Refined HLA Linkage Disequilibrium Architectures of World Populations by a Novel Allelic Correlation Measure

TL;DR

This paper introduces a new measure called CICC for analyzing complex linkage disequilibrium in the HLA region, revealing novel high-LD areas across global populations and improving genetic association studies.

Contribution

The study presents the CICC measure, a novel approach that better captures HLA LD structures by addressing limitations of existing methods, across diverse populations.

Findings

Identified 10 novel high-LD regions in HLA using CICC.

Detected nine strongly linked regions shared across five global populations.

Demonstrated CICC's effectiveness in capturing complex LD structures.

Abstract

Numerous diseases, particularly autoimmune disorders, are associated with the human leukocyte antigen (HLA), a small genomic region located on human chromosome 6. Adequate characterization of linkage disequilibrium (LD) in the HLA across populations is crucial for identifying genetic markers associated with specific traits and phenotypes. However, current LD measures often fail to capture HLA's structural complexity due to methodological limitations and sensitivity to low-frequency variants, marginal allele frequencies, and haplotype composition. To address these challenges, we introduced the Conditional Informatics Correlation Coefficient (CICC), which integrates conditional probability, information content, and haplotype-aware XOR logic to quantify LD robustly. When applied to high-resolution haploid genomes from the Human Pangenome Reference Consortium (HPRC), CICC revealed 10 novel high-LD regions in HLA. Further analyses using the 1000 Genomes Project and Genome Asia datasets identified nine strongly linked regions shared across five global populations-five in Class I and four in Class II. These results demonstrate CICC's ability to capture complex HLA LD structures across populations, highlighting its broad potential for disease gene mapping, population genomics, and guiding precision medicine.