Genome-Wide Detection of Disease-Associated Deletions in Case-Control Studies Using SNP Genotypes

Abstract

Genomic deletions have long been known to play a causative role in microdeletion syndromes. Recent whole-genome genetic studies have shown that deletions can increase the risk for psychiatric disorders, suggesting that genomic deletions play an important role in the genetic basis of complex traits. However, the association between genomic deletions and common, complex diseases has not yet been systematically investigated in gene mapping studies. In contrast with recent whole genome studies that focused on detecting genomic deletions in non-diseased individuals or were structured to be used in familial studies of parent-offspring trios, the purpose of this study is to develop statistical approaches for detecting genomic deletions associated with complex disease in case-control studies. Our methods are designed to be used with dense single nucleotide polymorphism (SNP) genotypes to detect deletions in whole genome scale. As more and more SNP genotype data for genome-wide association studies become available, development of sophisticated statistical approaches will be needed that use these data. A 2-stage design for detecting disease-associated deletions is proposed; the SNP-by-SNP analyses and cluster analyses based on combined evidence from multiple SNPs. We found that these approaches are useful for detecting disease-associated deletions and are robust in the presence of linkage disequilibrium using simulated SNP data sets. We applied the proposed statistical methods to whole genome 550K SNP genotype data for 868 rheumatoid arthritis patients and 1197 controls from the North American Rheumatoid Arthritis Consortium. We detected disease-associated deletions that encompassed HLA-DRB1 and C4 genes, in which genomic deletions were previously discovered in rheumatoid arthritis patients.