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Zhang Y, Li X, Guo Q, Wang Z, Jiang Y, Yuan X, Chen G, Chang G, Bai H. Genome-wide association study reveals 2 copy number variations associated with the variation of plumage color in the white duck hybrid population. Poult Sci 2024; 103:104107. [PMID: 39094499 PMCID: PMC11342262 DOI: 10.1016/j.psj.2024.104107] [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: 05/17/2024] [Revised: 07/05/2024] [Accepted: 07/13/2024] [Indexed: 08/04/2024] Open
Abstract
Plumage color is an intuitive external poultry characteristic with rich manifestations and complex genetic mechanisms. In our previous study, we observed that there were more dark variations in plumage color in the F2 population derived from the hybridization of 2 white duck varieties. Therefore, based on the statistics of plumage color of 308 F2 populations, we further used the resequencing data of these individuals to detect copy number variations (CNVs) in the whole genome and conducted genome-wide association studies (GWAS) to determine the genetic basis related to plumage color traits. The CNV detection revealed 9,337 CNVs, with an average length of 15,950 bp and a total length of 142.02 MB, accounting for approximately 12.91% of the reference genome. The CNV distribution on the chromosomes was relatively uniform, and the number of CNVs on each chromosome positively correlated with the length of the chromosome. In the pure black plumage group, 2,101 CNVs were only identified, and 1,714 were specifically identified in the pure white plumage group. Ten CNVs were randomly selected for validation using quantitative real-time PCR, and 9 CNVs had the same CNV types as predicted, with an accuracy of 90%. Based on GWAS, we identified 2 CNVs potentially associated with plumage color variations, with the associated CNV regions covering 9 genes. Enrichment analysis of these 9 candidate genes showed significant enrichment of 3 pathways (ribosome biogenesis in eukaryotes, RNA transport, and protein export) and 17 gene ontology terms. Among these, VWA5A can downregulate MITF by binding to the regulatory factors SOX10. The occurrence of CNV may indirectly contribute to duck plumage color variation by affecting the regulatory factors of the switch gene MITF in the melanogenesis pathway. These findings have improved the understanding of the genetic basis of duck plumage color variation and have been beneficial for developing and using plumage color traits in subsequent poultry breeding.
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Affiliation(s)
- Yi Zhang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China; Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Xiaofan Li
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China; Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Qixin Guo
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Zhixiu Wang
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Yong Jiang
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Xiaoya Yuan
- Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Guohong Chen
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China; Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Guobin Chang
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China; Key Laboratory of Animal Genetics and Breeding and Molecular Design of Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
| | - Hao Bai
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, the Ministry of Education of China, Institutes of Agricultural Science and Technology Development, Yangzhou University, Yangzhou 225009, China.
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Huang R, Zhu C, Zhen Y. Genetic diversity, demographic history, and selective signatures of Silkie chicken. BMC Genomics 2024; 25:754. [PMID: 39095706 PMCID: PMC11295612 DOI: 10.1186/s12864-024-10671-x] [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: 02/09/2024] [Accepted: 07/26/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Silkie is a traditional Chinese chicken breed characterized by its unique combination of specialized morphological traits. While previous studies have focused on the genetic basis of these traits, the overall genomic characteristics of the Silkie breed remain largely unexplored. In this study, we employed whole genome resequencing data to examine the genetic diversity, selective signals and demographic history of the Silkie breed through comparative analyses with seven other Chinese indigenous breeds (IDGBs), a commercial breed, and the wild ancestor Red Jungle Fowl. RESULTS In total, 20.8 million high-quality single nucleotide polymorphisms and 86 large structural variations were obtained. We discovered that Silkie exhibits a relatively high level of inbreeding and is genetically distinct from other IDGBs. Furthermore, our analysis indicated that Silkie has experienced a stronger historical population bottleneck and has a smaller effective population size compared with other IDGBs. We identified 45 putatively selected genes that are enriched in the melanogenesis pathway, which probably is related to the feather color. Among these genes, LMBR1 and PDSS2 have been previously associated with the extra toe and the hookless feathers, respectively. Six of the selected genes (KITLG, GSK3B, SOBP, CTBP1, ELMO2, SNRPN) are known to be associated with neurodevelopment and mental diseases in human, and are possibly related to the distinct behavior of Silkie. We further identified structural variants in Silkie and found previously reported variants linked to hyperpigmentation (END3), muff and beard (HOXB8), and Rose-comb phenotype (MNR2). Additionally, we found a 0.61 Mb inversion overlapping with the GMDS gene, which was previously linked to neurodevelopmental defects in zebrafish and humans. This may also be related to the behavior distinctiveness of Silkie. CONCLUSIONS Our study revealed that Silkie is genetically distinct and relatively highly inbred compared to other IDGB chicken populations, possibly attributed to more prolong population bottlenecks and selective breeding practice. These results enhance our understanding of how domestication and selective breeding have shaped the genome of Silkie. These findings contribute to the broader field of domestication and avian genomics, and have implications for the future conservation and breeding efforts.
