Ford Michael K B, Hari Ananth, Yeager Meredith, Mirabello Lisa, Chanock Stephen, Numanagić Ibrahim, Sahinalp S Cenk
National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA;
National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
Genome Res. 2025 Sep 2;35(9):2076-2086. doi: 10.1101/gr.280314.124.
As we enter the age of personalized medicine, healthcare is increasingly focused on tailoring diagnoses and treatments based on patients' genetic and environmental circumstances. A critical component of a person's physiological makeup is their immune system, but individual genetic variation in many immune system genes has remained resistant to analysis using classical whole-genome or targeted sequencing approaches. In particular, germline adaptive immune system genes, like immunoglobulin () and T cell receptor () genes, are particularly hard to genotype using classic reference-based methods owing to their highly repetitive and homologous nature. In this paper, we present ImmunoTyper2, a new computational toolkit for genotyping the variable genes of the lambda and kappa, and the loci with short-read whole genome sequence data, using an integer linear programming formulation, as an update to the ImmunoTyper-SR suite, which focused on region only. We evaluate its genotyping performance using Mendelian concordance analysis in 590 trios from the 1000 Genomes Project, benchmarking 40 samples against HPRC assembly-derived genotypes, and assessing robustness through sequencing depth analysis and parameter sensitivity tests. We introduce allele call confidence metrics to help quantify reliability. We also perform a prospective disease association study, applying ImmunoTyper2 to a WGS data set from a cohort of 461 COVID-19 patients from the COVNET Consortium to demonstrate how it can be applied to investigate genetic associations with disease.
随着我们进入个性化医疗时代，医疗保健越来越注重根据患者的基因和环境情况定制诊断和治疗方案。一个人的生理构成的关键组成部分是其免疫系统，但许多免疫系统基因的个体遗传变异一直难以通过经典的全基因组或靶向测序方法进行分析。特别是，种系适应性免疫系统基因，如免疫球蛋白（）和T细胞受体（）基因，由于其高度重复和同源的性质，使用基于经典参考的方法进行基因分型特别困难。在本文中，我们展示了ImmunoTyper2，这是一种新的计算工具包，用于使用整数线性规划公式，通过短读全基因组序列数据对lambda和kappa的可变基因以及loci进行基因分型，作为对仅专注于region的ImmunoTyper-SR套件的更新。我们使用来自千人基因组计划的590个三联体中的孟德尔一致性分析来评估其基因分型性能，以HPRC组装衍生的基因型为基准对40个样本进行测试，并通过测序深度分析和参数敏感性测试评估其稳健性。我们引入等位基因调用置信度指标以帮助量化可靠性。我们还进行了一项前瞻性疾病关联研究，将ImmunoTyper2应用于来自COVNET联盟的461名COVID-19患者队列的WGS数据集，以展示它如何用于研究与疾病的遗传关联。
