Dataset Information

CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data.

ABSTRACT:

Motivation

Several algorithms exist for detecting copy number variants (CNVs) from human exome sequencing read depth, but previous tools have not been well suited for large population studies on the order of tens or hundreds of thousands of exomes. Their limitations include being difficult to integrate into automated variant-calling pipelines and being ill-suited for detecting common variants. To address these issues, we developed a new algorithm--Copy number estimation using Lattice-Aligned Mixture Models (CLAMMS)--which is highly scalable and suitable for detecting CNVs across the whole allele frequency spectrum.

Results

In this note, we summarize the methods and intended use-case of CLAMMS, compare it to previous algorithms and briefly describe results of validation experiments. We evaluate the adherence of CNV calls from CLAMMS and four other algorithms to Mendelian inheritance patterns on a pedigree; we compare calls from CLAMMS and other algorithms to calls from SNP genotyping arrays for a set of 3164 samples; and we use TaqMan quantitative polymerase chain reaction to validate CNVs predicted by CLAMMS at 39 loci (95% of rare variants validate; across 19 common variant loci, the mean precision and recall are 99% and 94%, respectively). In the Supplementary Materials (available at the CLAMMS Github repository), we present our methods and validation results in greater detail.

Availability and implementation

https://github.com/rgcgithub/clamms (implemented in C).

Contact

jeffrey.reid@regeneron.com

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Packer JS

PROVIDER: S-EPMC4681995 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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