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Kebede FG, Derks MFL, Dessie T, Hanotte O, Barros CP, Crooijmans RPMA, Komen H, Bastiaansen JWM. Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens. BMC Genomics 2024; 25:284. [PMID: 38500079 PMCID: PMC10946127 DOI: 10.1186/s12864-024-10193-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 03/05/2024] [Indexed: 03/20/2024] Open
Abstract
Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6-7 populations per gradient). We performed signatures of selection analyses ([Formula: see text] and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified by[Formula: see text]and XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by [Formula: see text] are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations.
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Affiliation(s)
- Fasil Getachew Kebede
- Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, PB-6708, The Netherlands.
- International Livestock Research Institute, P.O. Box 5689, Addis Ababa, Ethiopia.
| | - Martijn F L Derks
- Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, PB-6708, The Netherlands
| | - Tadelle Dessie
- International Livestock Research Institute, P.O. Box 5689, Addis Ababa, Ethiopia
| | - Olivier Hanotte
- International Livestock Research Institute, P.O. Box 5689, Addis Ababa, Ethiopia
- School of Life Sciences, The University of Nottingham, Nottingham, NG7 2RD, UK
| | - Carolina Pita Barros
- Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, PB-6708, The Netherlands
| | - Richard P M A Crooijmans
- Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, PB-6708, The Netherlands
| | - Hans Komen
- Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, PB-6708, The Netherlands
| | - John W M Bastiaansen
- Animal Breeding and Genomics, Wageningen University & Research, Droevendaalsesteeg 1, Wageningen, PB-6708, The Netherlands
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2
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Haqani MI, Nakano M, Nagano AJ, Nakamura Y, Tsudzuki M. Association analysis of production traits of Japanese quail (Coturnix japonica) using restriction-site associated DNA sequencing. Sci Rep 2023; 13:21307. [PMID: 38042890 PMCID: PMC10693557 DOI: 10.1038/s41598-023-48293-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 10/10/2023] [Accepted: 11/24/2023] [Indexed: 12/04/2023] Open
Abstract
This study was designed to perform an association analysis and identify SNP markers associated with production traits of Japanese quail using restriction-site-associated DNA sequencing. Weekly body weight data from 805 quail were collected from hatching to 16 weeks of age. A total number of 3990 eggs obtained from 399 female quail were used to assess egg quality traits. Egg-related traits were measured at the beginning of egg production (first stage) and at 12 weeks of age (second stage). Five eggs were analyzed at each stage. Traits, such as egg weight, egg length and short axes, eggshell strength and weight, egg equator thickness, yolk weight, diameter, and colour, albumen weight, age of first egg, total number of laid eggs, and egg production rate, were assessed. A total of 383 SNPs and 1151 associations as well as 734 SNPs and 1442 associations were identified in relation to quail production traits using general linear model (GLM) and mixed linear model (MLM) approaches, respectively. The GLM-identified SNPs were located on chromosomes 1-13, 15, 17-20, 24, 26-28, and Z, underlying phenotypic traits, except for egg and albumen weight at the first stage and yolk yellowness at the second stage. The MLM-identified SNPs were positioned on defined chromosomes associated with phenotypic traits except for the egg long axis at the second stage of egg production. Finally, 35 speculated genes were identified as candidate genes for the targeted traits based on their nearest positions. Our findings provide a deeper understanding and allow a more precise genetic improvement of production traits of Galliformes, particularly in Japanese quail.
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Affiliation(s)
- Mohammad Ibrahim Haqani
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
| | - Michiharu Nakano
- Faculty of Agriculture and Marine Sciences, Kochi University, Nankoku, Kochi, 783-8502, Japan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku University, Otsu, Shiga, 520-2194, Japan
- Institute for Advanced Biosciences, Keio University, Yamagata, 997-0017, Japan
| | - Yoshiaki Nakamura
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan
| | - Masaoki Tsudzuki
- Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima, 739-8525, Japan.
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3
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Liu L, Chen Q, Yin L, Tang Y, Lin Z, Zhang D, Liu Y. A Comparison of the Meat Quality, Nutritional Composition, Carcass Traits, and Fiber Characteristics of Different Muscular Tissues between Aged Indigenous Chickens and Commercial Laying Hens. Foods 2023; 12:3680. [PMID: 37835333 PMCID: PMC10573064 DOI: 10.3390/foods12193680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/28/2023] [Accepted: 10/05/2023] [Indexed: 10/15/2023] Open
Abstract
The aim of this study is to assess the differences in the meat quality, nutritional composition, carcass traits, and myofiber characteristics between Hy-Line grey chickens (HLG, commercial breed) and Guangyuan grey chickens (GYG, indigenous breed). A total of 20 55-week-old chickens were selected for slaughter. The HLG exhibited a larger carcass weight, breast muscle weight, and abdominal fat weight (p < 0.05). The GYG exhibited a higher crude protein content, lower shear force, and smaller fiber size in the thigh muscles, whereas the HLG presented higher pH values and lower inosine-5'-monophosphate content in the breast muscles (p < 0.05). Darker meat based on higher redness and yellowness values was observed in the GYG instead of the HLG (p < 0.05). The research results also revealed parameter differences between different muscle types. Simultaneously, a correlation analysis showed significant correlations between the meat quality traits and myofiber characteristics (p < 0.05). In conclusion, aged indigenous chickens perform better in terms of tenderness and nutritional value in the thigh muscles, and may exhibit a better flavor in the breast muscles, but have a smaller breast muscle weight. Therefore, the current investigation provides a theoretical basis for the different needs of consumers and the processing of meat from old laying hens.
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Affiliation(s)
| | | | | | | | | | | | - Yiping Liu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
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Tian J, Zhu X, Wu H, Wang Y, Hu X. Serum metabolic profile and metabolome genome-wide association study in chicken. J Anim Sci Biotechnol 2023; 14:69. [PMID: 37138301 PMCID: PMC10158329 DOI: 10.1186/s40104-023-00868-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/09/2023] [Indexed: 05/05/2023] Open
Abstract
BACKGROUND Chickens provide globally important livestock products. Understanding the genetic and molecular mechanisms underpinning chicken economic traits is crucial for improving their selective breeding. Influenced by a combination of genetic and environmental factors, metabolites are the ultimate expression of physiological processes and can provide key insights into livestock economic traits. However, the serum metabolite profile and genetic architecture of the metabolome in chickens have not been well studied. RESULTS Here, comprehensive metabolome detection was performed using non-targeted LC-MS/MS on serum from a chicken advanced intercross line (AIL). In total, 7,191 metabolites were used to construct a chicken serum metabolomics dataset and to comprehensively characterize the serum metabolism of the chicken AIL population. Regulatory loci affecting metabolites were identified in a metabolome genome-wide association study (mGWAS). There were 10,061 significant SNPs associated with 253 metabolites that were widely distributed across the entire chicken genome. Many functional genes affect metabolite synthesis, metabolism, and regulation. We highlight the key roles of TDH and AASS in amino acids, and ABCB1 and CD36 in lipids. CONCLUSIONS We constructed a chicken serum metabolite dataset containing 7,191 metabolites to provide a reference for future chicken metabolome characterization work. Meanwhile, we used mGWAS to analyze the genetic basis of chicken metabolic traits and metabolites and to improve chicken breeding.
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Affiliation(s)
- Jing Tian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiaoning Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Hanyu Wu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
- National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing, 100193, China
| | - Yuzhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
- National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing, 100193, China.
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
- National Research Facility for Phenotypic and Genotypic Analysis of Model Animals (Beijing), China Agricultural University, Beijing, 100193, China.
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Wang F, Guo Y, Liu Z, Wang Q, Jiang Y, Zhao G. New insights into the novel sequences of the chicken pan-genome by liquid chip. J Anim Sci 2022; 100:6759641. [PMID: 36223424 PMCID: PMC9733507 DOI: 10.1093/jas/skac336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/11/2022] [Indexed: 12/15/2022] Open
Abstract
Increasing evidence indicates that the missing sequences and genes in the chicken reference genome are involved in many crucial biological pathways, including metabolism and immunity. The low detection rate of novel sequences by resequencing hindered the acquisition of these sequences and the exploration of the relationship between new genes and economic traits. To improve the capture ratio of novel sequences, a 48K liquid chip including 25K from the reference sequence and 23K from the novel sequence was designed. The assay was tested on a panel of 218 animals from 5 chicken breeds. The average capture ratio of the reference sequence was 99.55%, and the average sequencing depth of the target sites was approximately 187X, indicating a good performance and successful application of liquid chips in farm animals. For the target region in the novel sequence, the average capture ratio was 33.15% and the average sequencing depth of target sites was approximately 60X, both of which were higher than that of resequencing. However, the different capture ratios and capture regions among varieties and individuals proved the difficulty of capturing these regions with complex structures. After genotyping, GWAS showed variations in novel sequences potentially relevant to immune-related traits. For example, a SNP close to the differentiation of lymphocyte-related gene IGHV3-23-like was associated with the H/L ratio. These results suggest that targeted capture sequencing is a preferred method to capture these sequences with complex structures and genes potentially associated with immune-related traits.
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Affiliation(s)
| | | | | | - Qiao Wang
- State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yu Jiang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
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Comparative Study of Phenotypes and Genetics Related to the Growth Performance of Crossbred Thai Indigenous (KKU1 vs. KKU2) Chickens under Hot and Humid Conditions. Vet Sci 2022; 9:vetsci9060263. [PMID: 35737315 PMCID: PMC9228662 DOI: 10.3390/vetsci9060263] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/20/2022] [Accepted: 05/30/2022] [Indexed: 11/16/2022] Open
Abstract
To improve the body weight and growth performance traits of crossbred Thai indigenous chickens, phenotypic performance and genetic values were estimated. Crossbred Thai indigenous chickens, designated KKU1 and KKU2, were compared. The data included 1375 records of body weight (BW0, BW2, BW4, and BW16), breast circumference at 6 weeks of age (BrC6), and average daily gain (ADG0−2, ADG0−4, and ADG0−6). A multi-trait animal model with the average information-restricted maximum likelihood (AI-REML) was used to estimate the genetic parameters and breeding values. The results showed that the body weight and breast circumference traits (BW2, BW4, BW6, and BrC6) for the mixed sex KKU1 chickens were higher than for the KKU2 chickens (p < 0.05). For the growth performance traits, the KKU1 chickens had higher average daily gain and feed intake and a lower feed conversion ratio than the KKU2 chickens (p < 0.05). The survival rates were not different except at up to 6 weeks of age, when that of the KKU1 chickens was slightly lower. The specific combining ability, heritability, genetic and phenotypic correlations, and estimated breeding values showed that the KKU1 chickens had better genetics than the KKU2 chickens. In conclusion, KKU1 chickens are suitable for development as crossbred Thai indigenous chickens for enhanced growth performance and for commercial use.
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Yang R, Xu Z, Wang Q, Zhu D, Bian C, Ren J, Huang Z, Zhu X, Tian Z, Wang Y, Jiang Z, Zhao Y, Zhang D, Li N, Hu X. Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing. Genet Sel Evol 2021; 53:82. [PMID: 34706641 PMCID: PMC8555081 DOI: 10.1186/s12711-021-00672-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 09/08/2021] [Indexed: 12/25/2022] Open
Abstract
Background Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features. Results We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model. Conclusions A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features. Supplementary Information The online version contains supplementary material available at 10.1186/s12711-021-00672-9.
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Affiliation(s)
- Ruifei Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.,College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Zhenqiang Xu
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400, Guangdong Province, China
| | - Qi Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Di Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Cheng Bian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Jiangli Ren
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Zhuolin Huang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Xiaoning Zhu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Zhixin Tian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Yuzhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Ziqin Jiang
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400, Guangdong Province, China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Dexiang Zhang
- Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu, 527400, Guangdong Province, China.
