TIGP (BIO)—Capturing the Stars: Pharmacogenomics and Immunogenomics as My Targets

Abstract

Next-generation sequencing (NGS) has transformed genomics and precision medicine, reducing sequencing costs and enabling routine clinical application. Yet certain portions of the human genome remain difficult to interrogate because of high homology, segmental duplications, pseudogenes, structural variation and repeat expansions. These “black holes” or dark regions include clinically important pharmacogenomic genes such as CYP2D6, key immunogenomic loci such as HLA, KIR and the germline genes of adaptive immune receptor repertoire (AIRR, including BCR and TCR genes), as well as gene–pseudogene pairs like CYP21A2 and SMN1 and repeat expansion or structurally complex loci. Standard short-read workflows frequently misalign or under-cover these regions, creating blind spots that limit variant discovery, association studies and clinical implementation. 

This seminar will highlight how capture-based targeted enrichment combined with novel bioinformatics can address these challenges. By carefully designing probes and integrating algorithms for allele calling, copy number estimation, phasing and structural variant interpretation, we can resolve complex pharmacogenes and immunogenes at high resolution. I will present examples from our pharmacogenomics work, including the identification of actionable variants in genes such as CYP2C19 and CYP2D6 and their incorporation into clinical decision support systems, as well as our discovery that severe drug reactions like antithyroid drug-induced agranulocytosis are strongly associated with specific HLA alleles. 

I will also discuss our contributions in immunogenomics, including development of the gAIRR Suite for germline AIRR profiling, a capture-based gAIRR genotyping pipeline, as well as KIR annotation using Human Pangenome Reference Consortium assemblies. These efforts have enabled the discovery of hundreds of novel T cell receptor alleles, construction of comprehensive flanking sequence resources and improved typing of highly polymorphic immune loci. Finally, I will outline how bioinformatics and artificial intelligence, integrated into clinical decision support systems, can help translate these advances into precision prescribing, disease risk prediction, immune monitoring and future multi omics applications that bioinformatics PhD researchers can help build.  

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