Evaluation of HLA-DRB1 imputation using a Finnish dataset

Area of Science:

• Genetics and Genomics
• Immunogenetics
• Computational Biology

Background:

• High-resolution human leukocyte antigen (HLA) typing is essential for various medical applications but is costly and time-consuming due to the high number of alleles.
• Computational methods for HLA imputation aim to reduce costs and time by predicting HLA alleles from genetic data.

Purpose of the Study:

• To evaluate the reliability of two computational tools, HLA*IMP and SNP2HLA, for imputing HLA-DRB1 alleles.
• To assess the imputation accuracy in a specific Finnish population, considering its unique genetic background.

Main Methods:

• Tested HLA*IMP and SNP2HLA software for imputing HLA-DRB1 alleles in a Finnish cohort of 161 individuals.
• Analyzed per-individual success rates and identified commonly misimputed alleles.

Main Results:

• Both HLA*IMP and SNP2HLA demonstrated low per-individual success rates for HLA-DRB1 imputation, ranging from 16.68% to 25.4%.
• The common HLA-DRB1*01:01 allele was frequently misimputed, with an approximate 30% success rate.
• Finnish population's distinct haplotype frequencies, shaped by isolation and migration, may influence imputation accuracy compared to broader European datasets.

Conclusions:

• Current computational imputation methods show limited reliability for HLA-DRB1 alleles in the Finnish population.
• The findings underscore the critical need for population-specific reference panels to improve the accuracy of HLA imputation.