A Genome-Wide Association Study Reveals ARL15, a Novel Non-HLA Susceptibility Gene for Rheumatoid Arthritis in North Indians

Authors


Abstract

Objective

Genome-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.

Methods

High-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 × 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.

Results

In 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 (Pcombined = 6.57 × 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.

Conclusion

In 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.

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