Abstract:Domestic chickens are excellent models for investigating the genetic basis of phenotypic diversity, as numerous phenotypic changes in physiology, morphology, and behavior in chickens have been artificially selected. Genomic study is required to study genome-wide patterns of DNA variation for dissecting the genetic basis of phenotypic traits. We sequenced the genomes of the Silkie and the Taiwanese native chicken L2 at ∼23- and 25-fold average coverage depth, respectively, using Illumina sequencing. The reads w… Show more
“…However, some previous CNV studies in chickens based on aCGH and SNP platforms mainly suffered from low resolution and sensitivity [9,[32][33][34][35]. A latest study exhibited the detection of four main types of genetic variation from whole genome sequencing data using two chickens [36], suggesting the efficiency of CNV detection via deep sequencing. Considering that a great number of CNVs appears to be segregating in distinct breeds, we selected 12 chickens from multiple breeds with extensive genetic diversity, including seven Chinese indigenous breeds [37], four commercial breeds and one Red Jungle Fowl.…”
Background: Copy number variation (CNV) is important and widespread in the genome, and is a major cause of disease and phenotypic diversity. Herein, we performed a genome-wide CNV analysis in 12 diversified chicken genomes based on whole genome sequencing. Results: A total of 8,840 CNV regions (CNVRs) covering 98.2 Mb and representing 9.4% of the chicken genome were identified, ranging in size from 1.1 to 268.8 kb with an average of 11.1 kb. Sequencing-based predictions were confirmed at a high validation rate by two independent approaches, including array comparative genomic hybridization (aCGH) and quantitative PCR (qPCR). The Pearson's correlation coefficients between sequencing and aCGH results ranged from 0.435 to 0.755, and qPCR experiments revealed a positive validation rate of 91.71% and a false negative rate of 22.43%. In total, 2,214 (25.0%) predicted CNVRs span 2,216 (36.4%) RefSeq genes associated with specific biological functions. Besides two previously reported copy number variable genes EDN3 and PRLR, we also found some promising genes with potential in phenotypic variation. Two genes, FZD6 and LIMS1, related to disease susceptibility/resistance are covered by CNVRs. The highly duplicated SOCS2 may lead to higher bone mineral density. Entire or partial duplication of some genes like POPDC3 may have great economic importance in poultry breeding. Conclusions: Our results based on extensive genetic diversity provide a more refined chicken CNV map and genome-wide gene copy number estimates, and warrant future CNV association studies for important traits in chickens.
“…However, some previous CNV studies in chickens based on aCGH and SNP platforms mainly suffered from low resolution and sensitivity [9,[32][33][34][35]. A latest study exhibited the detection of four main types of genetic variation from whole genome sequencing data using two chickens [36], suggesting the efficiency of CNV detection via deep sequencing. Considering that a great number of CNVs appears to be segregating in distinct breeds, we selected 12 chickens from multiple breeds with extensive genetic diversity, including seven Chinese indigenous breeds [37], four commercial breeds and one Red Jungle Fowl.…”
Background: Copy number variation (CNV) is important and widespread in the genome, and is a major cause of disease and phenotypic diversity. Herein, we performed a genome-wide CNV analysis in 12 diversified chicken genomes based on whole genome sequencing. Results: A total of 8,840 CNV regions (CNVRs) covering 98.2 Mb and representing 9.4% of the chicken genome were identified, ranging in size from 1.1 to 268.8 kb with an average of 11.1 kb. Sequencing-based predictions were confirmed at a high validation rate by two independent approaches, including array comparative genomic hybridization (aCGH) and quantitative PCR (qPCR). The Pearson's correlation coefficients between sequencing and aCGH results ranged from 0.435 to 0.755, and qPCR experiments revealed a positive validation rate of 91.71% and a false negative rate of 22.43%. In total, 2,214 (25.0%) predicted CNVRs span 2,216 (36.4%) RefSeq genes associated with specific biological functions. Besides two previously reported copy number variable genes EDN3 and PRLR, we also found some promising genes with potential in phenotypic variation. Two genes, FZD6 and LIMS1, related to disease susceptibility/resistance are covered by CNVRs. The highly duplicated SOCS2 may lead to higher bone mineral density. Entire or partial duplication of some genes like POPDC3 may have great economic importance in poultry breeding. Conclusions: Our results based on extensive genetic diversity provide a more refined chicken CNV map and genome-wide gene copy number estimates, and warrant future CNV association studies for important traits in chickens.