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Affiliation(s)
- Ruoshi Huang
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences and Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Chengqi Zhu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences and Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China
| | - Ying Zhen
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences and Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
- Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang, China.
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Recuerda M, Campagna L. How structural variants shape avian phenotypes: Lessons from model systems. Mol Ecol 2024; 33:e17364. [PMID: 38651830 DOI: 10.1111/mec.17364] [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: 11/06/2023] [Revised: 04/04/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024]
Abstract
Despite receiving significant recent attention, the relevance of structural variation (SV) in driving phenotypic diversity remains understudied, although recent advances in long-read sequencing, bioinformatics and pangenomic approaches have enhanced SV detection. We review the role of SVs in shaping phenotypes in avian model systems, and identify some general patterns in SV type, length and their associated traits. We found that most of the avian SVs so far identified are short indels in chickens, which are frequently associated with changes in body weight and plumage colouration. Overall, we found that relatively short SVs are more frequently detected, likely due to a combination of their prevalence compared to large SVs, and a detection bias, stemming primarily from the widespread use of short-read sequencing and associated analytical methods. SVs most commonly involve non-coding regions, especially introns, and when patterns of inheritance were reported, SVs associated primarily with dominant discrete traits. We summarise several examples of phenotypic convergence across different species, mediated by different SVs in the same or different genes and different types of changes in the same gene that can lead to various phenotypes. Complex rearrangements and supergenes, which can simultaneously affect and link several genes, tend to have pleiotropic phenotypic effects. Additionally, SVs commonly co-occur with single-nucleotide polymorphisms, highlighting the need to consider all types of genetic changes to understand the basis of phenotypic traits. We end by summarising expectations for when long-read technologies become commonly implemented in non-model birds, likely leading to an increase in SV discovery and characterisation. The growing interest in this subject suggests an increase in our understanding of the phenotypic effects of SVs in upcoming years.
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Affiliation(s)
- María Recuerda
- Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Ithaca, New York, USA
| | - Leonardo Campagna
- Fuller Evolutionary Biology Program, Cornell Lab of Ornithology, Ithaca, New York, USA
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York, USA
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Bendesky A, Brew J, Francis KX, Tello Corbetto EF, González Ariza A, Nogales Baena S, Shimmura T. The main genetic locus associated with the evolution of gamecocks is centered on ISPD. G3 (BETHESDA, MD.) 2024; 14:jkad267. [PMID: 37991999 PMCID: PMC10849328 DOI: 10.1093/g3journal/jkad267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/02/2023] [Accepted: 11/09/2023] [Indexed: 11/24/2023]
Abstract
Chickens were domesticated >4,000 years ago, probably first for fighting them and only later as a source of food. Fighting chickens, commonly known as gamecocks, continue to be bred throughout the world, but the genetic relationships among geographically diverse gamecocks and with nongame chickens are not known. Here, we sequenced the genomes of 44 geographically diverse gamecocks and 62 nongame chickens representing a variety of breeds. We combined these sequences with published genomes to generate the most diverse chicken genomes dataset yet assembled, with 307 samples. We found that gamecocks do not form a homogeneous group, yet they share genetic similarities that distinguish them from nongame chickens. Such similarities are likely the result of a common origin before their local diversification into, or mixing with nongame chickens. Particularly noteworthy is a variant in an intron of the isoprenoid synthase domain containing gene (ISPD), an extreme outlier present at a frequency of 89% in gamecocks but only 4% in nongame chickens. The ISPD locus has the strongest signal of selection in gamecocks, suggesting it is important for fighting performance. Because ISPD variants that are highly prevalent in gamecocks are still segregating in nongame chickens, selective breeding may help reduce its frequency in farm conditions in which aggression is not a desired trait. Altogether, our work provides genomic resources for agricultural genetics, uncovers a common origin for gamecocks from around the world and what distinguishes them genetically from chickens bred for purposes other than fighting, and points to ISPD as the most important locus related to fighting performance.