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
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Wang K, Hu H, Tian Y, Li J, Scheben A, Zhang C, Li Y, Wu J, Yang L, Fan X, Sun G, Li D, Zhang Y, Han R, Jiang R, Huang H, Yan F, Wang Y, Li Z, Li G, Liu X, Li W, Edwards D, Kang X. The chicken pan-genome reveals gene content variation and a promoter region deletion in IGF2BP1 affecting body size. Mol Biol Evol 2021; 38:5066-5081. [PMID: 34329477 PMCID: PMC8557422 DOI: 10.1093/molbev/msab231] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Domestication and breeding have reshaped the genomic architecture of chicken, but the retention and loss of genomic elements during these evolutionary processes remain unclear. We present the first chicken pan-genome constructed using 664 individuals, which identified an additional ∼66.5 Mb sequences that are absent from the reference genome (GRCg6a). The constructed pan-genome encoded 20,491 predicated protein-coding genes, of which higher expression level are observed in conserved genes relative to dispensable genes. Presence/absence variation (PAV) analyses demonstrated that gene PAV in chicken was shaped by selection, genetic drift, and hybridization. PAV-based GWAS identified numerous candidate mutations related to growth, carcass composition, meat quality, or physiological traits. Among them, a deletion in the promoter region of IGF2BP1 affecting chicken body size is reported, which is supported by functional studies and extra samples. This is the first time to report the causal variant of chicken body size QTL located at chromosome 27 which was repeatedly reported. Therefore, the chicken pan-genome is a useful resource for biological discovery and breeding. It improves our understanding of chicken genome diversity and provides materials to unveil the evolution history of chicken domestication.
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Affiliation(s)
- Kejun Wang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Haifei Hu
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Crawley, 6009 WA, Australia
| | - Yadong Tian
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Jingyi Li
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, 430070 Wuhan, Hubei, China
| | - Armin Scheben
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Chenxi Zhang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Yiyi Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Junfeng Wu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Lan Yang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Xuewei Fan
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Guirong Sun
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Donghua Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Yanhua Zhang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Ruili Han
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Ruirui Jiang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Hetian Huang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Fengbin Yan
- Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Yanbin Wang
- Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Zhuanjian Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Guoxi Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Xiaojun Liu
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - Wenting Li
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Crawley, 6009 WA, Australia
| | - Xiangtao Kang
- College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046, China.,Henan Key laboratory for innovation and utilization of chicken germplasm resources,Zhengzhou, 450046, China
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Genome-Wide Association Study Identifies 12 Loci Associated with Body Weight at Age 8 Weeks in Korean Native Chickens. Genes (Basel) 2021; 12:genes12081170. [PMID: 34440344 PMCID: PMC8394794 DOI: 10.3390/genes12081170] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/24/2021] [Accepted: 07/26/2021] [Indexed: 02/07/2023] Open
Abstract
Meat from Korean native chickens (KNCs) has high consumer demand; however, slow growth performance and high variation in body weight (BW) of KNCs remain an issue. Genome-wide association study (GWAS) is a powerful method to identify quantitative trait-associated genomic loci. A GWAS, based on a large-scale KNC population, is needed to identify underlying genetic mechanisms related to its growth traits. To identify BW-associated genomic regions, we performed a GWAS using the chicken 60K single nucleotide polymorphism (SNP) panel for 1328 KNCs. BW was measured at 8 weeks of age, from 2018 to 2020. Twelve SNPs were associated with BW at the suggestive significance level (p < 2.95 × 10−5) and located near or within 11 candidate genes, including WDR37, KCNIP4, SLIT2, PPARGC1A, MYOCD and ADGRA3. Gene set enrichment analysis based on the GWAS results at p < 0.05 (1680 SNPs) showed that 32 Gene Ontology terms and two Kyoto Encyclopedia of Genes and Genomes pathways, including regulation of transcription, motor activity, the mitogen-activated protein kinase signaling pathway, and tight junction, were significantly enriched (p < 0.05) for BW-associated genes. These pathways are involved in cell growth and development, related to BW gain. The identified SNPs are potential biomarkers in KNC breeding.
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Wu Z, Bortoluzzi C, Derks MFL, Liu L, Bosse M, Hiemstra SJ, Groenen MAM, Crooijmans RPMA. Heterogeneity of a dwarf phenotype in Dutch traditional chicken breeds revealed by genomic analyses. Evol Appl 2021; 14:1095-1108. [PMID: 33897823 PMCID: PMC8061282 DOI: 10.1111/eva.13183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/29/2020] [Accepted: 12/06/2020] [Indexed: 12/14/2022] Open
Abstract
The growth of animals is a complex trait, in chicken resulting in a diverse variety of forms, caused by a heterogeneous genetic basis. Bantam chicken, known as an exquisite form of dwarfism, has been used for crossbreeding to create corresponding dwarf counterparts for native fowls in the Dutch populations. Here, we demonstrate the heterogeneity of the bantam trait in Dutch chickens and reveal the underlying genetic causes, using whole-genome sequence data from matching pairs of bantam and normal-sized breeds. During the bantam-oriented crossbreeding, various bantam origins were used to introduce the bantam phenotype, and three major bantam sources were identified and clustered. The genome-wide association studies revealed multiple genetic variants and genes associated with bantam phenotype, including HMGA2 and PRDM16, genes involved in body growth and stature. The comparison of associated variants among studies illustrated differences related to divergent bantam origins, suggesting a clear heterogeneity among bantam breeds. We show that in neo-bantam breeds, the bantam-related regions underwent a strong haplotype introgression from the bantam source, outcompeting haplotypes from the normal-sized counterpart. The bantam heterogeneity is further confirmed by the presence of multiple haplotypes comprising associated alleles, which suggests the selection of the bantam phenotype is likely subject to a convergent direction across populations. Our study demonstrates that the diverse history of human-mediated crossbreeding has contributed to the complexity and heterogeneity of the bantam phenotype.
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Affiliation(s)
- Zhou Wu
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Chiara Bortoluzzi
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Martijn F. L. Derks
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Langqing Liu
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Mirte Bosse
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
| | - Sipke Joost Hiemstra
- Centre for Genetic Resources, the Netherlands (CGN) of Wageningen University & ResearchWageningenThe Netherlands
| | - Martien A. M. Groenen
- Wageningen University & Research, Animal Breeding and GenomicsWageningenThe Netherlands
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11
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Guo L, Huang W, Tong F, Chen X, Cao S, Xu H, Luo W, Li Z, Nie Q. Whole Transcriptome Analysis of Chicken Bursa Reveals Candidate Gene That Enhances the Host's Immune Response to Coccidiosis. Front Physiol 2020; 11:573676. [PMID: 33192575 PMCID: PMC7662072 DOI: 10.3389/fphys.2020.573676] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 10/08/2020] [Indexed: 12/11/2022] Open
Abstract
Coccidiosis is a major hazard to the chicken industry, but the host’s immune response to coccidiosis remains unclear. Here, we performed Eimeria coccidia challenge in 28-day-old ROSS 308 broilers and selected the bursa from the three most severely affected individuals and three healthy individuals for RNA sequencing. We obtained 347 DEGs from RNA-seq and found that 7 upregulated DEGs were enriched in Cytokine-cytokine receptor interaction pathway. As the DEGs with the highest expression abundance in these 7 genes, TNFRSF6B was speculated to participate in the process of host’s immune response to coccidiosis. It is showed that TNFRSF6B can polarize macrophages to M1 subtype and promote inflammatory cytokines expression. In addition, the expression of TNFRSF6B suppressed HD11 cells apoptosis by downregulating Fas signal pathway. Besides, TNFRSF6B-mediated macrophages immunity activation can be reversed by apoptosis. Overall, our study indicates that TNFRSF6B upregulated in BAE, is capable of aggravating the inflammatory response by inhibiting macrophages apoptosis via downregulating Fas signal pathway, which may participate in host’s immune response to coccidiosis.
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Affiliation(s)
- Lijin Guo
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Weiling Huang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Feng Tong
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Xiaolan Chen
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Sen Cao
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Haiping Xu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Wei Luo
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Zhenhui Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
| | - Qinghua Nie
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Agro-Animal Genomics and Molecular Breeding, Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou, China.,Guangdong Laboratory for Lingnan Modern Agricultural Science and Technology, South China Agricultural University, Guangzhou, China
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12
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Li W, Jing Z, Cheng Y, Wang X, Li D, Han R, Li W, Li G, Sun G, Tian Y, Liu X, Kang X, Li Z. Analysis of four complete linkage sequence variants within a novel lncRNA located in a growth QTL on chromosome 1 related to growth traits in chickens. J Anim Sci 2020; 98:5822640. [PMID: 32309860 DOI: 10.1093/jas/skaa122] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 04/16/2020] [Indexed: 12/18/2022] Open
Abstract
An increasing number of studies have shown that quantitative trait loci (QTLs) at the end of chromosome 1 identified in different chicken breeds and populations exert significant effects on growth traits in chickens. Nevertheless, the causal genes underlying the QTL effect remain poorly understood. Using an updated gene database, a novel lncRNA (named LncFAM) was found at the end of chromosome 1 and located in a growth and digestion QTL. This study showed that the expression level of LncFAM in pancreas tissues with a high weight was significantly higher than that in pancreas tissues with a low weight, which indicates that the expression level of LncFAM was positively correlated with various growth phenotype indexes, such as growth speed and body weight. A polymorphism screening identified four polymorphisms with strong linkage disequilibrium in LncFAM: a 5-bp indel in the second exon, an A/G base mutation, and 7-bp and 97-bp indels in the second intron. A study of a 97-bp insertion in the second intron using an F2 chicken resource population produced by Anka and Gushi chickens showed that the mutant individuals with genotype II had the highest values for body weight (BW) at 0 days and 2, 4, 6, 8, 10 and 12 weeks, shank girth (SG) at 4, 8 and 12 weeks, chest width (CW) at 4, 8 and 12 weeks, body slant length (BSL) at 8 and 12 weeks, and pelvic width (PW) at 4, 8 and 12 weeks, followed by ID and DD genotypes. The amplification and typing of 2,716 chickens from ten different breeds, namely, the F2 chicken resource population, dual-type chickens, including Xichuan black-bone chickens, Lushi green-shell layers, Dongxiang green-shell layers, Changshun green-shell layers, and Gushi chickens, and commercial broilers, including Ross 308, AA, Cobb and Hubbard broilers, revealed that II was the dominant genotype. Interestingly, only genotype II existed among the tested populations of commercial broilers. Moreover, the expression level in the pancreas tissue of Ross 308 chickens was significantly higher than that in the pancreas tissue of Gushi chickens (P < 0.001), which might be related to the conversion rates among different chickens. The prediction and verification of the target gene of LncFAM showed that LncFAM might regulate the expression of its target gene FAM48A through cis-expression. Our results provide useful information on the mutation of LncFAM, which can be used as a potential molecular breeding marker.
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Affiliation(s)
- Wenya Li
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Zhenzhu Jing
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Yingying Cheng
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China
| | - Xiangnan Wang
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Donghua Li
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Ruili Han
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Wenting Li
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Guoxi Li
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Guirong Sun
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Yadong Tian
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Xiaojun Liu
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Xiangtao Kang
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
| | - Zhuanjian Li
- College of Animal Science and Veterinary Medicine, Henan Agricultural University, Zhengzhou, Henan, China.,Henan Innovative Engineering Research Center of Poultry Germplasm Resource, Zhengzhou
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13
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Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F 2 chicken population. Heredity (Edinb) 2020; 126:293-307. [PMID: 32989280 PMCID: PMC8026619 DOI: 10.1038/s41437-020-00365-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 08/18/2020] [Accepted: 08/30/2020] [Indexed: 02/07/2023] Open
Abstract
Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F2 resource population were phenotyped for 68 growth and carcass traits, and 768 samples were genotyped based on the genotyping-by-sequencing (GBS) method. Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1-6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.
