gAIRR-wgs: high-resolution T cell receptor allele typing in biobank-scale whole-genome sequencing data

Abstract

T cell receptor (TR) genes are essential components of the adaptive immune receptor repertoire, and growing evidence links TR germline variants to immune-related diseases. However, their highly allelic diversity and sequence homology make them challenging dark regions of the human genome. Current TR genotyping tools have limited support for population-scale studies using standard-depth (30×) whole-genome sequencing (WGS), leaving a critical gap. We present germline Adaptive Immune Receptor Repertoire (gAIRR)-wgs, the first highly resource-efficient workflow specifically designed for high-resolution TR allele typing from short-read WGS. Benchmarking against 44 assembly-validated Human Pangenome Reference Consortium Release 1 subjects showed high overall accuracy performance across TR loci (mean F1/accuracy: 0.996/0.997), with comparable results in an independent cohort of 182 Release 2 individuals (0.984/0.988). Applying gAIRR-wgs to 1492 Taiwan Biobank (TWB) participants, we identified 450 novel TR alleles absent from the international ImMunoGeneTics (IMGT) information system database, accounting for 57.5% of all identified TR alleles and representing an ~102% expansion of the current IMGT TR repertoire—277 of which were cross-validated in non-East Asian cohorts—and 109 novel TR V alleles with allele frequencies >1% in the TWB. Notably, the tool uncovered population-specific structural polymorphisms, including T cell receptor gamma variable (TRGV) genes (TRGV4/TRGV5 deletions) and T cell receptor beta variable (TRBV) genes (TRBV3-2/TRBV4-3 insertion/deletion), which were overlooked by Illumina Dynamic Read Analysis for GENomics (DRAGEN). Furthermore, we identified 34 TR genes exhibiting significant allelic divergence between Taiwanese and global populations. By enabling accurate TR genotyping from 30× WGS data, gAIRR-wgs effectively unlocks the immunogenomic potential of massive biobank resources, bridging the gap between standard genomic surveys and adaptive immune repertoire analysis.