Identification of Copy Number Variation in Domestic Chicken Using Whole-Genome Sequencing Reveals Evidence of Selection in the Genome.
Donghyeok SeolByung June KoBongsang KimHan-Ha ChaiDajeong LimHeebal KimPublished in: Animals : an open access journal from MDPI (2019)
Copy number variation (CNV) has great significance both functionally and evolutionally. Various CNV studies are in progress to find the cause of human disease and to understand the population structure of livestock. Recent advances in next-generation sequencing (NGS) technology have made CNV detection more reliable and accurate at whole-genome level. However, there is a lack of CNV studies on chickens using NGS. Therefore, we obtained whole-genome sequencing data of 65 chickens including Red Jungle Fowl, Cornish (broiler), Rhode Island Red (hybrid), and White Leghorn (layer) from the public databases for CNV region (CNVR) detection. Using CNVnator, a read-depth based software, a total of 663 domesticated-specific CNVRs were identified across autosomes. Gene ontology analysis of genes annotated in CNVRs showed that mainly enriched terms involved in organ development, metabolism, and immune regulation. Population analysis revealed that CN and RIR are closer to each other than WL, and many genes (LOC772271, OR52R1, RD3, ADH6, TLR2B, PRSS2, TPK1, POPDC3, etc.) with different copy numbers between breeds found. In conclusion, this study has helped to understand the genetic characteristics of domestic chickens at CNV level, which may provide useful information for the development of breeding systems in chickens.
Keyphrases
- copy number
- genome wide
- mitochondrial dna
- heat stress
- dna methylation
- bioinformatics analysis
- disease virus
- endothelial cells
- healthcare
- single cell
- real time pcr
- loop mediated isothermal amplification
- genome wide identification
- toll like receptor
- case control
- big data
- immune response
- squamous cell carcinoma
- electronic health record
- gene expression
- emergency department
- lymph node metastasis
- data analysis
- quantum dots
- machine learning
- artificial intelligence