HD-CNV: hotspot detector for copy number variants

Butler, Jenna; Locke, M Elizabeth O; Hill, Kathleen A.; Daley, Mark

doi:10.1093/bioinformatics/bts650

Cited by 14 publications

(17 citation statements)

References 10 publications

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“…Chromosomes 8, 14, 22 and Y show very highly conserved patterns, as there are large groupings of overlapping CNV calls, and very few individual calls. The number of events on chromosome 6 identified in our study is relatively low, contrary to the previous studies on HapMap data (Butler et al, 2013). The locus-specific mutation rates for CNV have been observed to be ∼100 to 10 000 times higher than those for nucleotide substitution rates, which not only highlights the instability of CNV regions but also suggests large variation in CNV mutation rate (Fu et al, 2010).…”

Section: (Iii) Hotspot Detectioncontrasting

confidence: 99%

“…In order to compare and identify CNVs between samples of the same and different populations as hotspots and rare, and to also correlate their abeyant effects on a wide variety of biological contexts, Hotspot Detector for CNVs (HD-CNV) (Butler et al, 2013) was used to analyse and detect recurrent CNV regions by finding cliques in an interval graph generated from the input. HD-CNV requires CNV calls as an input to detect recurrent regions based on percentage overlap.…”

Section: (Vi) Hd-cnvmentioning

confidence: 99%

“…CNV events are treated as nodes in an interval graph and are used to represent regions (intervals) on a real line, and edges are added where intervals overlap as postulated by Lekkerkerker et al (1962). Based on this, Butler et al (2013) modified and added edges between nodes that share the base pair overlap required to consider two CNV events part of a merged region (default 40%) and the overlap required for a family (region with highly similar CNV events, default 99%). Merged regions, therefore, contain a collection of CNV events where each CNV overlaps all others in the merged region by the minimum overlap specified, indicating the genomic location where those groups of overlapping CNVs are found (Butler et al, 2013).…”

Section: (Vi) Hd-cnvmentioning

confidence: 99%

“…Based on this, Butler et al (2013) modified and added edges between nodes that share the base pair overlap required to consider two CNV events part of a merged region (default 40%) and the overlap required for a family (region with highly similar CNV events, default 99%). Merged regions, therefore, contain a collection of CNV events where each CNV overlaps all others in the merged region by the minimum overlap specified, indicating the genomic location where those groups of overlapping CNVs are found (Butler et al, 2013). The output graph generated by the HD-CNV was then visualized using Gephi graph creation software.…”

Section: (Vi) Hd-cnvmentioning

confidence: 99%

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Global patterns of large copy number variations in the human genome reveal complexity in chromosome organization

et al. 2015

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Global patterns of copy number variations (CNVs) in chromosomes are required to understand the dynamics of genome organization and complexity. For this study, analysis was performed using the Affymetrix Genome-Wide Human SNP Array 6.0 chip and CytoScan High-Density arrays. We identified a total of 44 109 CNVs from 1715 genomes with a mean of 25 CNVs in an individual, which established the first drafts of population-specific CNV maps providing a rationale for prioritizing chromosomal regions. About 19 905 ancient CNVs were identified across all chromosomes and populations at varying frequencies. CNV count, and sometimes CNV size, contributed to the bulk CNV size of the chromosome. Population specific lengthening and shortening of chromosomal length was observed. Sex bias for CNV presence was largely dependent on ethnicity. Lower CNV inheritance rate was observed for India, compared to YRI and CEU. A total of 33 candidate CNV hotspots from 5382 copy number (CN) variable region (CNVR) clusters were identified. Population specific CNV distribution patterns in p and q arms disturbed the assumption that CNV counts in the p arm are less common compared to long arms, and the CNV occurrence and distribution in chromosomes is length independent. This study unraveled the force of independent evolutionary dynamics on genome organization and complexity across chromosomes and populations.

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Section: (Iii) Hotspot Detectioncontrasting

confidence: 99%

Section: (Vi) Hd-cnvmentioning

confidence: 99%

Section: (Vi) Hd-cnvmentioning

confidence: 99%

Section: (Vi) Hd-cnvmentioning

confidence: 99%

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Global patterns of large copy number variations in the human genome reveal complexity in chromosome organization

et al. 2015

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“…This calculation was used to test the relationship within each group in relation to their expected genetic relatedness. To perform the reciprocal overlap formula, HD-CNV was used with 50 as the identified RO merge criteria [26]. The d value was averaged for each type of relatedness in each individual software program.…”

Section: Methodsmentioning

confidence: 99%

Biological relevance of CNV calling methods using familial relatedness including monozygotic twins

et al. 2014

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BackgroundStudies involving the analysis of structural variation including Copy Number Variation (CNV) have recently exploded in the literature. Furthermore, CNVs have been associated with a number of complex diseases and neurodevelopmental disorders. Common methods for CNV detection use SNP, CNV, or CGH arrays, where the signal intensities of consecutive probes are used to define the number of copies associated with a given genomic region. These practices pose a number of challenges that interfere with the ability of available methods to accurately call CNVs. It has, therefore, become necessary to develop experimental protocols to test the reliability of CNV calling methods from microarray data so that researchers can properly discriminate biologically relevant data from noise.ResultsWe have developed a workflow for the integration of data from multiple CNV calling algorithms using the same array results. It uses four CNV calling programs: PennCNV (PC), Affymetrix® Genotyping Console™ (AGC), Partek® Genomics Suite™ (PGS) and Golden Helix SVS™ (GH) to analyze CEL files from the Affymetrix® Human SNP 6.0 Array™. To assess the relative suitability of each program, we used individuals of known genetic relationships. We found significant differences in CNV calls obtained by different CNV calling programs.ConclusionsAlthough the programs showed variable patterns of CNVs in the same individuals, their distribution in individuals of different degrees of genetic relatedness has allowed us to offer two suggestions. The first involves the use of multiple algorithms for the detection of the largest possible number of CNVs, and the second suggests the use of PennCNV over all other methods when the use of only one software program is desirable.

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Type 2 diabetes mellitus disease risk genes identified by genome wide copy number variation scan in normal populations

Prabhanjan

Suresh

Murthy

et al. 2016

Diabetes Research and Clinical Practice

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HD-CNV: hotspot detector for copy number variants

Abstract: jcamer7@uwo.ca

Cited by 14 publications

References 10 publications

Global patterns of large copy number variations in the human genome reveal complexity in chromosome organization

Global patterns of large copy number variations in the human genome reveal complexity in chromosome organization

Biological relevance of CNV calling methods using familial relatedness including monozygotic twins

Type 2 diabetes mellitus disease risk genes identified by genome wide copy number variation scan in normal populations

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