Graduate Group in Genomics and Computational Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
Center for Computational and Genomic Medicine, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Commun Biol. 2024 Oct 20;7(1):1357. doi: 10.1038/s42003-024-07010-x.
Bridging the gap between genotype and phenotype in GWAS studies is challenging. A multitude of genetic variants have been associated with immune-related diseases, including cancer, yet the interpretability of most variants remains low. Here, we investigate the quantitative components in the T cell receptor (TCR) repertoire, the frequency of clusters of TCR sequences predicted to have common antigen specificity, to interpret the genetic associations of diverse human diseases. We first developed a statistical model to predict the TCR components using variants in the TRB and HLA loci. Applying this model to over 300,000 individuals in the UK Biobank data, we identified 2309 associations between TCR abundances and various immune diseases. TCR clusters predicted to be pathogenic for autoimmune diseases were significantly enriched for predicted autoantigen-specificity. Moreover, four TCR clusters were associated with better outcomes in distinct cancers, where conventional GWAS cannot identify any significant locus. Collectively, our results highlight the integral role of adaptive immune responses in explaining the associations between genotype and phenotype.
在 GWAS 研究中，弥合基因型和表型之间的差距具有挑战性。大量的遗传变异与免疫相关疾病有关，包括癌症，但大多数变异的可解释性仍然很低。在这里，我们研究了 T 细胞受体 (TCR) 库中的定量成分，即预测具有共同抗原特异性的 TCR 序列簇的频率，以解释多种人类疾病的遗传关联。我们首先开发了一个统计模型，使用 TRB 和 HLA 基因座中的变体来预测 TCR 成分。将该模型应用于英国生物库数据中的 30 多万人，我们确定了 2309 个 TCR 丰度与各种免疫疾病之间的关联。预测对自身免疫性疾病具有致病性的 TCR 簇显著富集了预测的自身抗原特异性。此外，四个 TCR 簇与不同癌症的更好结果相关，而常规 GWAS 无法确定任何显著的基因座。总之，我们的结果强调了适应性免疫反应在解释基因型和表型之间关联中的重要作用。
