Computational Methods for Sequencing and Interpreting Killer-cell Immunoglobulin-like Receptors (KIRs) at Multiple Resolutions
2021-01
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Computational Methods for Sequencing and Interpreting Killer-cell Immunoglobulin-like Receptors (KIRs) at Multiple Resolutions
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2021-01
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The era of personalized genetic medicine has arrived. It is now routine to sequence an individual’s DNA, either whole genome sequences (WGS) or targeted, as part of a patient’s medical plan. One of the exceptions is an ~200 kilobase region in chromosome 19 containing the killer-cell immunoglobulin-like receptors (KIR). These genes encode proteins that influence the actions of natural killer (NK) cells based on whether or not they bind with peptide-bound human leukocyte antigen receptors. This is evolutionary important to fight pathogens and mediate pregnancy. Modern medicine has correlated low-resolution KIR with many diseases and treatments, although the findings are often relatively vague and sometimes contradictory due to low resolution interpretation and/or small cohort sizes. Whether for personal medicine or population studies, the current best practices for KIR genotyping are to determine the presence/absence (PA) or copy number variation (CNV) of each gene using oligo- or primer-based polymerase chain reaction.The goal of our research is to advance DNA sequencing and interpretation of human KIR haplotypes. To that end, we have created algorithms and workflows to enhance interpretations of KIR at resolutions from PA genotyping via short-read WGS to full-haplotype assembly from long-read targeted or whole-genome sequences. First, we developed the first workflow to efficiently and accurately capture, sequence, assemble, and annotate full KIR haplotype sequences. As part of this workflow, we designed small sequences to capture the DNA fragments. Next, we use the alignment pattern of those short sequences across finished KIR haplotypes to define and annotate haplotype structures. The results show, for the first time, that the KIR region is composed of 9 genes in 14 loci. Next, we annotated all 68 reported human haplotypes, aligned them at the structural level, and then refined the alignment down the base level, providing the first KIR haplotype multiple sequence alignment. These efforts have led to this region being the best annotated and most diverse in the human genome reference. We next leveraged the MSA to discover PA markers and leveraged them in the first KIR WGS genotyping application. It was evaluated independently and reported to be at least 97% accurate.
These discoveries and inventions are the culmination of several computational methods we have developed that interpret KIR under different typing resolutions. This multi-resolution aspect is crucial to overall understanding; it improves resolution at any given level by leveraging references and/or markers from other resolutions. From low-resolution genotyping from any kind of DNA sequence to the first efficient full-haplotype assembly method, these results advance interpretation of this important genetic region to the personal and population levels.
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University of Minnesota Ph.D. dissertation. January 2021 Major: Biomedical Informatics and Computational Biology. Advisor: Rui Kuang. 1 computer file (PDF); 72 pages.
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Roe, David. (2021). Computational Methods for Sequencing and Interpreting Killer-cell Immunoglobulin-like Receptors (KIRs) at Multiple Resolutions. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/219420.
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