Saunders, P. M. | bioRxiv Science

bioRxiv2026

Structural variation shapes regulatory and evolutionary diversity at the HLA locus

The human leukocyte antigen (HLA) region is among the most polymorphic loci in the human genome and plays a central role in immune function, yet the contribution of structural variation to its genetic and regulatory diversity remains poorly characterised. Using 460 phased, near-complete human genome assemblies from globally diverse populations, we systematically mapped structural variation and gene content across the HLA locus. We show that the HLA region contains substantially more structural variation than any other region of chromosome 6. At the HLA class II locus, all individuals could be assigned to one of 13 distinct HLA-DR-DQ structural haplotypes, whereas the HLA-A region comprised four major haplotypes, which we found to be interspersed among non-human primate lineages. These structural haplotypes exhibit marked differences in population frequency and show increasing allelic diversity over European prehistory. Integration of Iso-Seq and RNA-Seq data revealed that structural haplotypes are associated with differences in HLA gene expression, suggesting that structural variation directly influences immune gene regulation. Together, our results identify structural variation as a key and previously underappreciated contributor to HLA regulatory diversity, with broad functional and evolutionary implications for human immunity. Manuscript summaryStructural variation drives HLA haplotype diversity and gene expression differences across global human populations.

bioinformatics↗

bioRxiv2024

Prediction of KIR3DL1/Human Leukocyte Antigen binding

KIR3DL1 is a polymorphic inhibitory Natural Killer (NK) cell receptor that recognizes Human Leukocyte Antigen (HLA) class I allotypes that contain the Bw4 motif. Structural analyses have shown that in addition to residues 77-83 that span the Bw4 motif, polymorphism at other sites throughout the HLA molecule can influence the interaction with KIR3DL1. Given the extensive polymorphism of both KIR3DL1 and HLA class I, we built a machine learning prediction model to describe the influence of allotypic variation on the binding of KIR3DL1 to HLA class I. Nine KIR3DL1 tetramers were screened for reactivity against a panel of HLA class I molecules which revealed different patterns of specificity for each KIR3DL1 allotype. Separate models were trained for each of KIR3DL1 allotypes based on the full amino sequence of exons 2 and 3 encoding the 1 and 2 domains of the class I HLA allotypes, the set of polymorphic positions that span the Bw4 motif, or the positions that encode 1 and 2 but exclude the connecting loops. The Multi-Label-Vector-Optimization (MLVO) model trained on all alpha helix positions performed best with AUC scores ranging from 0.74 to 0.974 for the 9 KIR3DL1 allotype models. We show that a binary division into binder and non-binder is not precise, and that intermediate levels exist. Using the same models, within the binder group, high- and low-binder categories can also be predicted, the regions in HLA affecting the high vs low binder being completely distinct from the classical Bw4 motif. We further show that these positions affect binding affinity in a nonadditive way and induce deviations from linear models used to predict interaction strength. We propose that this approach should be used in lieu of simpler binding models based on a single HLA motif.

immunology↗