Considerations for using population frequency data in germline variant interpretation: Cancer syndrome genes as a model

生物 1000基因组计划 基因组学 等位基因频率 基因组 生殖系 计算生物学 遗传学 种系突变 DNA测序 人口 基因 癌症 外显子组 突变 支票2 外显子组测序 等位基因 单核苷酸多态性 基因型 人口学 社会学
作者
Aimee L. Davidson,Conrad Leonard,Lambros T. Koufariotis,Michael T. Parsons,Georgina E. Hollway,John V. Pearson,Felicity Newell,Nicola Waddell,Amanda B. Spurdle
出处
期刊:Human Mutation [Wiley]
卷期号:42 (5): 530-536 被引量:5
标识
DOI:10.1002/humu.24183
摘要

Aggregate population genomics data from large cohorts are vital for assessing germline variant pathogenicity. However, there are no specifications on how sequencing quality metrics should be considered, and whether exome-derived and genome-derived allele frequencies should be considered in isolation. Germline genome sequence data were simulated for nine read-depths to identify a minimum acceptable read-depth for detecting variants. gnomAD exome-derived and genome-derived datasets were assessed for read-depth, for six key cancer genes selected for variant curation by ClinGen expert panels. Non-Finnish European allele frequency (AF) or filter AF of coding variants in these genes, assigned into frequency bins using modified ACMG-AMP criteria, was compared between exome-derived and genome-derived datasets. A 30X read-depth achieved acceptable precision and recall for detection of substitutions, but poor recall for small insertions/deletions. Exome-derived and genome-derived datasets exhibited low read-depth for different gene exons. Individual variants were mostly assigned to non-divergent AF bins (>95%) or filter AF bins (>97%). Two major bin divergences were resolved by applying the minimal acceptable read-depth threshold. These findings show the importance of assessing read-depth separately for population datasets sourced from different short-read sequencing technologies before assigning a frequency-based ACMG-AMP classification code for variant interpretation.
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