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14
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Wang Y, Bu L, Cao X, Qu H, Zhang C, Ren J, Huang Z, Zhao Y, Luo C, Hu X, Shu D, Li N. Genetic Dissection of Growth Traits in a Unique Chicken Advanced Intercross Line. Front Genet 2020; 11:894. [PMID: 33033489 PMCID: PMC7509424 DOI: 10.3389/fgene.2020.00894] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/20/2020] [Indexed: 12/23/2022] Open
Abstract
The advanced intercross line (AIL) that is created by successive generations of pseudo-random mating after the F2 generation is a valuable resource, especially in agricultural livestock and poultry species, because it improves the precision of quantitative trait loci (QTL) mapping compared with traditional association populations by introducing more recombination events. The growth traits of broilers have significant economic value in the chicken industry, and many QTLs affecting growth traits have been identified, especially on chromosomes 1, 4, and 27, albeit with large confidence intervals that potentially contain dozens of genes. To promote a better understanding of the underlying genetic architecture of growth trait differences, specifically body weight and bone development, in this study, we report a nine-generation AIL derived from two divergent outbred lines: High Quality chicken Line A (HQLA) and Huiyang Bearded (HB) chicken. We evaluate the genetic architecture of the F0, F2, F8, and F9 generations of AIL and demonstrate that the population of the F9 generation sufficiently randomized the founder genomes and has the characteristics of rapid linkage disequilibrium decay, limited allele frequency decline, and abundant nucleotide diversity. This AIL yielded a much narrower QTL than the F2 generations, especially the QTL on chromosome 27, which was reduced to 120 Kb. An ancestral haplotype association analysis showed that most of the dominant haplotypes are inherited from HQLA but with fluctuation of the effects between them. We highlight the important role of four candidate genes (PHOSPHO1, IGF2BP1, ZNF652, and GIP) in bone growth. We also retrieved a missing QTL from AIL on chromosome 4 by identifying the founder selection signatures, which are explained by the loss of association power that results from rare alleles. Our study provides a reasonable resource for detecting quantitative trait genes and tracking ancestor history and will facilitate our understanding of the genetic mechanisms underlying chicken bone growth.
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Affiliation(s)
- Yuzhe Wang
- College of Animal Science and Technology, China Agricultural University, Beijing, China.,State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Lina Bu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Xuemin Cao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Hao Qu
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Chunyuan Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Jiangli Ren
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Zhuolin Huang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Chenglong Luo
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Dingming Shu
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
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15
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Wang Y, Cao X, Luo C, Sheng Z, Zhang C, Bian C, Feng C, Li J, Gao F, Zhao Y, Jiang Z, Qu H, Shu D, Carlborg Ö, Hu X, Li N. Multiple ancestral haplotypes harboring regulatory mutations cumulatively contribute to a QTL affecting chicken growth traits. Commun Biol 2020; 3:472. [PMID: 32859973 PMCID: PMC7455696 DOI: 10.1038/s42003-020-01199-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 08/03/2020] [Indexed: 01/04/2023] Open
Abstract
In depth studies of quantitative trait loci (QTL) can provide insights to the genetic architectures of complex traits. A major effect QTL at the distal end of chicken chromosome 1 has been associated with growth traits in multiple populations. This locus was fine-mapped in a fifteen-generation chicken advanced intercross population including 1119 birds and explored in further detail using 222 sequenced genomes from 10 high/low body weight chicken stocks. We detected this QTL that, in total, contributed 14.4% of the genetic variance for growth. Further, nine mosaic precise intervals (Kb level) which contain ancestral regulatory variants were fine-mapped and we chose one of them to demonstrate the key regulatory role in the duodenum. This is the first study to break down the detail genetic architectures for the well-known QTL in chicken and provides a good example of the fine-mapping of various of quantitative traits in any species.
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Affiliation(s)
- Yuzhe Wang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.,College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Xuemin Cao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Chenglong Luo
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Zheya Sheng
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.,Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chunyuan Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.,Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, 100193, China
| | - Cheng Bian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Chungang Feng
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Jinxiu Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Fei Gao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.,College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China.,Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, 100193, China
| | - Yiqiang Zhao
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.,Beijing Advanced Innovation Center for Food Nutrition and Human Health, China Agricultural University, Beijing, 100193, China
| | - Ziqin Jiang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Hao Qu
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Dingming Shu
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China.
| | - Örjan Carlborg
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, SE-751 23, Sweden.
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.
| | - Ning Li
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
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16
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Cao X, Wang Y, Shu D, Qu H, Luo C, Hu X. Food intake-related genes in chicken determined through combinatorial genome-wide association study and transcriptome analysis. Anim Genet 2020; 51:741-751. [PMID: 32720725 DOI: 10.1111/age.12980] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/15/2020] [Indexed: 11/30/2022]
Abstract
The chicken gizzard is the primary digestive and absorptive organ regulating food intake and metabolism. Body weight is a typical complex trait regulated by an interactive polygene network which is under the control of an interacting network of polygenes. To simplify these genotype-phenotype associations, the gizzard is a suitable target organ to preliminarily explore the mechanism underlying the regulation of chicken growth through controlled food intake. This study aimed to identify key food intake-related genes through combinatorial GWAS and transcriptome analysis. We performed GWAS of body weight in an F2 intercrossed population and transcriptional profiling analysis of gizzards from chickens with different body weight. We identified a major 10 Mb quantitative trait locus (QTL) on chromosome 1 and numerous minor QTL distributed among 24 chromosomes. Combining data regarding QTL and gizzard gene expression, two hub genes, MLNR and HTR2A, and a list of core genes with small effect were found to be associated with food intake. Furthermore, the neuroactive ligand-receptor interaction pathway was found to play a key role in regulating the appetite of chickens. The present results show the major-minor gene interactions in metabolic pathways and provide insights into the genetic architecture and gene regulation during food intake in chickens.
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Affiliation(s)
- Xuemin Cao
- State Key Laboratory of Agro-Biotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Yuzhe Wang
- State Key Laboratory of Agro-Biotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China.,College of Animal Science and Technology, China Agricultural University, Beijing, 100193, China
| | - Dingming Shu
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Hao Qu
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Chenglong Luo
- State Key Laboratory of Livestock and Poultry Breeding, Guangdong Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China
| | - Xiaoxiang Hu
- State Key Laboratory of Agro-Biotechnology, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
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17
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Huang T, Pu Y, Song C, Sheng Z, Hu X. A quantitative trait locus on chromosome 2 was identified that accounts for a substantial proportion of phenotypic variance of the yellow plumage color in chicken. Poult Sci 2020; 99:2902-2910. [PMID: 32475423 PMCID: PMC7597730 DOI: 10.1016/j.psj.2020.01.030] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 12/02/2019] [Accepted: 01/01/2020] [Indexed: 12/12/2022] Open
Abstract
Chicken plumage color is an important economical trait in poultry breeding, as triple-yellow indigenous broilers are preferred over western commercial broilers in the Chinese market. However, the studies on the pigmentation of plumage coloration are relatively rare at present. Here, we performed a genome-wide mapping study on an F2 intercross, whose 2 founders were one hybrid commercial line “High Quality chicken Line A” that originated from the Anak red chicken and one indigenous line “Huiyang Beard” chicken that is a classical “triple-yellow” Chinese indigenous breed. Moreover, we used an automatic colorimeter that can quantitatively assess the colorations in L∗, a∗, and b∗ values. One major quantitative trait locus (QTL) on chromosome 2 was thus identified by both genome-wide association and linkage analyses, which could explain 10 to 20% of the total phenotypic variance of the b∗ measurements of the back plumage color. Using linkage analysis, 2 additional QTL on chromosome 1 and 20 were also found to be significantly associated with the plumage coloration in this cross. With additional samples from Anak red and Huiyang Beard chickens as well as pooled resequencing data from the 2 founders of this cross, we then further narrowed down the QTL regions and identified several candidate genes, such as CABLES1, CHST11, BCL2L1, and CHD22. As the effects of QTL found in this study were substantial, quantitatively measuring the coloration rather than the descriptive measurements provides stronger statistical power for the analyses. In addition, this major QTL on chromosome 2 that was associated with feather pigmentation at the genome-wide level will facilitate the future chicken breeding for yellow plumage color. In conclusions, we mapped 3 associated QTL on chromosome 1, 2, and 20. The candidate genes identified in this study shed light in the genetic basis of yellow plumage color in chicken.
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Affiliation(s)
- Tao Huang
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan, Hubei Province, China; Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, Hubei Province, China
| | - Yuejin Pu
- Institute of Animal Husbandry and Veterinary, Hubei Academy of Agricultural Science, Wuhan, Hubei Province, China
| | - Chi Song
- State Key Laboratory for Agro-Biotechnology, China Agricultural University, Beijing, China
| | - Zheya Sheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, Hubei Province, China.
| | - Xiaoxiang Hu
- State Key Laboratory for Agro-Biotechnology, China Agricultural University, Beijing, China.
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18
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Liu T, Luo C, Ma J, Wang Y, Shu D, Su G, Qu H. High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers. Front Genet 2020; 11:108. [PMID: 32174971 PMCID: PMC7056902 DOI: 10.3389/fgene.2020.00108] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 01/30/2020] [Indexed: 11/13/2022] Open
Abstract
The choice of a genetic marker genotyping platform is important for genomic prediction in livestock and poultry. High-throughput sequencing can produce more genetic markers, but the genotype quality is lower than that obtained with single nucleotide polymorphism (SNP) chips. The aim of this study was to compare the accuracy of genomic prediction between high-throughput sequencing and SNP chips in broilers. In this study, we developed a new SNP marker screening method, the pre-marker-selection (PMS) method, to determine whether an SNP marker can be used for genomic prediction. We also compared a method which preselection marker based results from genome-wide association studies (GWAS). With the two methods, we analysed body weight at the12th week (BW) and feed conversion ratio (FCR) in a local broiler population. A total of 395 birds were selected from the F2 generation of the population, and 10X specific-locus amplified fragment sequencing (SLAF-seq) and the Illumina Chicken 60K SNP Beadchip were used for genotyping. The genomic best linear unbiased prediction method (GBLUP) was used to predict the genomic breeding values. The accuracy of genomic prediction was validated by the leave-one-out cross-validation method. Without SNP marker screening, the accuracies of the genomic estimated breeding value (GEBV) of BW and FCR were 0.509 and 0.249, respectively, when using SLAF-seq, and the accuracies were 0.516 and 0.232, respectively, when using the SNP chip. With SNP marker screening by the PMS method, the accuracies of GEBV of the two traits were 0.671 and 0.499, respectively, when using SLAF-seq, and 0.605 and 0.422, respectively, when using the SNP chip. Our SNP marker screening method led to an increase of prediction accuracy by 0.089-0.250. With SNP marker screening by the GWAS method, the accuracies of genomic prediction for the two traits were also improved, but the gains of accuracy were less than the gains with PMS method for all traits. The results from this study indicate that our PMS method can improve the accuracy of GEBV, and that more accurate genomic prediction can be obtained from an increased number of genomic markers when using high-throughput sequencing in local broiler populations. Due to its lower genotyping cost, high-throughput sequencing could be a good alternative to SNP chips for genomic prediction in breeding programmes of local broiler populations.
