CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations

Wang, Xihong; Zheng, Zhuqing; Cai, Yi; Chen, Ting; Li, Chao; Fu, Weiwei; Jiang, Yu

doi:10.1093/gigascience/gix115

Cited by 99 publications

(87 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CNVs were detected using CNVnator (v0.3.3) software [21] and CNVcaller (RRID:SRC 015752) [22]. The CNVnator captured the read-depth signal in genomic regions with different CNs and genotyped both deletions and duplications with the correction for GC bias.…”

Section: Re-sequencing and Cnv Detectionmentioning

confidence: 99%

See 1 more Smart Citation

CNV analysis of Meishan pig by next-generation sequencing and effects of AHR gene CNV on pig reproductive traits

Zheng

Zhao

Yang

et al. 2020

J Animal Sci Biotechnol

View full text Add to dashboard Cite

Background: Reproductive performance of livestock is an economically important aspect of global food production. The Chinese Meishan pig is a prolific breed, with an average of three to five more piglets per litter than European breeds; however, the genetic basis for this difference is not well understood. Results: In this study, we investigated copy number variations (CNVs) of 32 Meishan pigs and 29 Duroc pigs by nextgeneration sequencing. A genome-wide analysis of 61 pigs revealed 12,668 copy number variable regions (CNVRs) that were further divided into three categories based on copy number (CN) of the whole population, i.e., gain (n = 7,638), and loss (n = 5,030) CNVRs. We then compared Meishan and Duroc pigs and identified 17.17 Mb of 6,387 CNVRs that only existing in Meishan pigs CNVRs that overlapped the reproduction-related gene encoding the aryl hydrocarbon receptor (AHR) gene. We found that normal AHR CN was more frequent than CN loss in four different pig breeds. An association analysis showed that AHR CN had a positive effect on litter size (P < 0.05) and that a higher CN was associated with higher total number born (P < 0.05), number born alive (P < 0.05), number of weaned piglets, and birth weight. Conclusions: The present study provides comprehensive CNVRs for Meishan and Duroc pigs through large-scale population resequencing. Our results provide a supplement for the high-resolution map of copy number variation in the porcine genome and valuable information for the investigation of genomic structural variation underlying traits of interest in pig. In addition, the association results provide evidence for AHR as a candidate gene associated with reproductive traits that can be used as a genetic marker in pig breeding programs.

show abstract

Section: Re-sequencing and Cnv Detectionmentioning

confidence: 99%

“…The CNVcaller applies robust signal detection and noise deduction methods on basis of RD algorithm to increase the computational efficiency in complex genomes. We ran the CNVcaller by population levels for Meishan and Duroc breeds with the default arguments [22].…”

Section: Re-sequencing and Cnv Detectionmentioning

confidence: 99%

CNV analysis of Meishan pig by next-generation sequencing and effects of AHR gene CNV on pig reproductive traits

Zheng

Zhao

Yang

et al. 2020

J Animal Sci Biotechnol

View full text Add to dashboard Cite

show abstract

“…PennCNV software has been extensively applied to Illumina chip data, especially for high-density SNP data [16,29]. CNVcaller and CNVnator software use read depth methods to detect CNVs in resequencing data [30,31]. Each software package has its own advantages and disadvantages, which may impact the accuracy of CNV detection.…”

Section: Discussionmentioning

confidence: 99%

A global analysis of CNVs in Chinese indigenous fine-wool sheep populations using whole-genome resequencing

Yuan

Guo

et al. 2019

Preprint

View full text Add to dashboard Cite

Background Copy number variation (CNV) is an important source of genetic variation that has a significant influence on phenotypic diversity, important economic traits and the evolution of livestock species. In this study, the genome-wide CNV distribution characteristics of 32 fine-wool sheep from three breeds were analyzed using resequencing.Results A total of 1747604 CNVs were detected in this study, and 7228 CNV regions (CNVR) were obtained after merging overlapping CNVs; these regions accounted for 2.17% of the sheep reference genome. The average length of the CNVRs was 4307.17 bp. “Deletion” events took place more frequently than “duplication” or “both” events. The CNVRs obtained overlapped with previously reported sheep CNVRs to variable extents (4.39%–55.46%). Functional enrichment analysis showed that the CNVR-harboring genes were mainly involved in sensory perception systems, nutrient metabolism processes, and growth and development processes. Furthermore, 1855 of the CNVRs were associated with 166 quantitative trait loci (QTL), including milk QTLs, carcass QTLs, and health-related QTLs, among others. In addition, the 32 fine-wool sheep were divided into horned and polled groups to analyze for the selective elimination of CNVRs, and it was found that the RXFP2 gene was strongly influenced by selection.Conclusions In summary, we constructed a genomic CNV map for Chinese indigenous fine-wool sheep using resequencing, thereby providing a valuable genetic variation resource for sheep genome research, which will contribute to the study of complex traits in sheep.

show abstract

“…GC content and mappability correction was done on the RPKM using the formula used by Yoon et al [28], where adjusted read count is given by where m GC is the median GC content of all regions with the same read count and m is the median GC of all regions. This approach is similar to the read depth approaches used in CNVnator [27] and in CNVcaller [85]. The normalized read count values were treated as proxies of log R ratio (LRR) values normally obtained from array analysis.…”

Section: Population Cnv Differentiationmentioning

confidence: 99%

Detection of copy number variants in African goats using whole genome sequence data

Nandolo

Mészáros

Wurzinger

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundCopy number variations (CNV) are a significant source of variation in the genome, as such they are essential to the genetic characterization of animal breeds. The aim of this study was to develop a fine-scaled copy number variation map for African goats. We used sequence data from multiple breeds and from multiple African countries. ResultsA total of 253,553 CNV (244,876 deletions and 8,677 duplications) were identified, corresponding to an overall average of 1,393 CNV per animal. The mean CNV length was 3.3 kb, with a median of 1.3 kb. There was substantial differentiation between the populations for some CNV, suggestive of the effect of population-specific selective pressures. A total of 6,231 global CNV regions (CNVR) were found across all animals, representing 59.2 Mb (2.4%) of the goat genome. About 1.6% of the CNVR were present in all 34 breeds and 28.7% were present in all 5 geographical areas across Africa, where animals had been sampled. The CNVR had genes that were highly enriched in important biological functions, molecular functions, and cellular components including retrograde endocannabinoid signaling, glutamatergic synapse and circadian entrainment. ConclusionsThis study presents the first fine CNV map of African goat based on WGS data and adds to the growing body of knowledge on the genetic characterization of goats.

show abstract

CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations

Cited by 99 publications

References 53 publications

CNV analysis of Meishan pig by next-generation sequencing and effects of AHR gene CNV on pig reproductive traits

CNV analysis of Meishan pig by next-generation sequencing and effects of AHR gene CNV on pig reproductive traits

A global analysis of CNVs in Chinese indigenous fine-wool sheep populations using whole-genome resequencing

Detection of copy number variants in African goats using whole genome sequence data

Contact Info

Product

Resources

About