Special Issue "Copy-Number-Variation Microarrays in Basic Research and Clinical Applications"

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A special issue of Microarrays (ISSN 2076-3905).

Deadline for manuscript submissions: closed (30 September 2013)

Special Issue Editor

Guest Editor
Dr. Tobias Heckel (Website)

Department of Molecular Toxicology, F.Hoffmann-La Roche AG, CH-4070 Basel, Switzerland
Interests: genetic polymorphisms; CNV Arrays; SNP genotyping; molecular karyotyping; transcriptomics; genome structure and expression; integrative genomics; genotype-phenotype correlations

Special Issue Information

Dear Colleagues

Over the last years microarray technology has greatly improved the fields of structural genetic research and chromosomal disorders. Established cytogenetic and molecular techniques suffer of low resolution, limited to 5–10 megabases, like Giemsa-banded chromosome analysis, or are restricted in detecting only a few specific DNA regions like fluorescence in situ hybridization. Oligonucleotide microarrays containing millions of probes enabled the detection of sub-microscopic structural genetic variations, genome-wide, at ultra-high resolution down to a few hundred till thousand bases. Although high coverage next generation DNA sequencing can detect structural genetic variations, microarray screening is expected to be the main approach for several years due to its user-friendly workflow, lower cost and straightforward data interpretation.
This special issue invites contributions to the application and evaluation of microarrays for the discovery and diagnosis of structural genetic variations with a focus on Copy Number Variations (CNVs). These variations account for a substantial proportion of genetic variability in human and animal populations and range from seemingly neutral polymorphisms over polymorphisms correlating with phenotypic differences to pathological variations predisposing or causing disease.
It will be interesting to the reader of this special issue to increase the understanding of how microarray technology helped revealing the biological meaning of CNVs but also to learn about recent advances in CNV calling, database aided classification, and CNV phenotype correlations.

Dr. Tobias Heckel
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microarrays is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.


Keywords

  • array Comparative Genomic Hybridization
  • SNP genotyping arrays
  • clinical cytogenetics
  • chromosomal disorders
  • CNV calling algorithms
  • neurobehavioral phenotypes
  • genomic instability
  • genotype-phenotype correlations

Published Papers (6 papers)

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Research

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Open AccessArticle Identifying Potential Regions of Copy Number Variation for Bipolar Disorder
Microarrays 2014, 3(1), 52-71; doi:10.3390/microarrays3010052
Received: 1 December 2013 / Revised: 10 February 2014 / Accepted: 12 February 2014 / Published: 28 February 2014
Cited by 1 | PDF Full-text (495 KB) | HTML Full-text | XML Full-text
Abstract
Bipolar disorder is a complex psychiatric disorder with high heritability, but its genetic determinants are still largely unknown. Copy number variation (CNV) is one of the sources to explain part of the heritability. However, it is a challenge to estimate discrete values [...] Read more.
Bipolar disorder is a complex psychiatric disorder with high heritability, but its genetic determinants are still largely unknown. Copy number variation (CNV) is one of the sources to explain part of the heritability. However, it is a challenge to estimate discrete values of the copy numbers using continuous signals calling from a set of markers, and to simultaneously perform association testing between CNVs and phenotypic outcomes. The goal of the present study is to perform a series of data filtering and analysis procedures using a DNA pooling strategy to identify potential CNV regions that are related to bipolar disorder. A total of 200 normal controls and 200 clinically diagnosed bipolar patients were recruited in this study, and were randomly divided into eight control and eight case pools. Genome-wide genotyping was employed using Illumina Human Omni1-Quad array with approximately one million markers for CNV calling. We aimed at setting a series of criteria to filter out the signal noise of marker data and to reduce the chance of false-positive findings for CNV regions. We first defined CNV regions for each pool. Potential CNV regions were reported based on the different patterns of CNV status between cases and controls. Genes that were mapped into the potential CNV regions were examined with association testing, Gene Ontology enrichment analysis, and checked with existing literature for their associations with bipolar disorder. We reported several CNV regions that are related to bipolar disorder. Two CNV regions on chromosome 11 and 22 showed significant signal differences between cases and controls (p < 0.05). Another five CNV regions on chromosome 6, 9, and 19 were overlapped with results in previous CNV studies. Experimental validation of two CNV regions lent some support to our reported findings. Further experimental and replication studies could be designed for these selected regions. Full article
Open AccessArticle Copy Number Studies in Noisy Samples
Microarrays 2013, 2(4), 284-303; doi:10.3390/microarrays2040284
Received: 20 September 2013 / Revised: 24 October 2013 / Accepted: 25 October 2013 / Published: 6 November 2013
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Abstract
System noise was analyzed in 77 Affymetrix 6.0 samples from a previous clinical study of copy number variation (CNV). Twenty-three samples were classified as eligible for CNV detection, 29 samples as ineligible and 25 were classified as being of intermediate quality. New [...] Read more.
System noise was analyzed in 77 Affymetrix 6.0 samples from a previous clinical study of copy number variation (CNV). Twenty-three samples were classified as eligible for CNV detection, 29 samples as ineligible and 25 were classified as being of intermediate quality. New software (“noise-free-cnv”) was developed to visualize the data and reduce system noise. Fresh DNA preparations were more likely to yield eligible samples (p < 0.001). Eligible samples had higher rates of successfully genotyped SNPs (p < 0.001) and lower variance of signal intensities (p < 0.001), yielded fewer CNV findings after Birdview analysis (p < 0.001), and showed a tendency to yield fewer PennCNV calls (p = 0.053). The noise-free-cnv software visualized trend patterns of noise in the signal intensities across the ordered SNPs, including a wave pattern of noise, being co-linear with the banding pattern of metaphase chromosomes, as well as system deviations of individual probe sets (per-SNP noise). Wave noise and per-SNP noise occurred independently and could be separately removed from the samples. We recommend a two-step procedure of CNV validation, including noise reduction and visual inspection of all CNV calls, prior to molecular validation of a selected number of putative CNVs. Full article
Open AccessArticle Kernel-Based Aggregation of Marker-Level Genetic Association Tests Involving Copy-Number Variation
Microarrays 2013, 2(3), 265-283; doi:10.3390/microarrays2030265
Received: 2 August 2013 / Revised: 29 August 2013 / Accepted: 30 August 2013 / Published: 4 September 2013
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Abstract
Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information [...] Read more.
Genetic association tests involving copy-number variants (CNVs) are complicated by the fact that CNVs span multiple markers at which measurements are taken. The power of an association test at a single marker is typically low, and it is desirable to pool information across the markers spanned by the CNV. However, CNV boundaries are not known in advance, and the best way to proceed with this pooling is unclear. In this article, we propose a kernel-based method for aggregation of marker-level tests and explore several aspects of its implementation. In addition, we explore some of the theoretical aspects of marker-level test aggregation, proposing a permutation-based approach that preserves the family-wise error rate of the testing procedure, while demonstrating that several simpler alternatives fail to do so. The empirical power of the approach is studied in a number of simulations constructed from real data involving a pharmacogenomic study of gemcitabine and compares favorably with several competing approaches. Full article
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Review

