Interpretable GWAS by linking clinical phenotypes to quantifiable immune repertoire components.

Area of Science:

• Immunogenomics
• Human genetics
• Computational biology

Background:

• Genome-Wide Association Studies (GWAS) struggle to link genetic variants to immune-related diseases and cancer phenotypes.
• The interpretability of most identified genetic variants remains low, hindering biological understanding.
• The T cell receptor (TCR) repertoire plays a crucial role in adaptive immunity and disease pathogenesis.

Purpose of the Study:

• To interpret genetic associations of human diseases by investigating quantitative components of the TCR repertoire.
• To develop a statistical model predicting TCR repertoire features from genetic variants.
• To explore the role of TCR specificity in autoimmune diseases and cancer outcomes.

Main Methods:

• Developed a statistical model to predict TCR repertoire components (clusters of TCR sequences with common antigen specificity) using genetic variants in TRB and HLA loci.
• Applied the model to over 300,000 individuals from the UK Biobank dataset.
• Analyzed associations between predicted TCR abundances and immune-related diseases, including autoimmune conditions and various cancers.

Main Results:

• Identified 2309 significant associations between TCR abundances and diverse immune diseases.
• Found that TCR clusters predicted to be pathogenic in autoimmune diseases were enriched for predicted autoantigen specificity.
• Discovered four TCR clusters associated with improved outcomes in distinct cancers, where traditional GWAS found no significant loci.

Conclusions:

• The study highlights the critical role of adaptive immune responses, specifically the TCR repertoire, in bridging the genotype-phenotype gap in GWAS.
• TCR repertoire analysis provides a novel approach to interpret genetic associations for immune-related diseases and cancer.
• Findings suggest potential for TCR-based biomarkers for disease risk and patient outcomes.