Negi, S.,Juyal, G.,Senapati, S.,Prasad, P.,Gupta, A.,Singh, S.,Kashyap, S.,Kumar, A.,Kumar, U.,Gupta, R.,Kaur, S.,Agrawal, S.,Aggarwal, A.,Ott, J. E.,Jain, S.,Juyal, R. C.,Thelma, B. K.

ObjectiveGenome-wide association studies (GWAS) and their subsequent meta-analyses have changed the landscape of genetics in rheumatoid arthritis (RA) by uncovering several novel genes. Such studies are heavily weighted by samples from Caucasian populations, but they explain only a small proportion of total heritability. Our previous studies in genetically distinct North Indian RA cohorts have demonstrated apparent allelic/genetic heterogeneity between North Indian and Western populations, warranting GWAS in non-European populations. We undertook this study to detect additional disease-associated loci that may be collectively important in the presence or absence of genes with a major effect. MethodsHigh-quality genotypes for >600,000 single-nucleotide polymorphisms (SNPs) in 706 RA patients and 761 controls from North India were generated in the discovery stage. Twelve SNPs showing suggestive association (P < 5 x 10(-5)) were then tested in an independent cohort of 927 RA patients and 1,148 controls. Additional disease-associated loci were determined using support vector machine (SVM) analyses. Fine-mapping of novel loci was performed by using imputation. ResultsIn addition to the expected association of the HLA locus with RA, we identified association with a novel intronic SNP of ARL15 (rs255758) on chromosome 5 (P-combined = 6.57 x 10(-6); odds ratio 1.42). Genotype-phenotype correlation by assaying adiponectin levels demonstrated the functional significance of this novel gene in disease pathogenesis. SVM analysis confirmed this association along with that of a few more replication stage genes. ConclusionIn this first GWAS of RA among North Indians, ARL15 emerged as a novel genetic risk factor in addition to the classic HLA locus, which suggests that population-specific genetic loci as well as those shared between Asian and European populations contribute to RA etiology. Furthermore, our study reveals the potential of machine learning methods in unraveling gene-gene interactions using GWAS data.