Dataset Information

A novel deletion in KRT75L4 mediates the frizzle trait in a Chinese indigenous chicken.

ABSTRACT: BACKGROUND:Highly diversified in morphology and structure, feathers have evolved into various forms. Frizzle feathers, which result from a developmental defect of the feather, are observed in several domestic chicken breeds. The frizzle phenotype is consistent with incomplete dominance of a major gene, but the molecular mechanisms that underlie this phenotype remain obscure. Kirin, a Chinese indigenous chicken breed that originated in the Guangdong province, is famous for its frizzle feathers. The KRT75 gene is considered as the dominant gene responsible for the frizzle trait in several chicken breeds, but this is not the case in the Kirin breed. Thus, the objective of our study was to investigate the genomic region and mutation responsible for this phenotype in this particular breed. RESULTS:A resource population was produced by crossing Kirin and Huaixiang chickens to produce F1 and F2 generations. DNA samples from 75 frizzle feather and normal feather individuals were sequenced with double-digest genotyping by sequencing (dd-GBS). After the detection of 525,561 high-quality variants, a genome-wide association analysis was carried out and the gene responsible for the frizzle phenotype was localized within the type II ?-keratin cluster on chromosome 33. Sanger sequencing was used to screen for mutations in the exons of five genes of this type II ?-keratin cluster. A 15-bp deletion in exon 3 of KRT75L4 that showed complete segregation with the frizzle phenotype was detected within the F2 population. Transcriptome sequencing demonstrated that KRT75L4 was expressed but that the transcript was shorter in Kirin than in Huaixiang chickens. In addition, by using Sanger sequencing, we were able to confirm that the deletion was in complete linkage with frizzle feathers. CONCLUSIONS:A deletion in the KRT75L4 gene is responsible for the frizzle feather phenotype in the Kirin chicken. The identification of this mutation, which causes a developmental defect of avian integument appendages, will improve our understanding of the mechanisms that are involved in feather formation.

SUBMITTER: Dong J

PROVIDER: S-EPMC6302451 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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A novel deletion in KRT75L4 mediates the frizzle trait in a Chinese indigenous chicken.

Dong Jing J He Chuan C Wang Zhibing Z Li Yanqing Y Li Shanshan S Tao Lin L Chen Jiebo J Li Donghua D Yang Fenxia F Li Naibin N Zhang Quan Q Zhang Li L Wang Guangqin G Akinyemi Fisayo F Meng He H Du Bingwang B

Genetics, selection, evolution : GSE 20181220 1

<h4>Background</h4>Highly diversified in morphology and structure, feathers have evolved into various forms. Frizzle feathers, which result from a developmental defect of the feather, are observed in several domestic chicken breeds. The frizzle phenotype is consistent with incomplete dominance of a major gene, but the molecular mechanisms that underlie this phenotype remain obscure. Kirin, a Chinese indigenous chicken breed that originated in the Guangdong province, is famous for its frizzle fea ...[more]

PMID: 30572816

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Project description:BackgroundIn China, consumers often prefer indigenous broiler chickens over commercial breeds, as they have characteristic meat qualities requested within traditional culinary customs. However, the growth-rate of these indigenous breeds is slower than that of the commercial broilers, which means they have not yet reached their full economic value. Therefore, combining the valuable meat quality of the native chickens with the efficiency of the commercial broilers is of interest. In this study, we generated an F2 intercross between the slow growing native broiler breed, Huiyang Beard chicken, and the fast growing commercial broiler breed, High Quality chicken Line A, and used it to map loci explaining the difference in growth rate between these breeds.ResultsA genome scan to identify main-effect loci affecting 24 growth-related traits revealed nine distinct QTL on six chromosomes. Many QTL were pleiotropic and conformed to the correlation patterns observed between phenotypes. Most of the mapped QTL were found in locations where growth QTL have been reported in other populations, although the effects were greater in this population. A genome scan for pairs of interacting loci identified a number of additional QTL in 10 other genomic regions. The epistatic pairs explained 6-8% of the residual phenotypic variance. Seven of the 10 epistatic QTL mapped in regions containing candidate genes in the ubiquitin mediated proteolysis pathway, suggesting the importance of this pathway in the regulation of growth in this chicken population.ConclusionsThe main-effect QTL detected using a standard one-dimensional genome scan accounted for a significant fraction of the observed phenotypic variance in this population. Furthermore, genes in known pathways present interesting candidates for further exploration. This study has thus located several QTL regions as promising candidates for further study, which will increase our understanding of the genetic mechanisms underlying growth-related traits in chickens.

Project description:BackgroundGenome-wide association studies and genomic predictions are thought to be optimized by using whole-genome sequence (WGS) data. However, sequencing thousands of individuals of interest is expensive. Imputation from SNP panels to WGS data is an attractive and less expensive approach to obtain WGS data. The aims of this study were to investigate the accuracy of imputation and to provide insight into the design and execution of genotype imputation.ResultsWe genotyped 450 chickens with a 600 K SNP array, and sequenced 24 key individuals by whole genome re-sequencing. Accuracy of imputation from putative 60 K and 600 K array data to WGS data was 0.620 and 0.812 for Beagle, and 0.810 and 0.914 for FImpute, respectively. By increasing the sequencing cost from 24X to 144X, the imputation accuracy increased from 0.525 to 0.698 for Beagle and from 0.654 to 0.823 for FImpute. With fixed sequence depth (12X), increasing the number of sequenced animals from 1 to 24, improved accuracy from 0.421 to 0.897 for FImpute and from 0.396 to 0.777 for Beagle. Using optimally selected key individuals resulted in a higher imputation accuracy compared with using randomly selected individuals as a reference population for re-sequencing. With fixed reference population size (24), imputation accuracy increased from 0.654 to 0.875 for FImpute and from 0.512 to 0.762 for Beagle as the sequencing depth increased from 1X to 12X. With a given total cost of genotyping, accuracy increased with the size of the reference population for FImpute, but the pattern was not valid for Beagle, which showed the highest accuracy at six fold coverage for the scenarios used in this study.ConclusionsIn conclusion, we comprehensively investigated the impacts of several key factors on genotype imputation. Generally, increasing sequencing cost gave a higher imputation accuracy. But with a fixed sequencing cost, the optimal imputation enhance the performance of WGP and GWAS. An optimal imputation strategy should take size of reference population, imputation algorithms, marker density, and population structure of the target population and methods to select key individuals into consideration comprehensively. This work sheds additional light on how to design and execute genotype imputation for livestock populations.

Dataset Information

A novel deletion in KRT75L4 mediates the frizzle trait in a Chinese indigenous chicken.

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A novel deletion in KRT75L4 mediates the frizzle trait in a Chinese indigenous chicken.

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