Linkage Disequilibrium in Targeted Sequencing

Q3 Mathematics
D. Romanov, N. Skoblikow
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引用次数: 0

Abstract

We propose an approach for optimizing the development of gene diagnostic panels, which is based on the construction of non-equilibrium linkage maps. In the process of gene selection we essentially use genome-wide association analysis (GWAS). Whole-genome analysis of associations makes it possible to reveal the relationship of genomic variants with the studied phenotype. However, the nucleotide variants that showed the highest degree of association can only be statistically associated with the phenotype, not being the true cause of the phenotype. In this case, they may be in the block of linked inheritance with nucleotide variants that really affect the manifestation of the phenotype. The construction of maps of non-equilibrium linkage of nucleotides makes it possible to optimally determine the boundaries of linkage blocks, in which the desired variants fall. The aim of this study was to optimize the demarcation of genomic loci to create targeted panels aimed at predicting susceptibility to SARS-CoV-2 and the severity of COVID-19. The proposed method for selecting loci for a target panel, taking into account nonequilibrium linkage, makes it possible to use the phenomenon of nonequilibrium linkage in order to maximally cover the regions involved in the development of the phenotype, while simultaneously minimizing the length of these regions, and, at the same time, the cost of sequencing.
靶向测序中的连锁不平衡
本文提出了一种基于非平衡连锁图谱构建的基因诊断面板优化开发方法。在基因选择过程中,我们主要使用全基因组关联分析(GWAS)。全基因组关联分析使得揭示基因组变异与所研究表型的关系成为可能。然而,显示出最高关联程度的核苷酸变异只能在统计学上与表型相关,而不是表型的真正原因。在这种情况下,它们可能位于与核苷酸变异相关的遗传块中,而核苷酸变异真正影响了表型的表现。核苷酸的非平衡连锁图谱的构建使其有可能以最佳方式确定连锁块的边界,其中期望的变体落在。本研究的目的是优化基因组位点的划分,以创建旨在预测SARS-CoV-2易感性和COVID-19严重程度的靶向小组。所提出的选择靶板基因座的方法考虑了非平衡连锁,使得利用非平衡连锁现象来最大限度地覆盖涉及表型发展的区域成为可能,同时最小化这些区域的长度,同时降低测序成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Biology and Bioinformatics
Mathematical Biology and Bioinformatics Mathematics-Applied Mathematics
CiteScore
1.10
自引率
0.00%
发文量
13
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