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Affiliation(s)
- Tianfei Liu
- State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Chenglong Luo
- State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Jie Ma
- Guangdong Provincial Key Laboratory of Animal Breeding and Nutrition, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Yan Wang
- State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Dingming Shu
- State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Hao Qu
- State Key Laboratory of Livestock and Poultry Breeding, Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
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Li JJ, Zhang L, Ren P, Wang Y, Yin LQ, Ran JS, Zhang XX, Liu YP. Genotype frequency distributions of 28 SNP markers in two commercial lines and five Chinese native chicken populations. BMC Genet 2020; 21:12. [PMID: 32019486 PMCID: PMC7001339 DOI: 10.1186/s12863-020-0815-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 01/27/2020] [Indexed: 11/18/2022] Open
Abstract
Background Modern breeding in the poultry industry mainly aims to produce high-performance poultry lines and breeds in two main directions of productivity, meat and eggs. To understand more about the productive potential of lowly selected Chinese native chicken populations, we selected 14 representative SNP markers strongly associated with growth traits or carcass traits and 14 SNP markers strongly associated with egg laying traits through previous reports. By using the MassArray technology, we detected the genotype frequency distributions of these 28 SNP markers in seven populations including four lowly selected as well as one moderately selected Sichuan native chicken populations, one commercial broiler line and one commercial layer line. Results Based on the genotype frequency distributions of these 28 SNP markers in 5 native chicken populations and 2 commercial lines, the results suggested that these Chinese indigenous chicken populations have a relatively close relationship with the commercial broiler line but a marked distinction from the commercial layer line. Two native chicken breeds, Shimian Caoke Chicken and Daheng Broilers, share similar genetic structure with the broiler line. Conclusions Our observations may help us to better select and breed superior domestic chickens and provide new clues for further study of breeding programs in local chicken populations.
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Affiliation(s)
- Jing-Jing Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Long Zhang
- Institute of Ecology, Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong, 637009, Sichuan, China
| | - Peng Ren
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Ye Wang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Ling-Qian Yin
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Jin-Shan Ran
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Xian-Xian Zhang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China
| | - Yi-Ping Liu
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.
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Cao H, Dong X, Mao H, Xu N, Yin Z. Expression Analysis of the PITX2 Gene and Associations between Its Polymorphisms and Body Size and Carcass Traits in Chickens. Animals (Basel) 2019; 9:ani9121001. [PMID: 31756915 PMCID: PMC6940742 DOI: 10.3390/ani9121001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/16/2019] [Accepted: 11/16/2019] [Indexed: 12/20/2022] Open
Abstract
Simple Summary The Wuliang Mountain Black-bone chicken is a Chinese indigenous breed with good meat quality and strong resistance to disease. Like most of the other Chinese domestic breeds, it has a much slower early growth rate compared with foreign chicken breeds. Therefore, the genetic selection of body size and carcass traits is still the focus of Chinese indigenous chicken breeding. The paired-like homeodomain transcription factor 2 (PITX2) gene, an important transcription factor, plays an important role during the development of the eye, heart, skeletal muscle and other tissues in mammals. In chicken, the PITX2 gene affects the late myogenic differentiation of the limb. The objectives of this study were to detect the expression of the PITX2 gene and analyze the associations between the polymorphisms in the exons of the PITX2 gene and body size as well as carcass traits in chickens. The results could contribute to Chinese chicken breeding based on marker assisted-selection. Abstract PITX2 is expressed in and plays an important role in myocytes of mice, and it has effects on late myogenic differentiation in chickens. However, the expression profile and polymorphisms of PITX2 remain unclear in chickens. Therefore, the aim of the present study was to detect its expression and investigate single nucleotide polymorphisms (SNPs) within its exons and then to evaluate whether these polymorphisms affect body size as well as carcass traits in chickens. The expression analysis showed that the expression level of chicken PITX2 mRNA in the leg muscle and hypophysis was significantly higher (p < 0.01) than those in other tissues. The results of polymorphisms analysis identified two SNPs (i.e., g.9830C > T and g.10073C > T) in exon 1 and 10 SNPs (i.e., g.12713C > T, g.12755C > T, g.12938G > A, g. 3164C > T, g.13019G > A, g.13079G > A, g.13285G > A, g.13335G > A, g.13726A > G and g.13856C > T) in exon 3, including four novel SNPs (i.e., g.9830C > T, g.12713C > T, g.12938G > A and g.13856C > T). In the loci of g.10073C > T and g.12713C > T, chickens with the CT genotype had the highest (p < 0.05 or p < 0.01) breast depth and breast angle, respectively. For the locus of g.13335G > A, chickens with the GG genotype had the highest (p < 0.05 or p < 0.01) breast angle and shank circumference. For the locus of g.13726A > G, chickens with the GG genotype had the highest breast width, fossil keel bone length and shank circumference. The locus of g.12713A > G had significant effects on the PITX2 mRNA expression level in leg muscle. The H1H7 diplotype showed the highest shank circumference, and the H2H8 diplotype showed the highest breast muscle rate. The present research suggested that polymorphisms of the exons of the PITX2 gene were significantly associated with the body size and carcass traits of Wuliang Mountain Black-bone chickens and the PITX2 gene could be a potential candidate gene for molecular marker-aided selection in Wuliang Mountain Black-bone chickens and other chicken breeds.
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21
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Lin Y, Tang Q, Li Y, He M, Jin L, Ma J, Wang X, Long K, Huang Z, Li X, Gu Y, Li M. Genomic analyses provide insights into breed-of-origin effects from purebreds on three-way crossbred pigs. PeerJ 2019; 7:e8009. [PMID: 31737448 PMCID: PMC6855203 DOI: 10.7717/peerj.8009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 10/07/2019] [Indexed: 11/20/2022] Open
Abstract
Crossbreeding is widely used aimed at improving crossbred performance for poultry and livestock. Alleles that are specific to different purebreds will yield a large number of heterozygous single-nucleotide polymorphisms (SNPs) in crossbred individuals, which are supposed to have the power to alter gene function or regulate gene expression. For pork production, a classic three-way crossbreeding system of Duroc × (Landrace × Yorkshire) (DLY) is generally used to produce terminal crossbred pigs with stable and prominent performance. Nonetheless, little is known about the breed-of-origin effects from purebreds on DLY pigs. In this study, we first estimated the distribution of heterozygous SNPs in three kinds of three-way crossbred pigs via whole genome sequencing data originated from three purebreds. The result suggested that DLY is a more effective strategy for three-way crossbreeding as it could yield more stably inherited heterozygous SNPs. We then sequenced a DLY pig family and identified 95, 79, 132 and 42 allele-specific expression (ASE) genes in adipose, heart, liver and skeletal muscle, respectively. Principal component analysis and unrestricted clustering analyses revealed the tissue-specific pattern of ASE genes, indicating the potential roles of ASE genes for development of DLY pigs. In summary, our findings provided a lot of candidate SNP markers and ASE genes for DLY three-way crossbreeding system, which may be valuable for pig breeding and production in the future.
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Affiliation(s)
- Yu Lin
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Qianzi Tang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Yan Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Mengnan He
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Long Jin
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Jideng Ma
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Xun Wang
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Keren Long
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Zhiqing Huang
- Institute of Animal Nutrition, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Xuewei Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
| | - Yiren Gu
- Sichuan Animal Science Academy, Chengdu, Sichuan, China
| | - Mingzhou Li
- Institute of Animal Genetics and Breeding, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, China
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22
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Quantitative trait loci and candidate genes for the economic traits in meat-type chicken. WORLD POULTRY SCI J 2019. [DOI: 10.1017/s0043933914000348] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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23
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Chen J, Ren X, Li L, Lu S, Chen T, Tan L, Liu M, Luo Q, Liang S, Nie Q, Zhang X, Luo W. Integrative Analyses of mRNA Expression Profile Reveal the Involvement of IGF2BP1 in Chicken Adipogenesis. Int J Mol Sci 2019; 20:ijms20122923. [PMID: 31208008 PMCID: PMC6627201 DOI: 10.3390/ijms20122923] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 06/08/2019] [Accepted: 06/12/2019] [Indexed: 02/07/2023] Open
Abstract
Excessive abdominal fat deposition is an issue with general concern in broiler production, especially for Chinese native chicken breeds. A high-fat diet (HFD) can induce body weight gained and excessive fat deposition, and genes and pathways participate in fat metabolism and adipogenesis would be influenced by HFD. In order to reveal the main genes and pathways involved in chicken abdominal fat deposition, we used HFD and normal diet (ND) to feed a Chinese native chicken breed, respectively. Results showed that HFD can increase abdominal fat deposition and induce adipocyte hypertrophy. Additionally, we used RNA-sequencing to identify the differentially expressed genes (DEGs) between HFD and ND chickens in liver and abdominal fat. By analyzed these DEGs, we found that the many DEGs were enriched in fat metabolism related pathways, such as peroxisome proliferator-activated receptor (PPAR) signaling, fat digestion and absorption, extracellular matrix (ECM)-receptor interaction, and steroid hormone biosynthesis. Notably, the expression of insulin-like growth factor II mRNA binding protein 1 (IGF2BP1), which is a binding protein of IGF2 mRNA, was found to be induced in liver and abdominal fat by HFD. Ectopic expression of IGF2BP1 in chicken liver-related cell line Leghorn strain M chicken hepatoma (LMH) cell revealed that IGF2BP1 can regulate the expression of genes associated with fatty acid metabolism. In chicken preadipocytes (ICP cell line), we found that IGF2BP1 can promote adipocyte proliferation and differentiation, and the lipid droplet content would be increased by overexpression of IGF2BP1. Taken together, this study provides new insights into understanding the genes and pathways involved in abdominal fat deposition of Chinese native broiler, and IGF2BP1 is an important candidate gene for the study of fat metabolism and adipogenesis in chicken.
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Affiliation(s)
- Jiahui Chen
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Xueyi Ren
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Limin Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Shiyi Lu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Tian Chen
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Liangtian Tan
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Manqing Liu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Qingbin Luo
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Shaodong Liang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Qinghua Nie
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Xiquan Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Wen Luo
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, South China Agricultural University, Guangzhou 510642, China.
- Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou 510642, China.
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Bidirectional Selection for Body Weight on Standing Genetic Variation in a Chicken Model. G3-GENES GENOMES GENETICS 2019; 9:1165-1173. [PMID: 30737239 PMCID: PMC6469407 DOI: 10.1534/g3.119.400038] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Experimental populations of model organisms provide valuable opportunities to unravel the genomic impact of selection in a controlled system. The Virginia body weight chicken lines represent a unique resource to investigate signatures of selection in a system where long-term, single-trait, bidirectional selection has been carried out for more than 60 generations. At 55 generations of divergent selection, earlier analyses of pooled genome resequencing data from these lines revealed that 14.2% of the genome showed extreme differentiation between the selected lines, contained within 395 genomic regions. Here, we report more detailed analyses of these data exploring the regions displaying within- and between-line genomic signatures of the bidirectional selection applied in these lines. Despite the strict selection regime for opposite extremes in body weight, this did not result in opposite genomic signatures between the lines. The lines often displayed a duality of the sweep signatures, where an extended region of homozygosity in one line, in contrast to mosaic pattern of heterozygosity in the other line. These haplotype mosaics consisted of short, distinct haploblocks of variable between-line divergence, likely the results of a complex demographic history involving bottlenecks, introgressions and moderate inbreeding. We demonstrate this using the example of complex haplotype mosaicism in the growth1 QTL. These mosaics represent the standing genetic variation available at the onset of selection in the founder population. Selection on standing genetic variation can thus result in different signatures depending on the intensity and direction of selection.
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25
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Ono T, Kouguchi T, Ishikawa A, Nagano AJ, Takenouchi A, Igawa T, Tsudzuki M. Quantitative trait loci mapping for the shear force value in breast muscle of F2 chickens. Poult Sci 2019; 98:1096-1101. [PMID: 30329107 DOI: 10.3382/ps/pey493] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 10/10/2018] [Indexed: 12/18/2022] Open
Abstract
The shear force value is one of the major traits that determine meat quality. In the present study, we performed QTL analysis for chicken breast muscle shear force value at 7 wk of age using 545 single nucleotide polymorphism (SNP) markers developed via restriction-site associated DNA sequencing (RAD-seq). An F2 resource family was generated by mating Oh-Shamo, a native Japanese chicken breed, and the White Plymouth Rock chicken breed. A total of 215 F2 birds were produced. Simple interval mapping revealed one significant main-effect QTL between 6.28 and 8.10 Mb SNPs on the chromosome Z with a logarithm of odds score of 5.53 at the genome-wide 5% level. At this QTL, the confidence interval, phenotypic variance explained, and additive effect were 26 cM, 12.24%, and -0.31 in males and -0.34 in females, respectively. No QTL with epistatic interaction effects were detected. To our knowledge, this is the first report on a QTL affecting the shear force value in the chicken breast muscle, using SNP markers derived from RAD-seq.
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Affiliation(s)
- Takashi Ono
- Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8528, Japan
| | | | - Akira Ishikawa
- Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Aichi 464-8601, Japan.,Japanese Avian Bioresource Project Research Center, Higashi-Hiroshima, Hiroshima 739-8528, Japan
| | - Atsushi J Nagano
- Faculty of Agriculture, Ryukoku University, Otsu, Shiga 520-2194, Japan
| | - Atsushi Takenouchi
- Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8528, Japan
| | - Takeshi Igawa
- Japanese Avian Bioresource Project Research Center, Higashi-Hiroshima, Hiroshima 739-8528, Japan.,Graduate School of Science, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan
| | - Masaoki Tsudzuki
- Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8528, Japan.,Japanese Avian Bioresource Project Research Center, Higashi-Hiroshima, Hiroshima 739-8528, Japan
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Zhang C, Lin D, Wang Y, Peng D, Li H, Fei J, Chen K, Yang N, Hu X, Zhao Y, Li N. Widespread introgression in Chinese indigenous chicken breeds from commercial broiler. Evol Appl 2019; 12:610-621. [PMID: 30828377 PMCID: PMC6383742 DOI: 10.1111/eva.12742] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 10/17/2018] [Accepted: 11/15/2018] [Indexed: 12/17/2022] Open
Abstract
Chinese indigenous chickens (CICs) constitute world-renowned genetic resources due to their excellent traits, including early puberty, good meat quality and strong resistance to disease. Unfortunately, the introduction of a large number of commercial chickens in the past two decades has had an adverse effect on CICs. Using the chicken 60 K single nucleotide polymorphism chip, we assessed the genetic diversity and population structure of 1,187 chickens, representing eight Chinese indigenous chicken breeds, two hybrid chicken breeds, two ancestral chicken breeds, two commercial populations and additional red jungle fowl. By investigating haplotype similarity, we found extensive gene introgression from commercial broiler to almost all CICs. Approximately 15% of the genome, on average, of CICs was introgressed, ranging from 0.64% for Tibetan chicken to 21.52% for Huiyang Bearded chicken. Further analysis revealed signals consistent with positive selection in the introgression loci. For the first time, we systematically mapped and quantified introgression from commercial broiler to CICs at the whole genome level. Our data provided a usable resource for chicken genetic diversity, and our findings indicated a dire need for protecting the genetic resources of CICs.
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Affiliation(s)
- Chunyuan Zhang
- Beijing Advanced Innovation Center for Food Nutrition and Human HealthChina Agricultural UniversityBeijingChina
- State Key Laboratory for Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
| | - Deng Lin
- Beijing Advanced Innovation Center for Food Nutrition and Human HealthChina Agricultural UniversityBeijingChina
| | - Yuzhe Wang
- State Key Laboratory for Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
| | - Dezhi Peng
- State Key Laboratory for Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
| | - Huifang Li
- Institute of Poultry ScienceChinese Academy of Agricultural SciencesYangzhouChina
| | - Jing Fei
- State Key Laboratory for Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
| | - Kuanwei Chen
- Institute of Poultry ScienceChinese Academy of Agricultural SciencesYangzhouChina
| | - Ning Yang
- National Engineering Laboratory for Animal BreedingChina Agricultural UniversityBeijingChina
| | - Xiaoxiang Hu
- State Key Laboratory for Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
| | - Yiqiang Zhao
- Beijing Advanced Innovation Center for Food Nutrition and Human HealthChina Agricultural UniversityBeijingChina
- State Key Laboratory for Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
| | - Ning Li
- State Key Laboratory for Agrobiotechnology, College of Biological SciencesChina Agricultural UniversityBeijingChina
- National Engineering Laboratory for Animal BreedingChina Agricultural UniversityBeijingChina
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27
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Mapping of Quantitative Trait Loci for Growth and Carcass-Related Traits in Chickens Using a Restriction-Site Associated DNA Sequencing Method. J Poult Sci 2019; 56:166-176. [PMID: 32055211 PMCID: PMC7005382 DOI: 10.2141/jpsa.0180066] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
In the present study, quantitative trait loci (QTLs) analysis was performed to identify the chromosomal positions of growth and carcass-related trait QTLs using 319 F2 chickens obtained from intercrosses of an Oh-Shamo male and four White Plymouth Rock females. Body weight was measured weekly until the birds were 7 weeks old. Carcass-related traits were also measured at this timepoint. A genetic linkage map was constructed using 545 single nucleotide polymorphism (SNP) markers that were developed using a restriction-site associated DNA sequencing method. The linkage map included the 23 autosomes and the Z chromosome. Using simple interval QTL mapping, we were able to identify 10 significant and suggestive main-effect QTLs for growth and carcass-related traits present on chromosomes 1, 2, 3, 5, 8, 19, 24, and Z. These loci explained 5.60–16.52% of the phenotypic variances. The chromosomal positions of the 10 QTLs overlapped with those of previously reported QTLs, whereas the targeted traits varied. Our QTLs will aid future breeding programs in improving growth and meat yield of chickens (e.g., via marker-assisted selection), particularly in the Japanese brand chicken industry.
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Knaga S, Siwek M, Tavaniello S, Maiorano G, Witkowski A, Jezewska-Witkowska G, Bednarczyk M, Zieba G. Identification of quantitative trait loci affecting production and biochemical traits in a unique Japanese quail resource population. Poult Sci 2018; 97:2267-2277. [PMID: 29672744 DOI: 10.3382/ps/pey110] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 03/10/2018] [Indexed: 11/20/2022] Open
Abstract
The objective of the current study was to identify QTL associated with body weight, growth rate, egg quality traits, concentration of selected blood plasma, and yolk lipids as well as concentration of selected macro- and microelements, color, pH, basic chemical composition, and drip loss of breast muscle of Japanese quail (Coturnix japonica). Twenty-two meat-type males (line F33) were crossed with twenty-two laying-type females (line S22) to produce a generation of F1 hybrids. The F2 generation was created by mating 44 randomly chosen F1 hybrids, which were full siblings. The birds were individually weighed from the first to eighth week of age. At the age of 19 wk, 2 to 4 eggs were individually collected from each female and an analysis of the egg quality traits was performed. At slaughter, blood and breast muscles were collected from 324 individuals of the resource population. The basic chemical composition, concentration of chosen macro- and microelements, color, pH, and drip loss were determined in the muscle samples. The concentration of chosen lipids was determined in egg yolk and blood plasma. In total, 30 microsatellite markers located on chromosome 1 and 2 were genotyped. QTL mapping including additive and dominance genetic effects revealed 6 loci on chromosome 1 of the Japanese quail affecting the egg number, egg production rate, egg weight, specific gravity, egg shell weight, concentration of Na in breast muscle. In turn, there were 9 loci on chromosome 2 affecting the body weight in the first, fourth, and sixth week of age, growth rate in the second and seventh week of age, specific gravity, concentration of K and Cu in breast muscle, and the levels of triacylglycerols in blood plasma. In this study, QTL with a potential effect on the Na, K, and Cu content in breast muscles in poultry and on specific gravity in the Japanese quail were mapped for the first time.
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Affiliation(s)
- S Knaga
- Institute of Biological Bases of Animal Production, University of Life Sciences, Akademicka 13,20-950 Lublin, Poland
| | - M Siwek
- Department of Animal Biochemistry and Biotechnology, UTP University of Sciences and Technology, Bydgoszcz 85-064, Poland
| | - S Tavaniello
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Campobasso 86100, Italy
| | - G Maiorano
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Campobasso 86100, Italy
| | - A Witkowski
- Institute of Biological Bases of Animal Production, University of Life Sciences, Akademicka 13,20-950 Lublin, Poland
| | - G Jezewska-Witkowska
- Institute of Biological Bases of Animal Production, University of Life Sciences, Akademicka 13,20-950 Lublin, Poland
| | - M Bednarczyk
- Department of Animal Biochemistry and Biotechnology, UTP University of Sciences and Technology, Bydgoszcz 85-064, Poland
| | - G Zieba
- Institute of Biological Bases of Animal Production, University of Life Sciences, Akademicka 13,20-950 Lublin, Poland
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Wen C, Yan W, Zheng J, Ji C, Zhang D, Sun C, Yang N. Feed efficiency measures and their relationships with production and meat quality traits in slower growing broilers. Poult Sci 2018; 97:2356-2364. [PMID: 29669019 DOI: 10.3382/ps/pey062] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Indexed: 11/20/2022] Open
Abstract
Feed consumption accounts for the major cost of broiler production. Improving the efficiency of feed utilization is a primary goal in breeding strategies, although few studies have focused on slower growing broilers. Here, we recorded the feed intake (FI) during the fast-growing period (d 56 to 76) and measured the live weight, body measurements, carcass characteristics, and intramuscular fat (IMF) content of Chinese yellow broilers. Then, the residual feed intake (RFI) and feed conversion ratio (FCR) were calculated for each individual. Pair-wise phenotypic correlations were subsequently calculated between feed efficiency traits and others. Finally, we separately selected the more efficient individuals based on RFI and FCR values to evaluate the impacts on the traits of FI, growth, carcass characteristics, and meat quality. The results showed higher correlations between FCR and production traits than with RFI, while RFI showed a moderate and positive phenotypic correlation with abdominal fat. FCR was weakly correlated with FI and slightly positively correlated with IMF content. The correlation coefficient between RFI and FI was 0.62, and that between RFI and IMF content was close to zero. Without increasing FI, decreasing FCR could effectively enhance the growth rate and market weight with no adverse effect on meat quality. In contrast, by improving RFI, FI and abdominal fat mass were significantly reduced and thus increased the yield with no unfavorable effects on meat quality. In consideration of consumer preference and overall economical benefits, RFI is a more suitable index to improve feed efficiency in slower growing broilers.
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Affiliation(s)
- Chaoliang Wen
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Wei Yan
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jiangxia Zheng
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Congliang Ji
- Guangdong Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu 527400, Guangdong Province, China
| | - Dexiang Zhang
- Guangdong Wen's Nanfang Poultry Breeding Co. Ltd, Yunfu 527400, Guangdong Province, China
| | - Congjiao Sun
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Ning Yang
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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30
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Wang Y, Guo F, Qu H, Luo C, Wang J, Shu D. Associations between variants of bone morphogenetic protein 7 gene and growth traits in chickens. Br Poult Sci 2018; 59:264-269. [PMID: 29667421 DOI: 10.1080/00071668.2018.1454586] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
1. Enhancing bone strength to solve leg disorders in poultry has become an important goal in broiler production. Bone morphogenetic protein 7 (BMP7), a member of the BMP family, represents an attractive therapeutic target for bone regeneration in humans and plays critical roles in skeletal development. 2. The objective of this study was to investigate the relationship between BMP7 gene expression, single-nucleotide polymorphisms (SNPs) and growth traits in chickens. Here, a SNP (c.1995T>C) in the chicken (Gallus gallus) BMP7 gene was identified, that was associated with growth and carcass traits. 3. Genotyping revealed that the T allele occurred more frequently in breeds with high growth rates, whereas the C allele was predominant in those with low growth rates. The expression level of BMP7 in the thigh bone of birds with the TT genotype was significantly higher than in those with the CC genotype at 21, 42 and 91 d of age. 4. These findings suggest that selecting the birds with the TT genotype of SNP c.1995T>C could improve bone growth, could reduce leg disorders in fast-growing birds. The SNP c.1995T>C may serve as a selective marker for improving bone growth and increasing the consistency of body weights in poultry breeding.
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Affiliation(s)
- Y Wang
- a Institute of Animal Science , Guangdong Academy of Agricultural Sciences , Guangzhou 510640 , China.,b State Key Laboratory of Livestock and Poultry Breeding & Guangdong Key Laboratory of Animal Breeding and Nutrition , Guangzhou 510640 , China
| | - F Guo
- a Institute of Animal Science , Guangdong Academy of Agricultural Sciences , Guangzhou 510640 , China.,b State Key Laboratory of Livestock and Poultry Breeding & Guangdong Key Laboratory of Animal Breeding and Nutrition , Guangzhou 510640 , China
| | - H Qu
- a Institute of Animal Science , Guangdong Academy of Agricultural Sciences , Guangzhou 510640 , China.,b State Key Laboratory of Livestock and Poultry Breeding & Guangdong Key Laboratory of Animal Breeding and Nutrition , Guangzhou 510640 , China
| | - C Luo
- a Institute of Animal Science , Guangdong Academy of Agricultural Sciences , Guangzhou 510640 , China.,b State Key Laboratory of Livestock and Poultry Breeding & Guangdong Key Laboratory of Animal Breeding and Nutrition , Guangzhou 510640 , China
| | - J Wang
- a Institute of Animal Science , Guangdong Academy of Agricultural Sciences , Guangzhou 510640 , China.,b State Key Laboratory of Livestock and Poultry Breeding & Guangdong Key Laboratory of Animal Breeding and Nutrition , Guangzhou 510640 , China
| | - D Shu
- a Institute of Animal Science , Guangdong Academy of Agricultural Sciences , Guangzhou 510640 , China.,b State Key Laboratory of Livestock and Poultry Breeding & Guangdong Key Laboratory of Animal Breeding and Nutrition , Guangzhou 510640 , China
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31
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Lillie M, Sheng ZY, Honaker CF, Andersson L, Siegel PB, Carlborg Ö. Genomic signatures of 60 years of bidirectional selection for 8-week body weight in chickens. Poult Sci 2018; 97:781-790. [PMID: 29272516 DOI: 10.3382/ps/pex383] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 11/27/2017] [Indexed: 12/30/2022] Open
Abstract
Sixty years, constituting 60 generations, have passed since the founding of the Virginia body weight lines, an experimental population of White Plymouth Rock chickens. Using a stringent breeding scheme for divergent 8-week body weight, the lines, which originated from a common founder population, have responded to bidirectional selection with an approximate 15-fold difference in the selected trait. They provide a model system to study the genetics of complex traits in general and the influences of artificial selection on quantitative genetic architectures in particular. As we reflect on the 60th anniversary of the initiation of the Virginia body weight lines, there is opportunity to discuss the findings obtained using different analytical and experimental genetic and genomic strategies and integrate them with a recent pooled genome resequencing dataset. Hundreds of regions across the genome show differentiation between the 2 lines, reinforcing previous findings that response to selection relied on standing variation across many genes and giving insights into the haplotype complexity underlying regions associated with body weight.
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Affiliation(s)
- M Lillie
- Department of Medical Biochemistry and Microbiology, Genomics, Uppsala University, Uppsala, Sweden
| | - Z Y Sheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, People's Republic of China
| | - C F Honaker
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg
| | - L Andersson
- Department of Medical Biochemistry and Microbiology, Genomics, Uppsala University, Uppsala, Sweden.,Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden.,Department of Veterinary Integrative Biosciences, Texas A&M University, College Station
| | - P B Siegel
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg
| | - Ö Carlborg
- Department of Medical Biochemistry and Microbiology, Genomics, Uppsala University, Uppsala, Sweden
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32
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Yuan Y, Peng D, Gu X, Gong Y, Sheng Z, Hu X. Polygenic Basis and Variable Genetic Architectures Contribute to the Complex Nature of Body Weight -A Genome-Wide Study in Four Chinese Indigenous Chicken Breeds. Front Genet 2018; 9:229. [PMID: 30013594 PMCID: PMC6036123 DOI: 10.3389/fgene.2018.00229] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 06/11/2018] [Indexed: 01/08/2023] Open
Abstract
Body weight (BW) is one of the most important economic traits for animal production and breeding, and it has been studied extensively for its phenotype–genotype associations. While mapping studies have mostly aimed at finding as many loci as possible that contributed to the variation in BW, the role of other factors in its genetic architecture, including their frequencies in the population and their interactions, have been largely overlooked. To comprehensively characterized the genetic architecture of BW, we performed a genome-wide association study (GWAS) both at the single-marker and haplotype level on birds from four indigenous Chinese chicken breeds (Chahua, Silkie, Langshan, and Beard), rather than studying crosses between two founder lines. Additionally, samples from two more breeds (Red Junglefowl and Recessive White) were included to better reflect variable genetic characteristics across populations. Six loci were mapped in this study, revealing the polygenic basis underlying BW. Moreover, by further examining the frequencies of the significantly associated haplotypes in each subpopulation and their effect sizes, most of the loci were found to affect BW in the Beard chicken breed alone. Two loci in GGA9 and GGA27, however, had a common effect on BW across subpopulations, showing that different underlying genetic mechanisms contribute to the phenotypic variability. These findings, particularly the variable genetic architectures found in different loci, improve our understanding of the overall genetic contributions to the large variability in BW among Chinese indigenous chicken breeds. These findings thus will have important implications for future chicken breeding.
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Affiliation(s)
- Yangyang Yuan
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Dezhi Peng
- State Key Laboratory for Agro-Biotechnology, China Agricultural University, Beijing, China.,National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
| | - Xiaorong Gu
- State Key Laboratory for Agro-Biotechnology, China Agricultural University, Beijing, China.,National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
| | - Yanzhang Gong
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Zheya Sheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xiaoxiang Hu
- State Key Laboratory for Agro-Biotechnology, China Agricultural University, Beijing, China.,National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
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33
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Moreira GCM, Boschiero C, Cesar ASM, Reecy JM, Godoy TF, Trevisoli PA, Cantão ME, Ledur MC, Ibelli AMG, Peixoto JDO, Moura ASAMT, Garrick D, Coutinho LL. A genome-wide association study reveals novel genomic regions and positional candidate genes for fat deposition in broiler chickens. BMC Genomics 2018; 19:374. [PMID: 29783939 PMCID: PMC5963092 DOI: 10.1186/s12864-018-4779-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 05/10/2018] [Indexed: 12/21/2022] Open
Abstract
Background Excess fat content in chickens has a negative impact on poultry production. The discovery of QTL associated with fat deposition in the carcass allows the identification of positional candidate genes (PCGs) that might regulate fat deposition and be useful for selection against excess fat content in chicken’s carcass. This study aimed to estimate genomic heritability coefficients and to identify QTLs and PCGs for abdominal fat (ABF) and skin (SKIN) traits in a broiler chicken population, originated from the White Plymouth Rock and White Cornish breeds. Results ABF and SKIN are moderately heritable traits in our broiler population with estimates ranging from 0.23 to 0.33. Using a high density SNP panel (355,027 informative SNPs), we detected nine unique QTLs that were associated with these fat traits. Among these, four QTL were novel, while five have been previously reported in the literature. Thirteen PCGs were identified that might regulate fat deposition in these QTL regions: JDP2, PLCG1, HNF4A, FITM2, ADIPOR1, PTPN11, MVK, APOA1, APOA4, APOA5, ENSGALG00000000477, ENSGALG00000000483, and ENSGALG00000005043. We used sequence information from founder animals to detect 4843 SNPs in the 13 PCGs. Among those, two were classified as potentially deleterious and two as high impact SNPs. Conclusions This study generated novel results that can contribute to a better understanding of fat deposition in chickens. The use of high density array of SNPs increases genome coverage and improves QTL resolution than would have been achieved with low density. The identified PCGs were involved in many biological processes that regulate lipid storage. The SNPs identified in the PCGs, especially those predicted as potentially deleterious and high impact, may affect fat deposition. Validation should be undertaken before using these SNPs for selection against carcass fat accumulation and to improve feed efficiency in broiler chicken production. Electronic supplementary material The online version of this article (10.1186/s12864-018-4779-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gabriel Costa Monteiro Moreira
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Piracicaba, São Paulo, 13418-900, Brazil
| | - Clarissa Boschiero
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Piracicaba, São Paulo, 13418-900, Brazil
| | - Aline Silva Mello Cesar
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Piracicaba, São Paulo, 13418-900, Brazil
| | - James M Reecy
- Department of Animal Science, Iowa State University (ISU), Ames, Iowa, USA
| | - Thaís Fernanda Godoy
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Piracicaba, São Paulo, 13418-900, Brazil
| | - Priscila Anchieta Trevisoli
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Piracicaba, São Paulo, 13418-900, Brazil
| | | | | | | | | | | | - Dorian Garrick
- School of Agriculture, Massey University, Ruakura, Hamilton, New Zealand
| | - Luiz Lehmann Coutinho
- Department of Animal Science, University of São Paulo (USP) / Luiz de Queiroz College of Agriculture (ESALQ), Piracicaba, São Paulo, 13418-900, Brazil.
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Emrani H, Vaez Torshizi R, Akbar Masoudi A, Ehsani A. Identification of new loci for body weight traits in F2 chicken population using genome-wide association study. Livest Sci 2017. [DOI: 10.1016/j.livsci.2017.10.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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35
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Wang Y, Cao X, Zhao Y, Fei J, Hu X, Li N. Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens. PLoS One 2017; 12:e0179073. [PMID: 28598985 PMCID: PMC5466311 DOI: 10.1371/journal.pone.0179073] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 05/23/2017] [Indexed: 12/04/2022] Open
Abstract
High-density single nucleotide polymorphism (SNP) markers are crucial to improve the resolution and accuracy of genome-wide association study (GWAS) and genomic selection (GS). Numerous approaches, including whole genome sequencing, genome sampling sequencing, and SNP chips are able to discover or genotype markers at different densities and costs. Achieving an optimal balance between sequencing resolution and budgets, especially in large-scale population genetics research, constitutes a major challenge. Here, we performed improved double-enzyme digestion genotyping by sequencing (ddGBS) on chicken. We evaluated eight double-enzyme digestion combinations, and EcoR I- Mse I was chosen as the optimal combination for the chicken genome. We firstly proposed that two parameters, optimal read-count point (ORP) and saturated read-count point (SRP), could be utilized to determine the optimal sequencing volume. A total of 291,772 high-density SNPs from 824 animals were identified. By validation using the SNP chip, we found that the consistency between ddGBS data and the SNP chip is over 99%. The approach that we developed in chickens, which is high-quality, high-density, cost-effective (300 K, $30/sample), and time-saving (within 48 h), will have broad applications in animal breeding programs.
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Affiliation(s)
- Yuzhe Wang
- State Key Laboratories of Agro-biotechnology, College of Biological Science, China Agricultural University, Beijing, China
| | - Xuemin Cao
- State Key Laboratories of Agro-biotechnology, College of Biological Science, China Agricultural University, Beijing, China
| | - Yiqiang Zhao
- State Key Laboratories of Agro-biotechnology, College of Biological Science, China Agricultural University, Beijing, China
| | - Jing Fei
- State Key Laboratories of Agro-biotechnology, College of Biological Science, China Agricultural University, Beijing, China
| | - Xiaoxiang Hu
- State Key Laboratories of Agro-biotechnology, College of Biological Science, China Agricultural University, Beijing, China
- * E-mail:
| | - Ning Li
- State Key Laboratories of Agro-biotechnology, College of Biological Science, China Agricultural University, Beijing, China
- National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
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36
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Liu T, Luo C, Wang J, Ma J, Shu D, Lund MS, Su G, Qu H. Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens. PLoS One 2017; 12:e0173620. [PMID: 28278209 PMCID: PMC5344482 DOI: 10.1371/journal.pone.0173620] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2016] [Accepted: 02/23/2017] [Indexed: 11/19/2022] Open
Abstract
Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.
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Affiliation(s)
- Tianfei Liu
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
| | - Chenglong Luo
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
| | - Jie Wang
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
| | - Jie Ma
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Key Laboratory of Animal Breeding and Nutrition, Guangzhou, China
| | - Dingming Shu
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Denmark
| | - Hao Qu
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
- * E-mail:
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A short insertion mutation disrupts genesis of miR-16 and causes increased body weight in domesticated chicken. Sci Rep 2016; 6:36433. [PMID: 27808177 PMCID: PMC5093740 DOI: 10.1038/srep36433] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 10/17/2016] [Indexed: 11/26/2022] Open
Abstract
Body weight is one of the most important quantitative traits with high heritability in chicken. We previously mapped a quantitative trait locus (QTL) for body weight by genome-wide association study (GWAS) in an F2 chicken resource population. To identify the causal mutations linked to this QTL, expression profiles were determined on livers of high-weight and low-weight chicken lines by microarray. Combining the expression pattern with SNP effects by GWAS, miR-16 was identified as the most likely potential candidate with a 3.8-fold decrease in high-weight lines. Re-sequencing revealed that a 54-bp insertion mutation in the upstream region of miR-15a-16 displayed high allele frequencies in high-weight commercial broiler line. This mutation resulted in lower miR-16 expression by introducing three novel splicing sites instead of the missing 5′ terminal splicing of mature miR-16. Elevating miR-16 significantly inhibited DF-1 chicken embryo cell proliferation, consistent with a role in suppression of cellular growth. The 54-bp insertion was significantly associated with increased body weight, bone size and muscle mass. Also, the insertion mutation tended towards fixation in commercial broilers (Fst > 0.4). Our findings revealed a novel causative mutation for body weight regulation that aids our basic understanding of growth regulation in birds.
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38
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A Complex Structural Variation on Chromosome 27 Leads to the Ectopic Expression of HOXB8 and the Muffs and Beard Phenotype in Chickens. PLoS Genet 2016; 12:e1006071. [PMID: 27253709 PMCID: PMC4890787 DOI: 10.1371/journal.pgen.1006071] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 04/30/2016] [Indexed: 12/13/2022] Open
Abstract
Muffs and beard (Mb) is a phenotype in chickens where groups of elongated feathers gather from both sides of the face (muffs) and below the beak (beard). It is an autosomal, incomplete dominant phenotype encoded by the Muffs and beard (Mb) locus. Here we use genome-wide association (GWA) analysis, linkage analysis, Identity-by-Descent (IBD) mapping, array-CGH, genome re-sequencing and expression analysis to show that the Mb allele causing the Mb phenotype is a derived allele where a complex structural variation (SV) on GGA27 leads to an altered expression of the gene HOXB8. This Mb allele was shown to be completely associated with the Mb phenotype in nine other independent Mb chicken breeds. The Mb allele differs from the wild-type mb allele by three duplications, one in tandem and two that are translocated to that of the tandem repeat around 1.70 Mb on GGA27. The duplications contain total seven annotated genes and their expression was tested during distinct stages of Mb morphogenesis. A continuous high ectopic expression of HOXB8 was found in the facial skin of Mb chickens, strongly suggesting that HOXB8 directs this regional feather-development. In conclusion, our results provide an interesting example of how genomic structural rearrangements alter the regulation of genes leading to novel phenotypes. Further, it again illustrates the value of utilizing derived phenotypes in domestic animals to dissect the genetic basis of developmental traits, herein providing novel insights into the likely role of HOXB8 in feather development and differentiation. Genetic variation is a key part for the study of evolution, development and differentiation. In domestic animals, many breeds display striking phenotypes that differentiate them from their wild ancestors. Several of these have been related to structural variations, including Fibromelanosis and Rose-comb in chickens, Double-muscled and Osteopetrosis in cattle, Cone degeneration in dogs, and White coat color in pigs. The feather is a type of skin appendages that exists in multiple variants on different body parts, and the derived feathering phenotypes in domestic birds are perfect resources to decipher the mechanisms regulating feather development and differentiation. Here we study the genetics of the Muffs and beard trait, a variant that alters the feather development in the facial area of chickens. We show that this phenotype is associated with a genomic structural variant that leads to an ectopic expression of HOXB8 in the facial skin during feather development. This is thus another example of how structural variants in the genome lead to novel, derived phenotypic changes in domestic animals and suggests an important role for HOXB8 in feather development.
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Cahyadi M, Park HB, Seo DW, Jin S, Choi N, Heo KN, Kang BS, Jo C, Lee JH. Variance Component Quantitative Trait Locus Analysis for Body Weight Traits in Purebred Korean Native Chicken. ASIAN-AUSTRALASIAN JOURNAL OF ANIMAL SCIENCES 2016; 29:43-50. [PMID: 26732327 PMCID: PMC4698688 DOI: 10.5713/ajas.15.0193] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Revised: 04/24/2015] [Accepted: 06/02/2015] [Indexed: 11/27/2022]
Abstract
Quantitative trait locus (QTL) is a particular region of the genome containing one or more genes associated with economically important quantitative traits. This study was conducted to identify QTL regions for body weight and growth traits in purebred Korean native chicken (KNC). F1 samples (n = 595) were genotyped using 127 microsatellite markers and 8 single nucleotide polymorphisms that covered 2,616.1 centi Morgan (cM) of map length for 26 autosomal linkage groups. Body weight traits were measured every 2 weeks from hatch to 20 weeks of age. Weight of half carcass was also collected together with growth rate. A multipoint variance component linkage approach was used to identify QTLs for the body weight traits. Two significant QTLs for growth were identified on chicken chromosome 3 (GGA3) for growth 16 to18 weeks (logarithm of the odds [LOD] = 3.24, Nominal p value = 0.0001) and GGA4 for growth 6 to 8 weeks (LOD = 2.88, Nominal p value = 0.0003). Additionally, one significant QTL and three suggestive QTLs were detected for body weight traits in KNC; significant QTL for body weight at 4 weeks (LOD = 2.52, nominal p value = 0.0007) and suggestive QTL for 8 weeks (LOD = 1.96, Nominal p value = 0.0027) were detected on GGA4; QTLs were also detected for two different body weight traits: body weight at 16 weeks on GGA3 and body weight at 18 weeks on GGA19. Additionally, two suggestive QTLs for carcass weight were detected at 0 and 70 cM on GGA19. In conclusion, the current study identified several significant and suggestive QTLs that affect growth related traits in a unique resource pedigree in purebred KNC. This information will contribute to improving the body weight traits in native chicken breeds, especially for the Asian native chicken breeds.
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Affiliation(s)
- Muhammad Cahyadi
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 305-764 Korea ; Department of Animal Science, Faculty of Agriculture, Sebelas Maret University, Surakarta 57126, Indonesia
| | - Hee-Bok Park
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 305-764 Korea
| | - Dong-Won Seo
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 305-764 Korea
| | - Shil Jin
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 305-764 Korea
| | - Nuri Choi
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 305-764 Korea
| | - Kang-Nyeong Heo
- Poultry Science Division, National Institute of Animal Science, RDA, Cheonan 331-801, Korea
| | - Bo-Seok Kang
- Poultry Science Division, National Institute of Animal Science, RDA, Cheonan 331-801, Korea
| | - Cheorun Jo
- Department of Agricultural Biotechnology and Research Institute of Agriculture and Life Science, Seoul National University, Seoul 151-921, Korea
| | - Jun-Heon Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 305-764 Korea
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The genetics of feed conversion efficiency traits in a commercial broiler line. Sci Rep 2015; 5:16387. [PMID: 26552583 PMCID: PMC4639841 DOI: 10.1038/srep16387] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 10/14/2015] [Indexed: 11/26/2022] Open
Abstract
Individual feed conversion efficiency (FCE) is a major trait that influences the usage of energy resources and the ecological footprint of livestock production. The underlying biological processes of FCE are complex and are influenced by factors as diverse as climate, feed properties, gut microbiota, and individual genetic predisposition. To gain an insight to the genetic relationships with FCE traits and to contribute to the improvement of FCE in commercial chicken lines, a genome-wide association study was conducted using a commercial broiler population (n = 859) tested for FCE and weight traits during the finisher period from 39 to 46 days of age. Both single-marker (generalized linear model) and multi-marker (Bayesian approach) analyses were applied to the dataset to detect genes associated with the variability in FCE. The separate analyses revealed 22 quantitative trait loci (QTL) regions on 13 different chromosomes; the integration of both approaches resulted in 7 overlapping QTL regions. The analyses pointed to acylglycerol kinase (AGK) and general transcription factor 2-I (GTF2I) as positional and functional candidate genes. Non-synonymous polymorphisms of both candidate genes revealed evidence for a functional importance of these genes by influencing different biological aspects of FCE.
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Ouyang H, He X, Li G, Xu H, Jia X, Nie Q, Zhang X. Deep Sequencing Analysis of miRNA Expression in Breast Muscle of Fast-Growing and Slow-Growing Broilers. Int J Mol Sci 2015; 16:16242-62. [PMID: 26193261 PMCID: PMC4519947 DOI: 10.3390/ijms160716242] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Revised: 07/03/2015] [Accepted: 07/10/2015] [Indexed: 01/17/2023] Open
Abstract
Growth performance is an important economic trait in chicken. MicroRNAs (miRNAs) have been shown to play important roles in various biological processes, but their functions in chicken growth are not yet clear. To investigate the function of miRNAs in chicken growth, breast muscle tissues of the two-tail samples (highest and lowest body weight) from Recessive White Rock (WRR) and Xinghua Chickens (XH) were performed on high throughput small RNA deep sequencing. In this study, a total of 921 miRNAs were identified, including 733 known mature miRNAs and 188 novel miRNAs. There were 200, 279, 257 and 297 differentially expressed miRNAs in the comparisons of WRRh vs. WRRl, WRRh vs. XHh, WRRl vs. XHl, and XHh vs. XHl group, respectively. A total of 22 highly differentially expressed miRNAs (fold change > 2 or < 0.5; p-value < 0.05; q-value < 0.01), which also have abundant expression (read counts > 1000) were found in our comparisons. As far as two analyses (WRRh vs. WRRl, and XHh vs. XHl) are concerned, we found 80 common differentially expressed miRNAs, while 110 miRNAs were found in WRRh vs. XHh and WRRl vs. XHl. Furthermore, 26 common miRNAs were identified among all four comparisons. Four differentially expressed miRNAs (miR-223, miR-16, miR-205a and miR-222b-5p) were validated by quantitative real-time RT-PCR (qRT-PCR). Regulatory networks of interactions among miRNAs and their targets were constructed using integrative miRNA target-prediction and network-analysis. Growth hormone receptor (GHR) was confirmed as a target of miR-146b-3p by dual-luciferase assay and qPCR, indicating that miR-34c, miR-223, miR-146b-3p, miR-21 and miR-205a are key growth-related target genes in the network. These miRNAs are proposed as candidate miRNAs for future studies concerning miRNA-target function on regulation of chicken growth.
