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To address this, we developed TCR2HLA, an open-source tool that infers HLA genotypes from TCR\u03b2 repertoires. Expanding on work linking public TRBV-CDR3 sequences to HLA genotypes, we incorporated \u201cquasi-public\u201d metaclonotypes \u2013 composed of rarer TCR\u03b2 sequences with shared amino acid features \u2013 enriched by HLA genotypes. Using four TCR\u03b2seq datasets from 3,150 individuals, we applied TRBV gene partitioning and locality-sensitive hashing to identify \u223c96,000 TCR\u03b2 features strongly associated with specific HLA alleles from 71M input TCRs. Binary HLA classifiers built with these features achieved high balanced accuracy (&gt;0.9) across common HLA-A (9\/12), B (9\/12), C (6\/13), DRB1 (11\/11) alleles and prevalent DPA1\/DPB1 (6\/10), DQA1\/DQB1 (8\/17) heterodimers. We also introduced a high-sensitivity calibration to support predictions in samples with as few as 5,000 unique clonotypes. Calibrated predictions with confidence filtering improved reliability. Beyond genotype imputation, TCR2HLA enables the discovery of novel HLA- and exposure-associated TCRs, as shown by the identification of SARS-CoV-2 related TCRs in a large COVID-19 dataset lacking HLA data. TCR2HLA provides a scalable framework for bridging the gap between TCRseq data and HLA genotype for biomarker discovery.<\/jats:p>","DOI":"10.1101\/2025.07.18.665436","type":"posted-content","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T19:30:12Z","timestamp":1753299012000},"source":"Crossref","is-referenced-by-count":0,"title":["TCR2HLA: calibrated inference of HLA genotypes from TCR repertoires enables identification of immunologically relevant 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