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Open AccessReview Copy Number Variation in Chickens: A Review and Future Prospects
Microarrays 2014, 3(1), 24-38; doi:10.3390/microarrays3010024
Received: 15 December 2013 / Revised: 22 January 2014 / Accepted: 23 January 2014 / Published: 5 February 2014
Cited by 2 | PDF Full-text (158 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
DNA sequence variations include nucleotide substitution, deletion, insertion, translocation and inversion. Deletion or insertion of a large DNA segment in the genome, referred to as copy number variation (CNV), has caught the attention of many researchers recently. It is believed that CNVs [...] Read more.
DNA sequence variations include nucleotide substitution, deletion, insertion, translocation and inversion. Deletion or insertion of a large DNA segment in the genome, referred to as copy number variation (CNV), has caught the attention of many researchers recently. It is believed that CNVs contribute significantly to genome variability, and thus contribute to phenotypic variability. In chickens, genome-wide surveys with array comparative genome hybridization (aCGH), SNP chip detection or whole genome sequencing have revealed a large number of CNVs. A large portion of chicken CNVs involves protein coding or regulatory sequences. A few CNVs have been demonstrated to be the determinant factors for single gene traits, such as late-feathering, pea-comb and dermal hyperpigmentation. The phenotypic effects of the majority of chicken CNVs are to be delineated. Full article
Open AccessReview Chromosomal Microarrays in Prenatal Diagnosis: Time for a Change of Policy?
Microarrays 2013, 2(4), 304-317; doi:10.3390/microarrays2040304
Received: 22 October 2013 / Revised: 19 November 2013 / Accepted: 27 November 2013 / Published: 5 December 2013
Cited by 2 | PDF Full-text (185 KB) | HTML Full-text | XML Full-text
Abstract
Microarrays have replaced conventional karyotyping as a first-tier test for unbalanced chromosome anomalies in postnatal cytogenetics mainly due to their unprecedented resolution facilitating the detection of submicroscopic copy number changes at a rate of 10–20% depending on indication for testing. A number [...] Read more.
Microarrays have replaced conventional karyotyping as a first-tier test for unbalanced chromosome anomalies in postnatal cytogenetics mainly due to their unprecedented resolution facilitating the detection of submicroscopic copy number changes at a rate of 10–20% depending on indication for testing. A number of studies have addressed the performance of microarrays for chromosome analyses in high risk pregnancies due to abnormal ultrasound findings and reported an excess detection rate between 5% and 10%. In low risk pregnancies, clear pathogenic copy number changes at the submicroscopic level were encountered in 1% or less. Variants of unclear clinical significance, unsolicited findings, and copy number changes with variable phenotypic consequences are the main issues of concern in the prenatal setting posing difficult management questions. The benefit of microarray testing may be limited in pregnancies with only moderately increased risks (advanced maternal age, positive first trimester test). It is suggested to not change the current policy of microarray application in prenatal diagnosis until more data on the clinical significance of copy number changes are available. Full article
Open AccessReview Comparative Analysis of CNV Calling Algorithms: Literature Survey and a Case Study Using Bovine High-Density SNP Data
Microarrays 2013, 2(3), 171-185; doi:10.3390/microarrays2030171
Received: 2 May 2013 / Revised: 4 June 2013 / Accepted: 5 June 2013 / Published: 25 June 2013
Cited by 9 | PDF Full-text (183 KB) | HTML Full-text | XML Full-text
Abstract
Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be [...] Read more.
Copy number variations (CNVs) are gains and losses of genomic sequence between two individuals of a species when compared to a reference genome. The data from single nucleotide polymorphism (SNP) microarrays are now routinely used for genotyping, but they also can be utilized for copy number detection. Substantial progress has been made in array design and CNV calling algorithms and at least 10 comparison studies in humans have been published to assess them. In this review, we first survey the literature on existing microarray platforms and CNV calling algorithms. We then examine a number of CNV calling tools to evaluate their impacts using bovine high-density SNP data. Large incongruities in the results from different CNV calling tools highlight the need for standardizing array data collection, quality assessment and experimental validation. Only after careful experimental design and rigorous data filtering can the impacts of CNVs on both normal phenotypic variability and disease susceptibility be fully revealed. Full article

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