جستجوی مقالات مرتبط با کلیدواژه « copy number variation » در نشریات گروه « کشاورزی »

Copy number variation (CNV) consist of deletion, insertion, and duplications. It is an important source of genetic variation in organisms and thus influences on the gene expression and phenotypic variation. Copy number variation (CNV) is one of the structural variant with an intermediate size class larger than 50bp which involves unbalanced rearrangements that increase or decrease the amount of DNA (Pirooznia et al 2015, Alkan et al 2011). The size of CNVs is larger than 50bp, while smaller segments are known as insertions or deletions (indels). Thereupon these structural variations comprise more polymorphic than SNPs because of enormity, detection of them and their effect on phenotype has caught the attention of many researchers recently. It has been reported that CNVs changes in gene dosage and regulation as well as in transcript structure, and thus contribute to phenotypic variability (Pirooznia et al 2015, Alkan et al 2011). The pea-comb phenotype is caused by a CNV mapping to intron 1 of the SRY (sex determining region Y)-box 5 (SOX5) gene (Wright et al. 2009). Late feathering in chickens is due to incomplete duplication in PRLR and SPEF2 genes (Elfrink et al. 2008). In swine, dominant white colour has been related with a duplication of a 450-kb fragment of the KIT gene (Giuffra et al. 1999) and a splice mutation causing the skipping of exon 17 (Giuffra et al. 1999). In sheep, doubling in the ASIP gene results in the regulation of pigment in body coat (Norris et al. 2008). Doubling the 4.6 k base pair into the six introns of the STX17 gene results in a gray body color in the horse with age. Deletion of the intergenic region with a length of 11.7 kbp in the goat genome leads to the removal of horns (Clop et al. 2012). Chicken is the most intensively farmed animal on earth and is a major food source with billions of birds used in meat and egg production each year. A big share of chicken CNVs involves protein coding or regulatory sequences. A comprehensive study of chicken CNV can provide valuable information on genetic diversity and assist future analyses of associations between CNV and economically important traits in chickens. Unique chicken genome with macro and micro chromosomes and its biology make it an ideal organism for studies in development and evolution, as well as applications in agriculture and medicine (Burt 2005). In the last several years, There has been an increasing interest in the study of CNVs in the chicken. This study focuses on comparison of CNV between the broilers and layers chicken to find evidence of domestication on the genome using whole genome sequencing.

we used n=90 female birds of two commercial broiler (n=40) and layer (n=50) chicken. The broilers (BRs) were represented by 20 DNA samples of each of two lines (BRA and BRB) established independently and previously collected as part of the AVIANDIV project. In the layer group (LRs), data from 25 birds each from purebred white (WL) and brown (BL) egg laying populations, sequenced in the frame of the SYNBREED project (http://www.synbreed.tum.de/index.php?id=2 ,(were included. The paired-end reads with a read length of 101bp were mapped against the current reference genome assembly Galgal6 using the Burrows-Wheeler aligner (bwa, 0.6.2-r126 Version, with default parameters. Duplicate reads were masked during post-processing using the Picard tool set (version 2.9.2, http://picard.sourceforge.net). Finally, Genome Analysis Toolkit-3.3.0 was used to realign reads for correcting errors caused by InDels. Using GATK software package and Depth Of Coverage function (McKenna et al 2010), the depth of readings was calculated for each sample. Then filter out reads with mapping quality below 20. Because comparing the genomes of individuals in different groups was time consuming and computationally difficult for all parts of the genome, the genomes of each individual were divided into 1000 bp non-overlapping windows and the average reading depth per window was calculated. Then the results were normalized against the BL sample that showed highest average depth. In short, we created a correction factor per population and applied it on the depth of coverage value for each window. For all the contrasts, we performed an analysis of variance (ANOVA) as described (Carneiro et al 2014). For the Broilers-Layers contrast we scanned 935247 windows. 70372 windows showed significant by FDR with P < 0.001, with ANOVA using the Benjamini-Hochberg FDR method for multiple corrections (Benjamini and Hochberg 1995).

Mapping sequencing data to galGal6 assembly showed an average 98.61% mapping rate and 11.51 depth. Manhattan plot was plotted for regions of the genome that differed significantly between the two groups (FDR = 0.001). The points above the hypothetical line were identified and examined in a 25 Kbp confidence interval to identify possible genes. 39 regions were identified that half of them dose not contain any genes. Although Long noncoding RNAs are under lower selective pressure than protein-coding genes (Batista and Chang 2013), The other 11 regions contained 16 genes related to long non-coding RNAs. Long noncoding RNAs (lncRNAs) play a critical role in organizing the 3-dimensional genome architecture and regulating gene activity in cis or in trans through multiple mechanisms (Zhang et al 2019, Batista and Chang 2013). 6 othere regions also contained 12 coding genes. Most of the identified genes were somehow linked to the immune system disease or cancer. Genes such as DEDs and TNFAIP8 are involved in programmed cell death (apoptosis) and two genes NPAL3 and RCAN, which are involved in the immune system, had a copy number variation in the studied samples. In addition RCAN is involved in Down syndrome. The PFDN gene, located on chromosome 25, is also involved in Alzheimer's and Parkinson's disease.

