Abstract

Aggregate population genomics data from large cohorts is 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 was 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 or filter allele frequency of coding variants in these genes, assigned into frequency bins using modified ACMG-AMP criteria, were 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 the same allele frequency bin (>95%) or filter allele frequency bin (>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. This article is protected by copyright. All rights reserved.

Authors	Davidson, Aimee L; Leonard, Conrad; Koufariotis, Lambros T; Parsons, Michael T; Hollway, Georgina E; Pearson, John V; Newell, Felicity; Waddell, Nicola; Spurdle, Amanda B
Journal	HUMAN MUTATION
Pages	530-536
Volume	42
Date	1/01/2021
Grant ID
Funding Body
URL	http://www.ncbi.nlm.nih.gov/pubmed/?term=10.1002/humu.24183