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Affiliation(s)
- Andres Bendesky
- Department of Ecology, Evolution and Environmental Biology and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, 10027-2325, USA
| | - Joseph Brew
- Department of Ecology, Evolution and Environmental Biology and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, 10027-2325, USA
| | - Kerel X Francis
- Department of Ecology, Evolution and Environmental Biology and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, 10027-2325, USA
| | | | - Antonio González Ariza
- PAIDI AGR-218 Research Group, Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14014 Córdoba, Spain
- Diputación Provincial de Córdoba, Agropecuary Provincial Centre, 14014 Córdoba, Spain
| | - Sergio Nogales Baena
- PAIDI AGR-218 Research Group, Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14014 Córdoba, Spain
| | - Tsuyoshi Shimmura
- Department of Biological Production, Tokyo University of Agriculture and Technology, Fuchu, Tokyo 183-8509, Japan
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5
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Bendesky A, Brew J, Francis KX, Tello Corbetto EF, González Ariza A, Nogales Baena S, Shimmura T. Noncoding genetic variation in ISPD distinguishes gamecocks from nongame chickens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.16.553562. [PMID: 37662209 PMCID: PMC10473654 DOI: 10.1101/2023.08.16.553562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Chickens were domesticated >4,000 years ago, probably first for fighting them and only later as a source of food. Fighting chickens, commonly known as gamecocks, continue to be bred throughout the world, but the genetic relationships among geographically diverse gamecocks and with nongame chickens are not known. Here, we sequenced the genomes of 44 geographically diverse gamecocks and of 62 nongame chickens representing a variety of breeds. We combined these sequences with published genomes to generate the most diverse chicken genomes dataset yet assembled, at 307 samples. We found that gamecocks do not form a homogeneous group, yet they share genetic similarities that distinguish them from nongame chickens. Such similarities are likely the result of a common origin before their local diversification into, or mixing with, nongame chickens. Particularly noteworthy is a variant in an intron of ISPD, an extreme outlier present at a frequency of 90% in gamecocks but only 4% in nongame chickens. The ISPD locus has the strongest signal of selection in gamecocks, suggesting it is important for fighting performance. Because ISPD variants that are highly prevalent in gamecocks are still segregating in nongame chickens, selective breeding may help reduce its frequency in farm conditions in which aggression is not a desired trait. Altogether, our work provides genomic resources for agricultural genetics, uncovers a common origin for gamecocks from around the world and what distinguishes them genetically from chickens bred for purposes other than fighting, and points to ISPD as the most important locus related to fighting performance.
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Affiliation(s)
- Andres Bendesky
- Department of Ecology, Evolution and Environmental Biology and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Joseph Brew
- Department of Ecology, Evolution and Environmental Biology and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Kerel X. Francis
- Department of Ecology, Evolution and Environmental Biology and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | | | - Antonio González Ariza
- PAIDI AGR-218 Research Group, Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, Córdoba, Spain
- Agropecuary Provincial Centre, Diputación Provincial de Córdoba, Córdoba, Spain
| | - Sergio Nogales Baena
- PAIDI AGR-218 Research Group, Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, Córdoba, Spain
| | - Tsuyoshi Shimmura
- Department of Agriculture, Tokyo University of Agriculture and Technology, Japan
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Shinde SS, Sharma A, Vijay N. Decoding the fibromelanosis locus complex chromosomal rearrangement of black-bone chicken: genetic differentiation, selective sweeps and protein-coding changes in Kadaknath chicken. Front Genet 2023; 14:1180658. [PMID: 37424723 PMCID: PMC10325862 DOI: 10.3389/fgene.2023.1180658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023] Open
Abstract
Black-bone chicken (BBC) meat is popular for its distinctive taste and texture. A complex chromosomal rearrangement at the fibromelanosis (Fm) locus on the 20th chromosome results in increased endothelin-3 (EDN3) gene expression and is responsible for melanin hyperpigmentation in BBC. We use public long-read sequencing data of the Silkie breed to resolve high-confidence haplotypes at the Fm locus spanning both Dup1 and Dup2 regions and establish that the Fm_2 scenario is correct of the three possible scenarios of the complex chromosomal rearrangement. The relationship between Chinese and Korean BBC breeds with Kadaknath native to India is underexplored. Our data from whole-genome re-sequencing establish that all BBC breeds, including Kadaknath, share the complex chromosomal rearrangement junctions at the fibromelanosis (Fm) locus. We also identify two Fm locus proximal regions (∼70 Kb and ∼300 Kb) with signatures of selection unique to Kadaknath. These regions harbor several genes with protein-coding changes, with the bactericidal/permeability-increasing-protein-like gene having two Kadaknath-specific changes within protein domains. Our results indicate that protein-coding changes in the bactericidal/permeability-increasing-protein-like gene hitchhiked with the Fm locus in Kadaknath due to close physical linkage. Identifying this Fm locus proximal selective sweep sheds light on the genetic distinctiveness of Kadaknath compared to other BBC.
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Affiliation(s)
| | | | - Nagarjun Vijay
- Computational Evolutionary Genomics Lab, Department of Biological Sciences, IISER Bhopal, Bhauri, Madhya Pradesh, India
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7
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Arslan M. Whole-genome sequencing and genomic analysis of Norduz goat (Capra hircus). Mamm Genome 2023:10.1007/s00335-023-09990-3. [PMID: 37004528 DOI: 10.1007/s00335-023-09990-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/21/2023] [Indexed: 04/04/2023]
Abstract
Artificial and natural selective breeding of goats has resulted in many different goat breeds all around the world. Norduz goat is one of these breeds, and it is a local goat breed of Turkey. The goats are favorable due to pre-weaning viability and reproduction values compared to the regional breeds. Development in sequencing technologies has let to understand huge genomic structures and complex phenotypes. Until now, such a comprehensive study has not been carried out to understand the genomic structure of the Norduz goats, yet. In the study, the next-generation sequencing was carried out to understand the genomic structure of Norduz goat. Real-time PCR was used to evaluate prominent CNVs in the Norduz goat individuals. Whole genome of the goat was constructed with an average of 33.1X coverage level. In the stringent filtering condition, 9,757,980 SNPs, 1,536,715 InDels, and 290 CNVs were detected in the Norduz goat genome. Functional analysis of high-impact SNP variations showed that the classical complement activation biological process was affected significantly in the goat. CNVs in the goat genome were found in genes related to defense against viruses, immune response, and cell membrane transporters. It was shown that GBP2, GBP5, and mammalian ortholog GBP1, which are INF-stimulated GTPases, were found to be high copy numbers in the goats. To conclude, genetic variations mainly in immunological response processes suggest that Norduz goat is an immunologically improved goat breed and natural selection could take an important role in the genetical improvements of the goats.
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Affiliation(s)
- Mevlüt Arslan
- Department of Genetics, Faculty of Veterinary Medicine, Van Yüzüncü Yıl University, Tuşba, 65080, Van, Turkey.
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8
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The study of selection signature and its applications on identification of candidate genes using whole genome sequencing data in chicken - a review. Poult Sci 2023; 102:102657. [PMID: 37054499 PMCID: PMC10123265 DOI: 10.1016/j.psj.2023.102657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 03/17/2023] Open
Abstract
Chicken is a major source of protein for the increasing human population and is useful for research purposes. There are almost 1,600 distinct regional breeds of chicken across the globe, among which a large body of genetic and phenotypic variations has been accumulated due to extensive natural and artificial selection. Moreover, natural selection is a crucial force for animal domestication. Several approaches have been adopted to detect selection signatures in different breeds of chicken using whole genome sequencing (WGS) data including integrated haplotype score (iHS), cross-populated extend haplotype homozygosity test (XP-EHH), fixation index (FST), cross-population composite likelihood ratio (XP-CLR), nucleotide diversity (Pi), and others. In addition, gene enrichment analyses are utilized to determine KEGG pathways and gene ontology (GO) terms related to traits of interest in chicken. Herein, we review different studies that have adopted diverse approaches to detect selection signatures in different breeds of chicken. This review systematically summarizes different findings on selection signatures and related candidate genes in chickens. Future studies could combine different selection signatures approaches to strengthen the quality of the results thereby providing more affirmative inference. This would further aid in deciphering the importance of selection in chicken conservation for the increasing human population.
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Shi S, Shao D, Yang L, Liang Q, Han W, Xue Q, Qu L, Leng L, Li Y, Zhao X, Dong P, Walugembe M, Kayang BB, Muhairwa AP, Zhou H, Tong H. Whole Genome Analyses Reveal Novel Genes Associated with Chicken Adaptation to Tropical and Frigid Environments. J Adv Res 2022; 47:13-25. [PMID: 35907630 PMCID: PMC10173185 DOI: 10.1016/j.jare.2022.07.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/01/2022] [Accepted: 07/17/2022] [Indexed: 01/25/2023] Open
Abstract
INTRODUCTION Investigating the genetic footprints of historical temperature selection can get insights to the local adaptation and feasible influences of climate change on long-term population dynamics. OBJECT Chicken is a significative species to study genetic adaptation on account of its similar domestication track related to human activity with the most diversified varieties. Yet, few studies have demonstrated the genetic signatures of its adaptation to naturally tropical and frigid environments. METHOD Here, we generated whole genome resequencing of 119 domesticated chickens in China including the following breeds which are in order of breeding environmental temperature from more tropical to more frigid: Wenchang chicken (WCC), green-shell chicken (GSC), Tibetan chicken (TBC), and Lindian chicken (LDC). RESULTS Our results showed WCC branched off earlier than LDC with an evident genetic admixture between WCC and LDC, suggesting their closer genetic relationship. Further comparative genomic analyses solute carrier family 33 member 1 (SLC33A1) and thyroid stimulating hormone receptor (TSHR) genes exhibited stronger signatures for positive selection in the genome of the more tropical WCC. Furthermore, genotype data from about 3,000 African local ecotypes confirmed that allele frequencies of single nucleotide polymorphisms (SNPs) in these 2 genes appeared strongly associated with tropical environment adaptation. In addition, the NADH:ubiquinone oxidoreductase subunit S4 (NDUFS4) gene exhibited a strong signature for positive selection in the LDC genome, and SNPs with marked allele frequency differences indicated a significant relationship with frigid environment adaptation. CONCLUSION Our findings partially clarify how selection footprints from environmental temperature stress can lead to advantageous genomic adaptions to tropical and frigid environments in poultry and provide a valuable resource for selective breeding of chickens.
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Affiliation(s)
- Shourong Shi
- Poultry Institute, Chinese Academy of Agriculture Science, Yangzhou, Jiangsu 225125, China
| | - Dan Shao
- Poultry Institute, Chinese Academy of Agriculture Science, Yangzhou, Jiangsu 225125, China
| | - Lingyun Yang
- Novogene Bioinformatics Institute, Beijing 10089, China
| | - Qiqi Liang
- Novogene Bioinformatics Institute, Beijing 10089, China
| | - Wei Han
- Poultry Institute, Chinese Academy of Agriculture Science, Yangzhou, Jiangsu 225125, China
| | - Qian Xue
- Poultry Institute, Chinese Academy of Agriculture Science, Yangzhou, Jiangsu 225125, China
| | - Liang Qu
- Poultry Institute, Chinese Academy of Agriculture Science, Yangzhou, Jiangsu 225125, China
| | - Li Leng
- College of Animal Science and technology, Northeast Agricultural University, Harbin, Heilongjiang, 150038, China
| | - Yishu Li
- Tropical Crop Germplasm Research Institute, Haikou, Hainan, 571101, China
| | - Xiaogang Zhao
- Agriculture and Animal Husbandry Rural and Science and Technology Bureau, Xiangcheng County, Ganzi Tibetan Autonomous Prefecture, Sichuan, 626000, China
| | - Ping Dong
- Agriculture and Animal Husbandry Rural and Science and Technology Bureau, Xiangcheng County, Ganzi Tibetan Autonomous Prefecture, Sichuan, 626000, China
| | - Muhammed Walugembe
- Department of Animal Science, Iowa State University, 2255 Kildee Hall, Ames, IA 50011, USA
| | - Boniface B Kayang
- Department of Animal Science, University of Ghana, Legon, Accra 233, Ghana
| | - Amandus P Muhairwa
- Department of Veterinary Medicine and Public Health, College of Veterinary Medicine and Biomedical Sciences, Sokoine University of Agriculture, P.O. Box 3000 Chuo Kikuu, Morogoro, Tanzania
| | - Huaijun Zhou
- Department of Animal Science, College of Agricultural and Environmental Sciences, University of California, Davis, CA 95616, USA
| | - Haibing Tong
- Poultry Institute, Chinese Academy of Agriculture Science, Yangzhou, Jiangsu 225125, China.
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10
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Xu NY, Liu ZY, Yang QM, Bian PP, Li M, Zhao X. Genomic Analyses for Selective Signatures and Genes Involved in Hot Adaptation Among Indigenous Chickens From Different Tropical Climate Regions. Front Genet 2022; 13:906447. [PMID: 35979430 PMCID: PMC9377314 DOI: 10.3389/fgene.2022.906447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Climate change, especially weather extremes like extreme cold or extreme hot, is a major challenge for global livestock. One of the animal breeding goals for sustainable livestock production should be to breed animals with excellent climate adaptability. Indigenous livestock and poultry are well adapted to the local climate, and they are good resources to study the genetic footprints and mechanism of the resilience to weather extremes. In order to identify selection signatures and genes that might be involved in hot adaptation in indigenous chickens from different tropical climates, we conducted a genomic analysis of 65 indigenous chickens that inhabit different climates. Several important unique positively selected genes (PSGs) were identified for each local chicken group by the cross-population extended haplotype homozygosity (XP-EHH). These PSGs, verified by composite likelihood ratio, genetic differentiation index, nucleotide diversity, Tajima’s D, and decorrelated composite of multiple signals, are related to nerve regulation, vascular function, immune function, lipid metabolism, kidney development, and function, which are involved in thermoregulation and hot adaptation. However, one common PSG was detected for all three tropical groups of chickens via XP-EHH but was not confirmed by other five types of selective sweep analyses. These results suggest that the hot adaptability of indigenous chickens from different tropical climate regions has evolved in parallel by taking different pathways with different sets of genes. The results from our study have provided reasonable explanations and insights for the rapid adaptation of chickens to diverse tropical climates and provide practical values for poultry breeding.
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Affiliation(s)
- Nai-Yi Xu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Zhen-Yu Liu
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Qi-Meng Yang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Pei-Pei Bian
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, China
| | - Ming Li
- Department of Biology, University of Konstanz, Konstanz, Germany
| | - Xin Zhao
- Department of Animal Science, McGill University, Montreal, QC, Canada
- *Correspondence: Xin Zhao,
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Ng CS, Lai CK, Ke HM, Lee HH, Chen CF, Tang PC, Cheng HC, Lu MJ, Li WH, Tsai IJ. Genome Assembly and Evolutionary Analysis of the Mandarin Duck Aix galericulata Reveal Strong Genome Conservation among Ducks. Genome Biol Evol 2022; 14:evac083. [PMID: 35640266 PMCID: PMC9189614 DOI: 10.1093/gbe/evac083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
The mandarin duck, Aix galericulata, is popular in East Asian cultures and displays exaggerated sexual dimorphism, especially in feather traits during breeding seasons. We generated and annotated the first mandarin duck de novo assembly, which was 1.08 Gb in size and encoded 16,615 proteins. Using a phylogenomic approach calibrated with fossils and molecular divergences, we inferred that the last common ancestor of ducks occurred 13.3-26.7 Ma. The majority of the mandarin duck genome repetitive sequences belonged to the chicken repeat 1 (CR1) retroposon CR1-J2_Pass, which underwent a duck lineage-specific burst. Synteny analyses among ducks revealed infrequent chromosomal rearrangements in which breaks were enriched in LINE retrotransposons and DNA transposons. The calculation of the dN/dS ratio revealed that the majority of duck genes were under strong purifying selection. The expanded gene families in the mandarin duck are primarily involved in olfactory perception as well as the development and morphogenesis of feather and branching structures. This new reference genome will improve our understanding of the morphological and physiological characteristics of ducks and provide a valuable resource for functional genomics studies to investigate the feather traits of the mandarin duck.
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Affiliation(s)
- Chen Siang Ng
- Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu, Taiwan
- Department of Life Science, National Tsing Hua University, Hsinchu, Taiwan
- Bioresource Conservation Research Center, National Tsing Hua University, Hsinchu, Taiwan
- The iEGG and Animal Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
| | - Cheng-Kuo Lai
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan
| | - Huei-Mien Ke
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
| | - Hsin-Han Lee
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
| | - Chih-Feng Chen
- The iEGG and Animal Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
- Department of Animal Science, National Chung Hsing University, Taichung, Taiwan
| | - Pin-Chi Tang
- The iEGG and Animal Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
- Department of Animal Science, National Chung Hsing University, Taichung, Taiwan
| | - Hsu-Chen Cheng
- The iEGG and Animal Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
- Department of Life Science, National Chung Hsing University, Taichung, Taiwan
| | - Meiyeh J. Lu
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
| | - Wen-Hsiung Li
- The iEGG and Animal Biotechnology Center, National Chung Hsing University, Taichung, Taiwan
- Biodiversity Research Center, Academia Sinica, Taipei, Taiwan
- Department of Ecology and Evolution, University of Chicago, Illinois, USA
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12
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Assessment of linkage disequilibrium patterns between structural variants and single nucleotide polymorphisms in three commercial chicken populations. BMC Genomics 2022; 23:193. [PMID: 35264116 PMCID: PMC8908679 DOI: 10.1186/s12864-022-08418-7] [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: 08/31/2021] [Accepted: 02/24/2022] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Structural variants (SV) are causative for some prominent phenotypic traits of livestock as different comb types in chickens or color patterns in pigs. Their effects on production traits are also increasingly studied. Nevertheless, accurately calling SV remains challenging. It is therefore of interest, whether close-by single nucleotide polymorphisms (SNPs) are in strong linkage disequilibrium (LD) with SVs and can serve as markers. Literature comes to different conclusions on whether SVs are in LD to SNPs on the same level as SNPs to other SNPs. The present study aimed to generate a precise SV callset from whole-genome short-read sequencing (WGS) data for three commercial chicken populations and to evaluate LD patterns between the called SVs and surrounding SNPs. It is thereby the first study that assessed LD between SVs and SNPs in chickens. RESULTS The final callset consisted of 12,294,329 bivariate SNPs, 4,301 deletions (DEL), 224 duplications (DUP), 218 inversions (INV) and 117 translocation breakpoints (BND). While average LD between DELs and SNPs was at the same level as between SNPs and SNPs, LD between other SVs and SNPs was strongly reduced (DUP: 40%, INV: 27%, BND: 19% of between-SNP LD). A main factor for the reduced LD was the presence of local minor allele frequency differences, which accounted for 50% of the difference between SNP - SNP and DUP - SNP LD. This was potentially accompanied by lower genotyping accuracies for DUP, INV and BND compared with SNPs and DELs. An evaluation of the presence of tag SNPs (SNP in highest LD to the variant of interest) further revealed DELs to be slightly less tagged by WGS SNPs than WGS SNPs by other SNPs. This difference, however, was no longer present when reducing the pool of potential tag SNPs to SNPs located on four different chicken genotyping arrays. CONCLUSIONS The results implied that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects.
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13
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Zhang X, Wang H, Lou L, Li Q, Zhang L, Ge Y. Transcript expression profiling of fibromelanosis-related genes in black-bone chickens. Br Poult Sci 2021; 63:133-141. [PMID: 34402346 DOI: 10.1080/00071668.2021.1966750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
1. The aim of the present study was to identify differentially expressed genes (DEGs) and metabolic pathways involved in this phenotype. Fibromelanosis is the most striking feature of black-bone chickens, such as the Silkie and Dongxiang indigenous breeds. Due to the accumulation of eumelanin in connective tissues, fibromelanosis manifests as black colouration of the skin, muscles, gut, and periosteum. Studies on fibromelanosis can provide useful information pertaining to human diseases and offer commercial value to the poultry industry. However, the genetic basis of fibromelanosis remains unclear.2. Digital gene expression analysis was performed on black and white skin samples collected from the HW1 black-bone chicken line to detect differences in genome-wide expression patterns. A total of >30 billion bp were sequenced, and 2,707,926,466 bp and 2,948,782,964 bp of clean data obtained for creation of libraries for black and white skin, respectively. In total, 252 DEGs from 15,508 mapped genes were identified with 83 up-regulated in white skin and 169 up-regulated in black skin.3. Gene ontology analysis highlighted that genes from the extracellular region and associated components were abundant among the DEGs. Pathway analysis revealed that many DEGs were linked to amino acid metabolism and the immune system. qRT-PCR validation using 14 genes showed good conformity with the sequence analysis of fibromelanosis-related genes.4. The results showed that L-dopachrometautomerase precursor (DCT), tyrosine aminotransferase (TAT), 4-hydroxyphenylpyruvate dioxygenase (HPD) from the tyrosine metabolism pathway, coagulation factor II (F2), fibrinogen beta chain (FGB), plasminogen (PLG) and complement component 7 (C7) from the complement and coagulation cascades were important genes in the fibromelanosis process in black-bone chickens. These candidate genes require further correlation analysis and functional verification.
