Abstract
Large population studies of immune system genes are essential for characterizing their role in diseases, including autoimmune conditions. Of key interest are a group of genes encoding the killer cell immunoglobulin-like receptors (KIRs), which have known and hypothesized roles in autoimmune diseases, resistance to viruses, reproductive conditions, and cancer. These genes are highly polymorphic, which makes typing expensive and time consuming. Consequently, despite their importance, KIRs have been little studied in large cohorts. Statistical imputation methods developed for other complex loci (e.g., human leukocyte antigen [HLA]) on the basis of SNP data provide an inexpensive high-throughput alternative to direct laboratory typing of these loci and have enabled important findings and insights for many diseases. We present KIR∗IMP, a method for imputation of KIR copy number. We show that KIR∗IMP is highly accurate and thus allows the study of KIRs in large cohorts and enables detailed investigation of the role of KIRs in human disease.
Highlights
Over the past decade, studies of the genetics of human disease have benefitted greatly from the interrogation of large cohorts of samples genotyped at hundreds of thousands of markers
We developed and validated killer cell immunoglobulin-like receptors (KIRs)*IMP by using data from two cohorts of European individuals typed for both SNP genotypes and KIR copy number
For the methods that operate on a per-haplotype basis (HLA*IMP:[01] and tag SNPs), we evaluated them by using both validation strategies described earlier
Summary
Studies of the genetics of human disease have benefitted greatly from the interrogation of large cohorts of samples genotyped at hundreds of thousands of markers. Formal genetics has extensively been replaced by the analysis of large amounts of SNP genotype or, more recently, sequence data, as the cost of obtaining such data has dramatically reduced, in part because of automation. In spite of these advances, some regions of the genome are refractory to detailed investigation because performing automated typing is difficult. This is because they are highly variable between individuals or because they exhibit copynumber variation (CNV). The application of statistical methods to typing alleles via linkage disequilibrium with combinations of adjacent SNPs, known as imputation, has allowed large numbers of samples to be typed with high accuracy[1,2,3,4,5] so that massive cohorts of affected and control individuals can be studied.[6,7,8]
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