采用基于功率的滑动窗口法评估核苷酸序列或折叠蛋白空间位置中罕见遗传变异的临床影响。

IF 3.3 Q2 GENETICS & HEREDITY
HGG Advances Pub Date : 2024-07-18 Epub Date: 2024-03-19 DOI:10.1016/j.xhgg.2024.100284
Elizabeth T Cirulli, Kelly M Schiabor Barrett, Alexandre Bolze, Daniel P Judge, Pamala A Pawloski, Joseph J Grzymski, William Lee, Nicole L Washington
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引用次数: 0

摘要

即使在基因与表型之间存在既定关联的情况下,系统性地确定新型变体的致病性仍是一项重大挑战。在这里,我们介绍一种滑动窗口技术--Power Window(PW),它能利用人群规模的临床基因组数据集确定基因的影响区域。通过根据变异携带者的数量而不是变异或核苷酸的数量来确定分析窗口的大小,可以保持统计能力不变,从而实现临床表型的定位并去除无关联的基因区域。可以通过滑动基因核苷酸序列(通过一维空间)或折叠蛋白质中氨基酸的位置(通过三维空间)来建立窗口。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A power-based sliding window approach to evaluate the clinical impact of rare genetic variants in the nucleotide sequence or the spatial position of the folded protein.

Systematic determination of novel variant pathogenicity remains a major challenge, even when there is an established association between a gene and phenotype. Here we present Power Window (PW), a sliding window technique that identifies the impactful regions of a gene using population-scale clinico-genomic datasets. By sizing analysis windows on the number of variant carriers, rather than the number of variants or nucleotides, statistical power is held constant, enabling the localization of clinical phenotypes and removal of unassociated gene regions. The windows can be built by sliding across either the nucleotide sequence of the gene (through 1D space) or the positions of the amino acids in the folded protein (through 3D space). Using a training set of 350k exomes from the UK Biobank (UKB), we developed PW models for well-established gene-disease associations and tested their accuracy in two independent cohorts (117k UKB exomes and 65k exomes sequenced at Helix in the Healthy Nevada Project, myGenetics, or In Our DNA SC studies). The significant models retained a median of 49% of the qualifying variant carriers in each gene (range 2%-98%), with quantitative traits showing a median effect size improvement of 66% compared with aggregating variants across the entire gene, and binary traits' odds ratios improving by a median of 2.2-fold. PW showcases that electronic health record-based statistical analyses can accurately distinguish between novel coding variants in established genes that will have high phenotypic penetrance and those that will not, unlocking new potential for human genomics research, drug development, variant interpretation, and precision medicine.

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来源期刊
HGG Advances
HGG Advances Biochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
4.30
自引率
4.50%
发文量
69
审稿时长
14 weeks
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