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Exome sequencing and arrayCGH detection of gene sequence and copy number variation between ILS and ISS mouse strains.

ABSTRACT: It has been well documented that genetic factors can influence predisposition to develop alcoholism. While the underlying genomic changes may be of several types, two of the most common and disease associated are copy number variations (CNVs) and sequence alterations of protein coding regions. The goal of this study was to identify CNVs and single-nucleotide polymorphisms that occur in gene coding regions that may play a role in influencing the risk of an individual developing alcoholism. Toward this end, two mouse strains were used that have been selectively bred based on their differential sensitivity to alcohol: the Inbred long sleep (ILS) and Inbred short sleep (ISS) mouse strains. Differences in initial response to alcohol have been linked to risk for alcoholism, and the ILS/ISS strains are used to investigate the genetics of initial sensitivity to alcohol. Array comparative genomic hybridization (arrayCGH) and exome sequencing were conducted to identify CNVs and gene coding sequence differences, respectively, between ILS and ISS mice. Mouse arrayCGH was performed using catalog Agilent 1 × 244 k mouse arrays. Subsequently, exome sequencing was carried out using an Illumina HiSeq 2000 instrument. ArrayCGH detected 74 CNVs that were strain-specific (38 ILS/36 ISS), including several ISS-specific deletions that contained genes implicated in brain function and neurotransmitter release. Among several interesting coding variations detected by exome sequencing was the gain of a premature stop codon in the alpha-amylase 2B (AMY2B) gene specifically in the ILS strain. In total, exome sequencing detected 2,597 and 1,768 strain-specific exonic gene variants in the ILS and ISS mice, respectively. This study represents the most comprehensive and detailed genomic comparison of ILS and ISS mouse strains to date. The two complementary genome-wide approaches identified strain-specific CNVs and gene coding sequence variations that should provide strong candidates to contribute to the alcohol-related phenotypic differences associated with these strains.

SUBMITTER: Dumas L

PROVIDER: S-EPMC5512554 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Exome sequencing and arrayCGH detection of gene sequence and copy number variation between ILS and ISS mouse strains.

Dumas Laura L Dickens C Michael CM Anderson Nathan N Davis Jonathan J Bennett Beth B Radcliffe Richard A RA Sikela James M JM

Mammalian genome : official journal of the International Mammalian Genome Society 20140220 5-6

It has been well documented that genetic factors can influence predisposition to develop alcoholism. While the underlying genomic changes may be of several types, two of the most common and disease associated are copy number variations (CNVs) and sequence alterations of protein coding regions. The goal of this study was to identify CNVs and single-nucleotide polymorphisms that occur in gene coding regions that may play a role in influencing the risk of an individual developing alcoholism. Toward ...[more]

PMID: 24553828

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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

Exome sequencing and arrayCGH detection of gene sequence and copy number variation between ILS and ISS mouse strains.

Publications

Exome sequencing and arrayCGH detection of gene sequence and copy number variation between ILS and ISS mouse strains.

Similar Datasets

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