Genome-Wide Analyses of Exonic Copy Number Variants in a Family-Based Study Point to Novel Autism Susceptibility Genes

Background: Copy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. Results: Gene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. Conclusions: This is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

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On the genome-wide analysis of copy number variants in family-based designs: methods for combining family-based and population-based information for testing dichotomous or quantitative traits, or completely ascertained samples

Genetic Epidemiology ◽

10.1002/gepi.20515 ◽

2010 ◽

Vol 34 (6) ◽

pp. 582-590 ◽

Cited By ~ 5

Author(s):

Amy Murphy ◽

Sungho Won ◽

Angela Rogers ◽

Jen-Hwa Chu ◽

Benjamin A. Raby ◽

...

Keyword(s):

Copy Number ◽

Quantitative Traits ◽

Copy Number Variants ◽

Population Based ◽

Genome Wide Analysis ◽

Genome Wide ◽

Family Based

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Rare Copy Number Variants (CNVs) and Breast Cancer Risk

10.21203/rs.3.rs-602209/v1 ◽

2021 ◽

Author(s):

Joe Dennis ◽

Jonathan Tyrer ◽

Logan Walker ◽

Kyriaki Michailidou ◽

Leila Dorling ◽

...

Keyword(s):

Breast Cancer ◽

Copy Number ◽

Statistical Significance ◽

Copy Number Variants ◽

Susceptibility Genes ◽

European Ancestry ◽

Rare Cnvs ◽

Cancer Susceptibility Genes ◽

Large Breast ◽

Genome Wide

Abstract BackgroundCopy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data.ResultsGene burden tests detected the strongest association for deletions in BRCA1 (P= 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P= 0.0008), ATM (P= 0.002) and BRCA2 (P= 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci.ConclusionsThis is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

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Assessing the Role of Copy Number Variants in Prostate Cancer Risk and Progression using a Novel Genome-Wide Screening Method

10.21236/ada568305 ◽

2012 ◽

Author(s):

Donna Lehman ◽

August Blackburn ◽

Robin Leach

Keyword(s):

Prostate Cancer ◽

Cancer Risk ◽

Copy Number ◽

Screening Method ◽

Copy Number Variants ◽

Prostate Cancer Risk ◽

Genome Wide

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Detection and characterization of copy number variants based on whole-genome sequencing by DNBSEQ platforms

10.1101/786962 ◽

2019 ◽

Author(s):

Junhua Rao ◽

Lihua Peng ◽

Fang Chen ◽

Hui Jiang ◽

Chunyu Geng ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Copy Number Variants ◽

Copy Number Variant ◽

Whole Genome ◽

Genome Wide ◽

Wide Range ◽

Distribution Sensitivity ◽

Cnv Detection

AbstractBackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ™ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ™ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.ResultsDNBSEQ™ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ™ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ™ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.ConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ™ platforms for the first time.

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RefCNV: Identification of Gene-Based Copy Number Variants Using Whole Exome Sequencing

Cancer Informatics ◽

10.4137/cin.s36612 ◽

2016 ◽

Vol 15 ◽

pp. CIN.S36612 ◽

Cited By ~ 3

Author(s):

Lun-Ching Chang ◽

Biswajit Das ◽

Chih-Jian Lih ◽

Han Si ◽

Corinne E. Camalier ◽

...

Keyword(s):

Exome Sequencing ◽

Whole Exome Sequencing ◽

Copy Number ◽

Copy Number Variants ◽

Estimation Methods ◽

Single Nucleotide Variants ◽

False Positive Error ◽

Reference Set ◽

Genome Wide ◽

Whole Exome

With rapid advances in DNA sequencing technologies, whole exome sequencing (WES) has become a popular approach for detecting somatic mutations in oncology studies. The initial intent of WES was to characterize single nucleotide variants, but it was observed that the number of sequencing reads that mapped to a genomic region correlated with the DNA copy number variants (CNVs). We propose a method RefCNV that uses a reference set to estimate the distribution of the coverage for each exon. The construction of the reference set includes an evaluation of the sources of variability in the coverage distribution. We observed that the processing steps had an impact on the coverage distribution. For each exon, we compared the observed coverage with the expected normal coverage. Thresholds for determining CNVs were selected to control the false-positive error rate. RefCNV prediction correlated significantly ( r = 0.96–0.86) with CNV measured by digital polymerase chain reaction for MET (7q31), EGFR (7p12), or ERBB2 (17q12) in 13 tumor cell lines. The genome-wide CNV analysis showed a good overall correlation (Spearman's coefficient = 0.82) between RefCNV estimation and publicly available CNV data in Cancer Cell Line Encyclopedia. RefCNV also showed better performance than three other CNV estimation methods in genome-wide CNV analysis.

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