caa a22 4500 445861800 CHVBK 20180317145445.0 cr unu---uuuuu 170323e20110501xx s 000 0 eng 10.1007/s00439-010-0943-z doi (NATIONALLICENCE)springer-10.1007/s00439-010-0943-z A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility [Elektronische Daten] [Chunyu Liu, H. Ackerman, John Carulli] The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach "random forest” applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene-gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls. Springer-Verlag (outside the USA), 2011 Liu Chunyu Center for Population Studies and the Framingham Heart Study, NHLBI, 73 Mt. Wayte Avenue, Suite 2, 01702, Framingham, MA, USA aut Ackerman H. Department of Statistics, PhD Program, University of California at Berkeley, Berkeley, CA, USA aut Carulli John Genetics and Genomics Department, Biogen Idec, Cambridge, MA, USA aut Human Genetics Springer-Verlag 129/5(2011-05-01), 473-485 0340-6717 129:5<473 2011 129 439 https://doi.org/10.1007/s00439-010-0943-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00439-010-0943-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Chunyu Center for Population Studies and the Framingham Heart Study, NHLBI, 73 Mt. Wayte Avenue, Suite 2, 01702, Framingham, MA, USA aut NATIONALLICENCE 700 1- Ackerman H. Department of Statistics, PhD Program, University of California at Berkeley, Berkeley, CA, USA aut NATIONALLICENCE 700 1- Carulli John Genetics and Genomics Department, Biogen Idec, Cambridge, MA, USA aut NATIONALLICENCE 773 0- Human Genetics Springer-Verlag 129/5(2011-05-01), 473-485 0340-6717 129:5<473 2011 129 439 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer