MHCnvex: Likelihood-based model for calling the copy number variations and loss of heterozygosity in MHC class I and II locus

Abstract

Motivation: Accurate detection of copy number variation (CNV) and loss of heterozygosity (LOH) in the major histocompatibility complex (MHC) locus is of great significance to both clinicians and researchers since it has the potential to inform treatment decisions, particularly in the context of immunotherapy. However, due to the high level of polymorphism in this region, calling copy number variations is a challenging task and requires special methodology. To address this challenge, we have developed a tool with a wide range of applicability to call CNV and LOH in the MHC region. Results: To address the challenge mentioned above, we have developed MHCnvex, an algorithm that accurately calls haplotype level CNVs for the genes in the MHC class I and II locus. MHCnvex presents a novel approach based on likelihood models for detecting CNVs at haplotype level. Additionally, this method integrates the MHC locus with other adjacent loci from the short arm of chromosome 6 to enhance the accuracy of the calls. The incorporation of a statistical approach and the examination of the broader chromosome 6 region, rather than just the MHC locus alone, make MHCnvex less vulnerable to local coverage biases (commonly associated with MHC locus). The performance of MHCnvex has been evaluated according to different measures including concordance with MHC flanking regions and changes in the allelic expression of MHC genes due to alteration. MHCnvex has also shown to significantly reduce the variability of calculated coverage for CNV analysis. Availability: Implementation of MHCnvex algorithm is available as an R package at: https://github.com/NoshadHo/MHCnvex