daniel D   Stahl Eli A EA   Diogo Dorothée D   Cui Jing J   Liao Katherine K   Guo Michael H MH   Myouzen Keiko K   Kawaguchi Takahisa T   Coenen Marieke J H MJ   van Riel Piet L C M PL   van de Laar Mart A F J MA   Guchelaar Henk-Jan HJ   Huizinga Tom W J TW   Dieudé Philippe P   Mariette Xavier X   Bridges S Louis SL   Zhernakova Alexandra A   Toes Rene E M RE   Tak Paul P PP   Miceli-Richard Corinne C   Bang So-Young SY   Lee Hye-Soon HS   Martin Javier J   Gonzalez-Gay Miguel A MA   Rodriguez-Rodriguez Luis L   Rantapää-Dahlqvist Solbritt S   Arlestig Lisbeth L   Choi Hyon K HK   Kamatani Yoichiro Y   Galan Pilar P   Lathrop Mark M   Eyre Steve S   Bowes John J   Barton Anne A   de Vries Niek N   Moreland Larry W LW   Criswell Lindsey A LA   Karlson Elizabeth W EW   Taniguchi Atsuo A   Yamada Ryo R   Kubo Michiaki M   Liu Jun S JS   Bae Sang-Cheol SC   Worthington Jane J   Padyukov Leonid L   Klareskog Lars L   Gregersen Peter K PK   Raychaudhuri Soumya S   Stranger Barbara E BE   De Jager Philip L PL   Franke Lude L   Visscher Peter M PM   Brown Matthew A MA   Yamanaka Hisashi H   Mimori Tsuneyo T   Takahashi Atsushi A   Xu Huji H   Behrens Timothy W TW   Siminovitch Katherine A KA   Momohara Shigeki S   Matsuda Fumihiko F   Yamamoto Kazuhiko K   Plenge Robert M RM  

Nature 20131225 7488


A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA). Here we performed a genome-wide association study 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 polymo  ...[more]

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