Dataset Information

GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data.

ABSTRACT: Copy number variants (CNVs) are major contributors to genetic diversity and disease. While standardized methods, such as the genome analysis toolkit (GATK), exist for detecting short variants, technical challenges have confounded uniform large-scale CNV analyses from whole-exome sequencing (WES) data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, we developed GATK-gCNV, a flexible algorithm to discover rare CNVs from sequencing read-depth information, complete with open-source distribution via GATK. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data, finding up to 95% recall of rare coding CNVs at a resolution of more than two exons. We used GATK-gCNV to generate a reference catalog of rare coding CNVs in WES data from 197,306 individuals in the UK Biobank, and observed strong correlations between per-gene CNV rates and measures of mutational constraint, as well as rare CNV associations with multiple traits. In summary, GATK-gCNV is a tunable approach for sensitive and specific CNV discovery in WES data, with broad applications.

SUBMITTER: Babadi M

PROVIDER: S-EPMC10904014 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data.

Babadi Mehrtash M Fu Jack M JM Lee Samuel K SK Smirnov Andrey N AN Smirnov Andrey N AN Gauthier Laura D LD Walker Mark M Benjamin David I DI Zhao Xuefang X Karczewski Konrad J KJ Wong Isaac I Collins Ryan L RL Sanchis-Juan Alba A Brand Harrison H Banks Eric E Talkowski Michael E ME

Nature genetics 20230821 9

Copy number variants (CNVs) are major contributors to genetic diversity and disease. While standardized methods, such as the genome analysis toolkit (GATK), exist for detecting short variants, technical challenges have confounded uniform large-scale CNV analyses from whole-exome sequencing (WES) data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, we developed GATK-gCNV, a flexible algorithm to discover rare CNVs from sequencing read-depth inf ...[more]

PMID: 37604963

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Project description:BackgroundExome sequencing (ES) is a first-tier diagnostic test for many suspected Mendelian disorders. While it is routine to detect small sequence variants, it is not a standard practice in clinical settings to detect germline copy-number variants (CNVs) from ES data due to several reasons relating to performance. In this work, we comprehensively characterized one of the most sensitive ES-based CNV tools, ExomeDepth, against SNP array, a standard of care test in clinical settings to detect genome-wide CNVs.MethodsWe propose a modified ExomeDepth workflow by excluding exons with low mappability prior to variant calling to drastically reduce the false positives originating from the repetitive regions of the genome, and an iterative variant calling framework to assess the reproducibility. We used a cohort of 307 individuals with clinical ES data and clinical SNP array to estimate the sensitivity and false discovery rate of the CNV detection using exome sequencing. Further, we performed targeted testing of the STRC gene in 1972 individuals. To reduce the number of variants for downstream analysis, we performed a large-scale iterative variant calling process with random control cohorts to assess the reproducibility of the CNVs.ResultsThe modified workflow presented in this paper reduced the number of total variants identified by one third while retaining a higher sensitivity of 97% and resulted in an improved false discovery rate of 11.4% compared to the default ExomeDepth pipeline. The exclusion of exons with low mappability removes 4.5% of the exons, including a subset of exons (0.6%) in disease-associated genes which are intractable by short-read next-generation sequencing (NGS). Results from the reproducibility analysis showed that the clinically reported variants were reproducible 100% of the time and that the modified workflow can be used to rank variants from high to low confidence. Targeted testing of 30 CNVs identified in STRC, a challenging gene to ascertain by NGS, showed a 100% validation rate.ConclusionsIn summary, we introduced a modification to the default ExomeDepth workflow to reduce the false positives originating from the repetitive regions of the genome, created a large-scale iterative variant calling framework for reproducibility, and provided recommendations for implementation in clinical settings.

Project description:Copy number variants (CNVs) are a major source of genetic variation and can disrupt genes or affect gene dosage. They are known to be causal or underlie predisposition to various diseases. However, the role of CNVs in inherited breast cancer susceptibility has not been thoroughly investigated. To address this, we performed whole-exome sequencing based analysis of rare CNVs in 98 high-risk Northern Finnish breast cancer cases. After filtering, selected candidate alleles were validated and characterized with a combination of orthogonal methods, including PCR-based approaches, optical genome mapping and long-read sequencing. This revealed three recurrent alterations: a 31 kb deletion co-occurring with a retrotransposon insertion (delins) in RAD52, a 13.4 kb deletion in HSD17B14 and a 64 kb partial duplication of RAD51C. Notably, all these genes encode proteins involved in pathways previously identified as essential for breast cancer development. Variants were genotyped in geographically matched cases and controls (altogether 278 hereditary and 1983 unselected breast cancer cases, and 1229 controls). The RAD52 delins and HSD17B14 deletion both showed significant enrichment among cases with indications of hereditary disease susceptibility. RAD52 delins was identified in 7/278 cases (2.5%, P = 0.034, OR = 2.86, 95% CI = 1.10-7.45) and HSD17B14 deletion in 8/278 cases (2.9%, P = 0.014, OR = 3.28, 95% CI = 1.31-8.23), the frequency of both variants in the controls being 11/1229 (0.9%). This suggests a role for RAD52 and HSD17B14 in hereditary breast cancer susceptibility. The RAD51C duplication was very rare, identified only in 2/278 of hereditary cases and 2/1229 controls (P = 0.157, OR = 4.45, 95% CI = 0.62-31.70). The identification of recurrent CNVs in these genes, and especially the relatively high frequency of RAD52 and HSD17B14 alterations in the Finnish population, highlights the importance of studying CNVs alongside single nucleotide variants when searching for genetic factors underlying hereditary disease predisposition.

Dataset Information

GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data.

Publications

GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data.

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