Dataset Information

CoNVaQ: a web tool for copy number variation-based association studies.

ABSTRACT: BACKGROUND:Copy number variations (CNVs) are large segments of the genome that are duplicated or deleted. Structural variations in the genome have been linked to many complex diseases. Similar to how genome-wide association studies (GWAS) have helped discover single-nucleotide polymorphisms linked to disease phenotypes, the extension of GWAS to CNVs has aided the discovery of structural variants associated with human traits and diseases. RESULTS:We present CoNVaQ, an easy-to-use web-based tool for CNV-based association studies. The web service allows users to upload two sets of CNV segments and search for genomic regions where the occurrence of CNVs is significantly associated with the phenotype. CoNVaQ provides two models: a simple statistical model using Fisher's exact test and a novel query-based model matching regions to user-defined queries. For each region, the method computes a global q-value statistic by repeated permutation of samples among the populations. We demonstrate our platform by using it to analyze a data set of HPV-positive and HPV-negative penile cancer patients. CONCLUSIONS:CoNVaQ provides a simple workflow for performing CNV-based association studies. It is made available as a web platform in order to provide a user-friendly workflow for biologists and clinicians to carry out CNV data analysis without installing any software. Through the web interface, users are also able to analyze their results to find overrepresented GO terms and pathways. In addition, our method is also available as a package for the R programming language. CoNVaQ is available at https://convaq.compbio.sdu.dk .

SUBMITTER: Larsen SJ

PROVIDER: S-EPMC5960125 | biostudies-other | 2018 May

REPOSITORIES: biostudies-other

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CoNVaQ: a web tool for copy number variation-based association studies.

Larsen Simon Jonas SJ do Canto Luisa Matos LM Rogatto Silvia Regina SR Baumbach Jan J

BMC genomics 20180518 1

<h4>Background</h4>Copy number variations (CNVs) are large segments of the genome that are duplicated or deleted. Structural variations in the genome have been linked to many complex diseases. Similar to how genome-wide association studies (GWAS) have helped discover single-nucleotide polymorphisms linked to disease phenotypes, the extension of GWAS to CNVs has aided the discovery of structural variants associated with human traits and diseases.<h4>Results</h4>We present CoNVaQ, an easy-to-use w ...[more]

PMID: 29776329

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Project description:BACKGROUND:Percent mammographic density (PD) estimates the proportion of stromal, fat, and epithelial breast tissues on the mammogram image. Adjusted for age and body mass index (BMI), PD is one of the strongest risk factors for breast cancer. Inherited factors are hypothesized to explain between 30 and 60% of the variance in this trait. However, previously identified common genetic variants account for less than 6% of the variance in PD, leaving much of the genetic contribution to this trait unexplained. We performed the first study to examine whether germline copy number variation (CNV) are associated with PD. Two genome-wide association studies (GWAS) of percent density conducted on the Illumina 660W-Quad were used to identify and replicate the association between candidate CNVs and PD: the Minnesota Breast Cancer Family Study (MBCFS) and controls from the Mayo Venous Thromboembolism (Mayo VTE) Case-Control Study, with 585 and 328 women, respectively. Linear models were utilized to examine the association of each probe with PD, adjusted for age, menopausal status and BMI. Segmentation was subsequently performed on the probe-level test statistics to identify candidate CNV regions that were associated with PD. RESULTS:Sixty-one probes from five chromosomal regions [3q26.1 (2 regions), 8q24.22, 11p15.3, and 17q22] were significantly associated with PD in MBCFS (p-values <0.0001). A CNV at 3q26.1 showed the greatest evidence for association with PD; a region without any known SNPs. Conversely, the CNV at 17q22 was largely due to the association between SNPs and PD in the region. SNPs in the 8q24.22 region have been shown to be associated with risk of many cancers; however, SNPs in this region were not responsible for the observed CNV association. While we were unable to replicate the associations with PD, two of the five CNVs (3q26.1 and 11p15.3) were also observed in the Mayo VTE controls. CONCLUSIONS:CNVs may help to explain some of the variability in PD that is currently unexplained by SNPs. While we were able to replicate the existence of two CNVs across the two GWAS studies, we were unable to replicate the associations with PD. Even so, the proximity of the identified CNV regions to loci known to be associated with breast cancer risk suggests further investigation and potentially shared genetic mechanisms underlying the PD and breast cancer association.

Dataset Information

CoNVaQ: a web tool for copy number variation-based association studies.

Publications

CoNVaQ: a web tool for copy number variation-based association studies.

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