LILR genotype imputation with attribute bagging (LIBAG): leukocyte immunoglobulin-like receptor copy number imputation system

There are ten leukocyte immunoglobulin (Ig)-like receptor (<i>LILR</i>) genes, i.e., five genes encoding activating receptors (<i>LILRA1, LILRA2, LILRA4, LILRA5</i>, and <i>LILRA6</i>) characterized by their truncated cytoplasmic tails, and five genes encoding inhibitory receptors (<i>LILRB1, LILRB2, LILRB3, LILRB4</i>, and <i>LILRB5</i>) characterized by their extended cytoplasmic tails containing immunoreceptor tyrosine-based inhibitory motifs (ITIMs). Among these, <i>LILRB3</i>, <i>LILRA6</i>, and <i>LILRA3</i> are known for harboring high frequencies of copy number variations (CNVs). However, the presence of CNVs in the leukocyte receptor complex (LRC) region complicates single nucleotide polymorphism (SNP) association analysis within commercially available SNP microarray datasets. This study introduces LILR Genotype Imputation with Attribute Bagging (LIBAG), a novel method for determining CNVs in <i>LILRB3, LILRA6</i>, and <i>LILRA3</i> from commercially available SNP genotyping array datasets. <i>LILRA6</i> CNV imputation accuracy peaked at 98.0% for the Infinium Japanese Screening Array, followed by 97.4% for Axiom Japonica V2, 97.3% for Axiom Japonica Array NEO, and 94.3% for Axiom Japonica V1, with the lowest recorded accuracy of 93.6% for the Axiom Genome-wide ASI1 array. For the 1000 Genomes Project (1kGP) dataset, <i>LILRA6</i> CNV imputation achieved peak accuracies of 94.5% for 1kGP-EAS (East Asian), 86.6% for 1kGP-AMR (Admixed American), 83.8% for 1kGP-EUR European), and 75.0% for 1kGP-AFR (African), particularly after the 20 kb flanking region. Similarly, imputation accuracy for <i>LILRA3</i> CNV progressively increased, peaking at the 80 kb flanking region. Accuracy reached 1kGP-AMR, reaching 99.2% and 98.9% for 1kGP-AFR, 98.7% for 1kGP-EUR, and 97.5% for 1kGP-EAS. Investigating the <i>LILR</i> copy number (CN) in diseases associated with HLA class I molecules will provide further insights into disease pathogenesis.