Dataset Information

Copy number variation study in Japanese quail associated with stress related traits using whole genome re-sequencing data.

ABSTRACT: Copy number variation (CNV) is a major driving factor for genetic variation and phenotypic diversity in animals. To detect CNVs and understand genetic components underlying stress related traits, we performed whole genome re-sequencing of pooled DNA samples of 20 birds each from High Stress (HS) and Low Stress (LS) Japanese quail lines using Illumina HiSeq 2×150 bp paired end method. Sequencing data were aligned to the quail genome and CNVnator was used to detect CNVs in the aligned data sets. The depth of coverage for the data reached to 41.4x and 42.6x for HS and LS birds, respectively. We identified 262 and 168 CNV regions affecting 1.6 and 1.9% of the reference genome that completely overlapped 454 and 493 unique genes in HS and LS birds, respectively. Ingenuity pathway analysis showed that the CNV genes were significantly enriched to phospholipase C signaling, neuregulin signaling, reelin signaling in neurons, endocrine and nervous development, humoral immune response, and carbohydrate and amino acid metabolisms in HS birds, whereas CNV genes in LS birds were enriched in cell-mediated immune response, and protein and lipid metabolisms. These findings suggest CNV genes identified in HS and LS birds could be candidate markers responsible for stress responses in birds.

SUBMITTER: Khatri B

PROVIDER: S-EPMC6438477 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Copy number variation study in Japanese quail associated with stress related traits using whole genome re-sequencing data.

Khatri Bhuwan B Kang Seong S Shouse Stephanie S Anthony Nicholas N Kuenzel Wayne W Kong Byungwhi C BC

PloS one 20190328 3

Copy number variation (CNV) is a major driving factor for genetic variation and phenotypic diversity in animals. To detect CNVs and understand genetic components underlying stress related traits, we performed whole genome re-sequencing of pooled DNA samples of 20 birds each from High Stress (HS) and Low Stress (LS) Japanese quail lines using Illumina HiSeq 2×150 bp paired end method. Sequencing data were aligned to the quail genome and CNVnator was used to detect CNVs in the aligned data sets. T ...[more]

PMID: 30921419

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Project description:BackgroundWhole genome sequencing enables a high resolution view of the human genome and provides unique insights into genome structure at an unprecedented scale. There have been a number of tools to infer copy number variation in the genome. These tools, while validated, also include a number of parameters that are configurable to genome data being analyzed. These algorithms allow for normalization to account for individual and population-specific effects on individual genome CNV estimates but the impact of these changes on the estimated CNVs is not well characterized. We evaluate in detail the effect of normalization methodologies in two CNV algorithms FREEC and CNV-seq using whole genome sequencing data from 8 individuals spanning four populations.MethodsWe apply FREEC and CNV-seq to a sequencing data set consisting of 8 genomes. We use multiple configurations corresponding to different read-count normalization methodologies in FREEC, and statistically characterize the concordance of the CNV calls between FREEC configurations and the analogous output from CNV-seq. The normalization methodologies evaluated in FREEC are: GC content, mappability and control genome. We further stratify the concordance analysis within genic, non-genic, and a collection of validated variant regions.ResultsThe GC content normalization methodology generates the highest number of altered copy number regions. Both mappability and control genome normalization reduce the total number and length of copy number regions. Mappability normalization yields Jaccard indices in the 0.07 - 0.3 range, whereas using a control genome normalization yields Jaccard index values around 0.4 with normalization based on GC content. The most critical impact of using mappability as a normalization factor is substantial reduction of deletion CNV calls. The output of another method based on control genome normalization, CNV-seq, resulted in comparable CNV call profiles, and substantial agreement in variable gene and CNV region calls.ConclusionsChoice of read-count normalization methodology has a substantial effect on CNV calls and the use of genomic mappability or an appropriately chosen control genome can optimize the output of CNV analysis.

Project description:BackgroundRecently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors' complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.ResultsIn this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.ConclusionsThe limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

Dataset Information

Copy number variation study in Japanese quail associated with stress related traits using whole genome re-sequencing data.

Publications

Copy number variation study in Japanese quail associated with stress related traits using whole genome re-sequencing data.

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