BACOM: in silico detection of genomic deletion types and correction of normal cell contamination in copy number data

The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than BRCA1/2, mostly in ATM, CHEK2, and RAD51C and less frequently in BARD1, MLH1, MSH2, PALB2, PMS2, RAD51D, and TP53. The Sophia Genetics DDM software showed the highest sensitivity; six CNVs were missed by at least one of the non-commercial tools. The positive predictive values ranged from 5.9% (74/1249) for panelcn.MOPS to 79.1% (72/91) for ExomeDepth. Verification of in silico predicted CNVs is required due to high frequencies of false positive predictions, particularly affecting target regions at the extremes of the GC content or target length distributions. CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further BC/OC predisposition genes.

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Model-Integrated Estimation of Normal Tissue Contamination for Cancer SNP Allelic Copy Number Data

Cancer Informatics ◽

10.4137/cin.s6873 ◽

2011 ◽

Vol 10 ◽

pp. CIN.S6873 ◽

Cited By ~ 3

Author(s):

Susann Stjernqvist ◽

Tobias Rydén ◽

Chris D. Greenman

Keyword(s):

Copy Number ◽

Normal Tissue ◽

Index Structure ◽

Copy Number Data ◽

Cancer Data ◽

Unequally Spaced ◽

Intensity Measurements ◽

Tissue Contamination ◽

Continuous Index ◽

Allelic Copy Number

SNP allelic copy number data provides intensity measurements for the two different alleles separately. We present a method that estimates the number of copies of each allele at each SNP position, using a continuous-index hidden Markov model. The method is especially suited for cancer data, since it includes the fraction of normal tissue contamination, often present when studying data from cancer tumors, into the model. The continuous-index structure takes into account the distances between the SNPs, and is thereby appropriate also when SNPs are unequally spaced. In a simulation study we show that the method performs favorably compared to previous methods even with as much as 70% normal contamination. We also provide results from applications to clinical data produced using the Affymetrix genome-wide SNP 6.0 platform.

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Accurate Estimation of Genomic Deletions and Normal Cell Contamination by Bayesian Analysis of Mixtures

2009 IEEE International Conference on Bioinformatics and Biomedicine ◽

10.1109/bibm.2009.54 ◽

2009 ◽

Author(s):

Guoqiang Yu ◽

Bai Zhang ◽

Jianfeng Xu ◽

Ie-Ming Shih ◽

Yue Wang

Keyword(s):

Bayesian Analysis ◽

Normal Cell ◽

Accurate Estimation ◽

Genomic Deletions ◽

Normal Cell Contamination

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Copy number variant detection with low-coverage whole-genome sequencing is a viable replacement for the traditional array-CGH

10.1101/2020.09.07.20183665 ◽

2020 ◽

Author(s):

Marcel Kucharik ◽

Jaroslav Budis ◽

Michaela Hyblova ◽

Gabriel Minarik ◽

Tomas Szemes

Keyword(s):

In Silico ◽

Copy Number ◽

Normal Population ◽

Genetic Disorders ◽

Prenatal Testing ◽

In Silico Analysis ◽

Copy Number Variant ◽

Detection Algorithm ◽

Copy Number Variations ◽

Cnv Detection

Copy number variations (CNVs) are a type of structural variant involving alterations in the number of copies of specific regions of DNA, which can either be deleted or duplicated. CNVs contribute substantially to normal population variability; however, abnormal CNVs cause numerous genetic disorders. Nowadays, several methods for CNV detection are used, from the conventional cytogenetic analysis through microarray-based methods (aCGH) to next-generation sequencing (NGS). We present GenomeScreen - NGS based CNV detection method based on a previously described CNV detection algorithm used for non-invasive prenatal testing (NIPT). We determined theoretical limits of its accuracy and confirmed it with extensive in-silico study and already genotyped samples. Theoretically, at least 6M uniquely mapped reads are required to detect CNV with a length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in-silico analysis showed the requirement at least 8M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has a 200 kb mean resolution. GenomeScreen and aCGH both detected 59 deviations, GenomeScreen furthermore detected 134 other (usually) smaller variations. Furthermore, the overall cost per sample is about 2-3x lower in the case of GenomeScreen.

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TEGS-CN: A Statistical Method for Pathway Analysis of Genome-wide Copy Number Profile

Cancer Informatics ◽

10.4137/cin.s13978 ◽

2014 ◽

Vol 13s4 ◽

pp. CIN.S13978

Author(s):

Yen-Tsung Huang ◽

Thomas Hsu ◽

David C. Christiani

Keyword(s):

Copy Number ◽

Copy Number Data ◽

Copy Number Profile ◽

Test Statistic ◽

Gene Set ◽

Bonferroni Adjustment ◽

Gene Sets ◽

Genome Wide ◽

A Genome ◽

Pathway Analyses

The effects of copy number alterations make up a significant part of the tumor genome profile, but pathway analyses of these alterations are still not well established. We proposed a novel method to analyze multiple copy numbers of genes within a pathway, termed Test for the Effect of a Gene Set with Copy Number data (TEGS-CN). TEGS-CN was adapted from TEGS, a method that we previously developed for gene expression data using a variance component score test. With additional development, we extend the method to analyze DNA copy number data, accounting for different sizes and thus various numbers of copy number probes in genes. The test statistic follows a mixture of X 2 distributions that can be obtained using permutation with scaled X 2 approximation. We conducted simulation studies to evaluate the size and the power of TEGS-CN and to compare its performance with TEGS. We analyzed a genome-wide copy number data from 264 patients of non-small-cell lung cancer. With the Molecular Signatures Database (MSigDB) pathway database, the genome-wide copy number data can be classified into 1814 biological pathways or gene sets. We investigated associations of the copy number profile of the 1814 gene sets with pack-years of cigarette smoking. Our analysis revealed five pathways with significant P values after Bonferroni adjustment (<2.8 x 10-5), including the PTEN pathway (7.8 x 10-7), the gene set up-regulated under heat shock (3.6 x 10-6), the gene sets involved in the immune profile for rejection of kidney transplantation (9.2 x 10-6) and for transcriptional control of leukocytes (2.2 x 10-5), and the ganglioside biosynthesis pathway (2.7 x 10-5). In conclusion, we present a new method for pathway analyses of copy number data, and causal mechanisms of the five pathways require further study.

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