Horses have played an important role in the history of Iranians during different centuries. They kept horses for various aims such as agriculture, transportation, sport, food sources. Iran has a suitable climatic, social and economic potential for keeping and breeding horses, that is why it has been created different breeds using the selection and breeding. But due to problems such as mechanization, lack of government support, export ban, high costs of breeding and maintenance, import of foreign horses, lack of proper planning, interest in keeping horses has decreased. So, unfortunately, only a few native Iranian breeds remain. Additional investigation of the equine genomic architecture is critical for a better understanding of the equine genome, as well as for expanded comparisons across diverse mammalian species. Turkmen and Caspian horses are well-known breeds of Iranian horse breeds. These breeds were historically selected to perform distinct tasks and therefore may harbor a wealth of unique variation at the genome level. Copy number variation (CNV) along with single nonucleotide polymorphisms (SNPs) play a key role in genetic diversity in livestock species. CNVs, a term that refers to a change in the number of copies of a genomic segment, are responsible for more sequence differences between individuals than SNPs and are considered to be a major source of genetic variation contributing to differences in phenotypes (Beckmann et al, 2007). Several studies identified copy number variations in horses using different techniques (Doan et al, 2012). Part of these studies tried to establish associations between CNVs and a specific trait, a disease or even gene expression (Schurink et al, 2017). Most of these studies found either no association or inconclusive associations as the number of horses with phenotypic information or with specific CNVs were limited. For example, a 62 kb duplication on Equus caballus (ECA) chromosome 10 seemed to be related to recurrent laryngeal neuropathy (Dupuis et al, 2013). However, little is known about CNV in Iranian horses.

In this study, detection of CNVs and CNVRs were performed based on SNP data from Caspian and Turkman horse breeds were genotyped via Equine70k SNP beadchip. PennCNV software was only used to detect CNV on autosomes. The PennCNV algorithm was only applied to autosomes (command: -lastchr 26) to identify individual-based CNVs. To increase the confidence of the detected CNVs, quality control was performed by employing standard exclusions of the LRR (standard deviation of LRR) <0.3, a BAF drift <0.01 and a waviness factor <0.05. We classified the status of these CNV into two categories: “loss” (CNV containing a deletion) and “gain” (CNV containing a duplication). The CNVRs were determined by aggregating the overlapping CNVs with CNVRuler. BioMart in the Ensembl database and DAVID was employed to identify genes located in CNVRs and GO terms and KEGG pathway analyses respectively. Quantitative real-time PCR (qPCR) was applied to validate the CNVRs that were detected in this study.

A total of 202 and 105 CNVs and CNVRs were identified in the studied horses, respectively, which cover 1.08% of the horse genome. The number CNVs in Caspian breed were 1.6 times more than Turkmen. Also, the average size of CNVs in Caspian breed was longer than Turkmen. In both breeds, the genetic event of gain was higher than the genetic event of deletion. In Caspian breed, chromosomes 1, 3, 12, and in the Turkmen breed, chromosomes 1, 6 and 12 showed the most changes in CNVs, respectively. Functional analysis showed that the identified CNVRs overlapped with 434 genes and the most of these genes were common between the two horse breeds (more than 60%). Among these genes, PPARG and GALR have potential related with breed-specific traits. The KEGG pathway analysis also identified several pathways that are significantly enriched in olfactory sensory perception, chemical stimulus sensory perception, antigen processing, and G protein signaling pathway. Also, 60% of successfully detected CNVRs were confirmed by Real-time qPCR. The results of this study were compared with the results of eight other studies. For example, we concluded that the average size of the CNVRs detected by the 70k arrays and the 50K arrays were significantly larger than obtained by the CGH and NGS arrays. This may be due to the relatively low coverage and non-uniform distribution of SNP in the equine genome in SNP arrays. Possible reasons for the differences between our results and some CNV studies can be related to different parameters such as sample size and genetic background, different detection platforms and CNV retrieval algorithms, CNV definitions and CNVRs, as well as random error estimation methods (Pinto et al. 2011).

CNVs can describe part of the phenotypic diversity and adaptation evidence in Iranian horses. With regard to Genes identified in a number of cellular components, biological processes and molecular functions within CNVRs, the importance of such CNVRs and the possible effect needs to be studied and may interest insight into the functional and adaptive consequence of CNVs in horse. In total, the number of CNVs in the Caspian breed was greater than in the Turkmen breed, and also the CNV length in the number of copies in the Caspian breed was greater than in the Turkmen breed. In both breeds, there were overlapping genes with CNVRs that were significantly enriched in biological pathways, including sensory perception, immunity, and metabolism. This is the first CNV report on Turkmen and Caspian horses and the findings of this study could provide valuable information for better understanding of the horse genome and also the important performance traits with CNVRs and associated genes for the future studies in horse breeds.

Copy number variation (CNV), one of the most important structural changes in the genome, has been known as an important source of genetic and phenotypic variations. The purpose of this study was to compare the two different algorithms in CNV detection consisting PennCNV and QuantiSNP in Baluchi sheep.Data analysis was performed using the Illumina OvineSNP50k BeadChip on 96 Baluchi sheep.After CNV calling, the copy number variation regions (CNVRs) were determined using the CNVRuler program.91 CNVRs with a length range of 18.75 up to 511.7 kbp were identified by the PennCNV algorithm, covering 0.46% of whole sheep genome.Also, 316 CNVRs with the length range of 7.5 up to 1280 kbp were obtained using QuantiSNP algorithm, covering 1.33% of whole sheep genome. The number of loss events was about five and three times more than the number of gain events for QuantiSNP and PennCNV algorithms, respectively.Also, the number of CNVs detected by QuantiSNP was about four times higherthan PennCNV.Also, 86.6% of total CNVs (174 CNVs with average length of 12.62 kb) identified by PennCNV were common with CNVs detected by QuantiSNP. In general, the results showed that the use of several algorithms could improve the accuracy for detecting the structural variation in the genome and led to a better understanding of the sheep genome.