Imputing HLA-G high-resolution alleles and regulatory haplotypes from exomes and SNP array data

Area of Science:

• Immunogenetics
• Genomic Medicine

Background:

• Human Leukocyte Antigen-G (HLA-G) is an immune checkpoint molecule with crucial roles in immune privilege and pathology.
• HLA-G exhibits limited coding diversity but significant regulatory variation, influencing its expression.
• Genetic variants in HLA-G are structured into haplotypes due to strong linkage disequilibrium.

Purpose of the Study:

• To develop and validate imputation models for predicting complete HLA-G haplotypes, including regulatory regions.
• To enable comprehensive HLA-G analysis from commonly available genomic data types like whole-exome sequencing and SNP arrays.
• To facilitate the study of HLA-G's role in various biological contexts without requiring full-gene sequencing.

Main Methods:

• Construction of multi-ethnic reference panels using data from 5,347 individuals across three cohorts.
• Development of HIBAG-based imputation models to predict HLA-G 4-field alleles, promoter, and 3'UTR haplotypes.
• Validation of models through cross-validation and independent datasets.

Main Results:

• High accuracy imputation models for HLA-G alleles and haplotypes were developed.
• Exome-based imputation achieved >99% accuracy for common alleles, with mean posterior probabilities >0.95.
• SNP array-based imputation demonstrated robust performance with >95% accuracy.

Conclusions:

• The developed imputation approach allows simultaneous prediction of coding and regulatory HLA-G sequences.
• This method enhances the utility of existing genomic datasets for HLA-G research.
• Facilitates investigation of HLA-G's involvement in immune regulation, transplantation, cancer, and pregnancy complications.