“…For example, using different analyses, we identified two genes, TSHR and TBC1D1, both of which were previously reported to regulate seasonal reproduction and growth among domestic chickens [1,25]. IG-F2BP3 was identified by its high score in the 10-kb grid size XP-CLR test (Figure 1), and further supported by the 5-kb and 2-kb grid size analyses (Supplementary information, Figure S2).…”
Section: Positively Selected Genes In Domestic Chickens Compared Withmentioning
As noted by Darwin, chickens have the greatest phenotypic diversity of all birds, but an interesting evolutionary difference between domestic chickens and their wild ancestor, the Red Junglefowl, is their comparatively weaker vision. Existing theories suggest that diminished visual prowess among domestic chickens reflect changes driven by the relaxation of functional constraints on vision, but the evidence identifying the underlying genetic mechanisms responsible for this change has not been definitively characterized. Here, a genome-wide analysis of the domestic chicken and Red Junglefowl genomes showed significant enrichment for positively selected genes involved in the development of vision. There were significant differences between domestic chickens and their wild ancestors regarding the level of mRNA expression for these genes in the retina. Numerous additional genes involved in the development of vision also showed significant differences in mRNA expression between domestic chickens and their wild ancestors, particularly for genes associated with phototransduction and photoreceptor development, such as RHO (rhodopsin), GUCA1A, PDE6B and NR2E3. Finally, we characterized the potential role of the VIT gene in vision, which experienced positive selection and downregulated expression in the retina of the village chicken. Overall, our results suggest that positive selection, rather than relaxation of purifying selection, contributed to the evolution of vision in domestic chickens. The progenitors of domestic chickens harboring weaker vision may have showed a reduced fear response and vigilance, making them easier to be unconsciously selected and/or domesticated.
“…Recently, population genomics studies, which search for genetic signatures of selection in the genome (Akey, 2009), have been conducted using high density SNPs genotyping and whole genome sequencing techniques in the livestock genome (e.g., Rubin et al, 2010;Elferink et al, 2012;Fan et al, 2013;Qanbari et al, 2015). This population genomics approach has allowed gene-level resolution mapping of loci that have been positively selected for, without any phenotypic information (Qanbari et al, 2014).…”
Chickens display a wide spectrum of phenotypic variations in quantitative traits such as egg-related traits. Quantitative trait locus (QTL) analysis is a statistical method used to understand the relationship between phenotypic (trait measurements) and genotypic data (molecular markers). We have performed QTL analyses for egg-related traits using an original resource population based on the Japanese Large Game (Oh-Shamo) and the White Leghorn breeds of chickens. In this article, we summarize the results of our extensive QTL analyses for 11 and 66 traits for egg production and egg quality, respectively. We reveal that at least 30 QTL regions on 17 different chromosomes affect phenotypic variation in egg-related traits. Each locus had an age-specific effect on traits, and a variety in effects was also apparent, such as additive, dominance, and epistatic-interaction effects. Although genome-wide association study (GWAS) is suitable for gene-level resolution mapping of GWAS loci with additive effects, QTL mapping studies enable us to comprehensively understand genetic control, such as chromosomal regions, genetic contribution to phenotypic variance, mode of inheritance, and age-specificity of both common and rare alleles. QTL analyses also describe the relationship between genotypes and phenotypes in experimental populations. Accumulation of QTL information, including GWAS loci, is also useful for studies of population genomics approached without phenotypic data in order to validate the identified genomic signatures of positive selection. The combination of QTL studies and next-generation sequencing techniques with uncharacterized genetic resources will enhance current understanding of the relationship between genotypes and phenotypes in livestock animals.
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