Genome-wide association identifies diverse causes of common variable immunodeficiency. - Orange, J; Glessner, J; Resnick, E; Sullivan, K; Lucas, M; Ferry, B; Kim, C; Hou, C; Wang, F; Chiavacci, R; Kugathasan, S; Sleasman, J; et al. (2011)
BACKGROUND: Common variable immunodeficiency (CVID) is a heterogeneous immune defect characterized by hypogammaglobulinemia, failure of specific antibody production, susceptibility to infections, and an array of comorbidities. OBJECTIVE: To address the underlying immunopathogenesis of CVID and comorbidities, we conducted the first genome-wide association and gene copy number variation (CNV) study in patients with CVID. METHODS: Three hundred sixty-three patients with CVID from 4 study sites were genotyped with 610,000 single nucleotide polymorphisms (SNPs). Patients were divided into a discovery cohort of 179 cases in comparison with 1,917 control subjects and a replication cohort of 109 cases and 1,114 control subjects. RESULTS: Our analyses detected strong association with the MHC region and association with a disintegrin and metalloproteinase (ADAM) genes (P combined = 1.96 × 10(-7)) replicated in the independent cohort. CNV analysis defined 16 disease-associated deletions and duplications, including duplication of origin recognition complex 4L (ORC4L) that was unique to 15 cases (P = 8.66 × 10(-16)), as well as numerous unique rare intraexonic deletions and duplications suggesting multiple novel genetic causes of CVID. Furthermore, the 1,000 most significant SNPs were strongly predictive of the CVID phenotype by using a Support Vector Machine algorithm with positive and negative predictive values of 1.0 and 0.957, respectively. CONCLUSION: Our integrative genome-wide analysis of SNP genotypes and CNVs has uncovered multiple novel susceptibility loci for CVID, both common and rare, which is consistent with the highly heterogeneous nature of CVID. These results provide new mechanistic insights into immunopathogenesis based on these unique genetic variations and might allow for improved diagnosis of CVID based on accurate prediction of the CVID clinical phenotypes by using our Support Vector Machine model.