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Affiliation(s)
- Hongjia Ouyang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China.
| | - Xiaomei He
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China.
| | - Guihuan Li
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China.
| | - Haiping Xu
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China.
| | - Xinzheng Jia
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China.
| | - Qinghua Nie
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China.
| | - Xiquan Zhang
- Department of Animal Genetics, Breeding and Reproduction, College of Animal Science, South China Agricultural University, Guangzhou 510642, China.
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China.
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Identification of loci and genes for growth related traits from a genome-wide association study in a slow- × fast-growing broiler chicken cross. Genes Genomics 2015. [DOI: 10.1007/s13258-015-0314-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Luo C, Sun L, Ma J, Wang J, Qu H, Shu D. Association of single nucleotide polymorphisms in the microRNA miR-1596 locus with residual feed intake in chickens. Anim Genet 2015; 46:265-71. [PMID: 25818998 DOI: 10.1111/age.12284] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/05/2015] [Indexed: 02/04/2023]
Abstract
MicroRNAs are an abundant class of small non-coding RNAs that regulate gene expression. Genetic variations in microRNA sequences may be associated with phenotype differences by influencing the expression of microRNAs and/or their targets. This study identified two single nucleotide polymorphisms (SNPs) in the genomic region of the microRNA miR-1596 locus of chicken. Of the two SNPs, one was 95 bp upstream of miR-1596 (g.5678784A>T) and the other was in the middle of the sequence producing the mature microRNA gga-miR-1596-3p (g.5678944A>G). Genotypic distribution of the two SNPs had large differences among 12 chicken breeds (lines), especially between the fast-growing commercial lines and the slow-growing Chinese indigenous breeds for the g.5678784A>T SNP. Only the g.5678784A>T SNP was significantly associated with residual feed intake (RFI) in the F2 population derived from a fast-growing and a slow-growing broiler as well as in the pure Huiyang bearded chicken. The birds with the AA genotype of the g.5678784A>T SNP had lower RFI and higher expression of the mature gga-miR-1596-3p microRNA of miR-1596 than did those with the other genotypes of the same SNP. We also found that the expression of the mature gga-miR-1596-3p microRNA of miR-1596 was significantly associated with RFI. These findings suggest that miR-1596 can become a candidate gene related to RFI, and its genetic variation may contribute to changes in RFI by altering expression levels of the mature gga-miR-1596-3p microRNA in chicken.
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Affiliation(s)
- C Luo
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, China; State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, 510640, China
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Liu T, Qu H, Luo C, Li X, Shu D, Lund MS, Su G. Genomic selection for the improvement of antibody response to Newcastle disease and avian influenza virus in chickens. PLoS One 2014; 9:e112685. [PMID: 25401767 PMCID: PMC4234505 DOI: 10.1371/journal.pone.0112685] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 10/10/2014] [Indexed: 12/18/2022] Open
Abstract
Newcastle disease (ND) and avian influenza (AI) are the most feared diseases in the poultry industry worldwide. They can cause flock mortality up to 100%, resulting in a catastrophic economic loss. This is the first study to investigate the feasibility of genomic selection for antibody response to Newcastle disease virus (Ab-NDV) and antibody response to Avian Influenza virus (Ab-AIV) in chickens. The data were collected from a crossbred population. Breeding values for Ab-NDV and Ab-AIV were estimated using a pedigree-based best linear unbiased prediction model (BLUP) and a genomic best linear unbiased prediction model (GBLUP). Single-trait and multiple-trait analyses were implemented. According to the analysis using the pedigree-based model, the heritability for Ab-NDV estimated from the single-trait and multiple-trait models was 0.478 and 0.487, respectively. The heritability for Ab-AIV estimated from the two models was 0.301 and 0.291, respectively. The estimated genetic correlation between the two traits was 0.438. A four-fold cross-validation was used to assess the accuracy of the estimated breeding values (EBV) in the two validation scenarios. In the family sample scenario each half-sib family is randomly allocated to one of four subsets and in the random sample scenario the individuals are randomly divided into four subsets. In the family sample scenario, compared with the pedigree-based model, the accuracy of the genomic prediction increased from 0.086 to 0.237 for Ab-NDV and from 0.080 to 0.347 for Ab-AIV. In the random sample scenario, the accuracy was improved from 0.389 to 0.427 for Ab-NDV and from 0.281 to 0.367 for Ab-AIV. The multiple-trait GBLUP model led to a slightly higher accuracy of genomic prediction for both traits. These results indicate that genomic selection for antibody response to ND and AI in chickens is promising.
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Affiliation(s)
- Tianfei Liu
- College of Animal Science and Technology, Sichuan Agricultural University, Yaan, China
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
| | - Hao Qu
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
| | - Chenglong Luo
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
| | - Xuewei Li
- College of Animal Science and Technology, Sichuan Agricultural University, Yaan, China
- * E-mail: (XL); (DS); (GS)
| | - Dingming Shu
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- State Key Laboratory of Livestock and Poultry Breeding, Guangzhou, China
- * E-mail: (XL); (DS); (GS)
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- * E-mail: (XL); (DS); (GS)
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Liu T, Qu H, Luo C, Shu D, Wang J, Lund MS, Su G. Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC Genet 2014; 15:110. [PMID: 25316160 PMCID: PMC4201679 DOI: 10.1186/s12863-014-0110-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Accepted: 10/01/2014] [Indexed: 11/10/2022] Open
Abstract
Background Growth and carcass traits are very important traits for broiler chickens. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool for such traits because of its accurate prediction of breeding values of animals without own phenotypic information. This study investigated the efficiency of genomic prediction in Chinese triple-yellow chickens. As a new line, Chinese triple-yellow chicken was developed by cross-breeding and had a small effective population. Two growth traits and three carcass traits were analyzed: body weight at 6 weeks, body weight at 12 weeks, eviscerating percentage, breast muscle percentage and leg muscle percentage. Results Genomic prediction was assessed using a 4-fold cross-validation procedure for two validation scenarios. In the first scenario, each test data set comprised two half-sib families (family sample) and the rest represented the reference data. In the second scenario, the whole data were randomly divided into four subsets (random sample). In each fold of validation, one subset was used as the test data and the others as the reference data in each single validation. Genomic breeding values were predicted using a genomic best linear unbiased prediction model, a Bayesian least absolute shrinkage and selection operator model, and a Bayesian mixture model with four distributions. The accuracy of genomic estimated breeding value (GEBV) was measured as the correlation between GEBV and the corrected phenotypic value. Using the three models, the correlations ranged from 0.448 to 0.468 for the two growth traits and from 0.176 to 0.255 for the three carcass traits in the family sample scenario, and were between 0.487 and 0.536 for growth traits and between 0.312 and 0.430 for carcass traits in the random sample scenario. The differences in the prediction accuracies between the three models were very small; the Bayesian mixture model was slightly more accurate. According to the results from the random sample scenario, the accuracy of GEBV was 0.197 higher than the conventional pedigree index, averaged over the five traits. Conclusions The results indicated that genomic selection could greatly improve the accuracy of selection in chickens, compared with conventional selection. Genomic selection for growth and carcass traits in broiler chickens is promising. Electronic supplementary material The online version of this article (doi:10.1186/s12863-014-0110-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | | | | | - Dingming Shu
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.
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Guan RF, Lyu F, Chen XQ, Ma JQ, Jiang H, Xiao CG. Meat quality traits of four Chinese indigenous chicken breeds and one commercial broiler stock. J Zhejiang Univ Sci B 2014; 14:896-902. [PMID: 24101206 DOI: 10.1631/jzus.b1300163] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Meat quality traits of four genotypes of Chinese indigenous chicken [Ninghai chicken (NC), frizzle chicken (FC), Ninghai xiang chicken (XC), and Zhenning loquat chicken (LC)] and one genotype of commercial broiler [Arbor Acres plus broiler (AAB)] were analyzed. The indigenous chickens were raised before the commercial chickens in order to achieve the same final processed days. Indigenous chickens of NC, FC, XC, and LC showed significantly higher inosine-5'-monophosphate (IMP) content, shorter fiber diameter, and lower shear force than those of AAB (P<0.05). In the indigenous genotypes, NC and FC had significantly shorter fiber diameters and lower shear forces than XC and LC (P<0.05), and NC and XC had a higher IMP content than FC and LC (P<0.05). Moreover, the indigenous genotype of LC significantly displayed the highest protein content (P<0.05) in the five genotypes of birds, and no significant differences of protein content were found between the other genotypes of NC, FC, XC, and AAB (P>0.05). The indigenous chickens from FC displayed the highest total lipid content in the five bird genotypes (P<0.05). Significant differences of pH, color values of L* and a*, and drip loss for the five genotypes of birds were also observed. In conclusion, there were significant differences in the meat quality traits of the bird breeds selected in this study, and the indigenous chickens, especially the NC genotype, produced better quality meat as far as the IMP content, fiber diameters, and shear forces were concerned.
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Affiliation(s)
- Rong-fa Guan
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, China Jiliang University, Hangzhou 310018, China; College of Biological and Environmental Engineering, Zhejiang University of Technology, Hangzhou 310014, China; Hubei University of Technology, Wuhan 430068, China; Food Science Institute, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
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Wang Y, Wang J, Li BH, Qu H, Luo CL, Shu DM. An association between genetic variation in the roundabout, axon guidance receptor, homolog 2 gene and immunity traits in chickens. Poult Sci 2014; 93:31-8. [PMID: 24570420 DOI: 10.3382/ps.2013-03512] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The roundabout, axon guidance receptor, homolog 2 (ROBO2) gene is one member of the roundabout (ROBO) family, which belongs to the immunoglobulin superfamily. The ROBO molecules are known to function in axon guidance and cell migration and are involved in SLIT/ROBO signaling. In this study, we obtained the full-length cDNA sequence of the chicken ROBO2 gene. Sequence analysis indicated that 3 SNP (1418G > A, 1421C > A and 2462T > C) exist in exons 5 and 12 of the ROBO2 gene. Genotyping results revealed that the allele frequency of SNP 1421C > A was similar in all tested breeds, but the allele frequencies of the other 2 SNP were different between White Leghorn and Chinese indigenous chickens. Allele G of 1418G > A and allele T of 2462T > C predominated in the Chinese indigenous breed, whereas alleles A and C predominated in the White Leghorn breed. Association analyses revealed that birds with the GG genotype of SNP 1418G > A or the TT genotype of SNP 2462T > C had significantly higher antibody responses to Newcastle disease virus (NDV_S/P; P < 0.01) than carriers of the A allele (GA and AA) or the C allele (TC), respectively. Real-time PCR further revealed that ROBO2 expression in the spleens of the birds with higher antibody responses (GG and TT genotypes at SNP 1418 and 2462, respectively) was significantly higher than in the spleens of birds with the AA and AG genotypes at SNP 1418 or the TC genotype at SNP 2462 (P < 0.01). The results demonstrated that genetic variation at the ROBO2 gene plays a key role in the immune response to Newcastle disease virus, and SNP 1418G > A and 2462T > C can be used as genetic markers for the selection of chickens with stronger immune responses to Newcastle disease virus.
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Affiliation(s)
- Y Wang
- Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; and State Key Laboratory of Livestock and Poultry Breeding, Guangzhou 510640, China
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