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Affiliation(s)
- X Zhang
- Institute of Animal Husbandry, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
| | - H Wang
- Institute of Animal Husbandry, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
| | - L Lou
- Institute of Animal Husbandry, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
| | - Q Li
- Institute of Animal Husbandry, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
| | - L Zhang
- Institute of Animal Husbandry, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
| | - Y Ge
- Institute of Animal Husbandry, Hangzhou Academy of Agricultural Sciences, Hangzhou, China
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14
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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: 68] [Impact Index Per Article: 22.7] [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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15
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Wang MS, Zhang JJ, Guo X, Li M, Meyer R, Ashari H, Zheng ZQ, Wang S, Peng MS, Jiang Y, Thakur M, Suwannapoom C, Esmailizadeh A, Hirimuthugoda NY, Zein MSA, Kusza S, Kharrati-Koopaee H, Zeng L, Wang YM, Yin TT, Yang MM, Li ML, Lu XM, Lasagna E, Ceccobelli S, Gunwardana HGTN, Senasig TM, Feng SH, Zhang H, Bhuiyan AKFH, Khan MS, Silva GLLP, Thuy LT, Mwai OA, Ibrahim MNM, Zhang G, Qu KX, Hanotte O, Shapiro B, Bosse M, Wu DD, Han JL, Zhang YP. Large-scale genomic analysis reveals the genetic cost of chicken domestication. BMC Biol 2021; 19:118. [PMID: 34130700 PMCID: PMC8207802 DOI: 10.1186/s12915-021-01052-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 05/19/2021] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Species domestication is generally characterized by the exploitation of high-impact mutations through processes that involve complex shifting demographics of domesticated species. These include not only inbreeding and artificial selection that may lead to the emergence of evolutionary bottlenecks, but also post-divergence gene flow and introgression. Although domestication potentially affects the occurrence of both desired and undesired mutations, the way wild relatives of domesticated species evolve and how expensive the genetic cost underlying domestication is remain poorly understood. Here, we investigated the demographic history and genetic load of chicken domestication. RESULTS We analyzed a dataset comprising over 800 whole genomes from both indigenous chickens and wild jungle fowls. We show that despite having a higher genetic diversity than their wild counterparts (average π, 0.00326 vs. 0.00316), the red jungle fowls, the present-day domestic chickens experienced a dramatic population size decline during their early domestication. Our analyses suggest that the concomitant bottleneck induced 2.95% more deleterious mutations across chicken genomes compared with red jungle fowls, supporting the "cost of domestication" hypothesis. Particularly, we find that 62.4% of deleterious SNPs in domestic chickens are maintained in heterozygous states and masked as recessive alleles, challenging the power of modern breeding programs to effectively eliminate these genetic loads. Finally, we suggest that positive selection decreases the incidence but increases the frequency of deleterious SNPs in domestic chicken genomes. CONCLUSION This study reveals a new landscape of demographic history and genomic changes associated with chicken domestication and provides insight into the evolutionary genomic profiles of domesticated animals managed under modern human selection.
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Affiliation(s)
- Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China.,Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.,Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Jin-Jin Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Xing Guo
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, China
| | - Ming Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, 712100, China
| | - Rachel Meyer
- Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Hidayat Ashari
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Bogor, 16911, Indonesia.,CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Zhu-Qing Zheng
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, The Cooperative Innovation Center for Sustainable Pig Production, Ministry of Education, Huazhong Agricultural University, Wuhan, 430070, China
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, 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, 712100, China
| | - Mukesh Thakur
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Zoological Survey of India, New Alipore, Kolkata, West Bengal, 700053, India
| | - Chatmongkon Suwannapoom
- School of Agriculture and Natural Resources, University of Phayao, Phayao, 56000, Thailand.,Unit of Excellence on Biodiversity and Natural Resources Management, University of Phayao, Phayao, 56000, Thailand
| | - Ali Esmailizadeh
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Department of Animal Science, Shahid Bahonar University of Kerman, P.O. Box 76169133, Kerman, Iran
| | - Nalini Yasoda Hirimuthugoda
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Faculty of Agriculture, University of Ruhuna, Matara, Sri Lanka
| | - Moch Syamsul Arifin Zein
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Bogor, 16911, Indonesia
| | - Szilvia Kusza
- Institute of Animal Husbandry, Biotechnology and Nature Conservation, University of Debrecen, Debrecen, H-4032, Hungary
| | - Hamed Kharrati-Koopaee
- Department of Animal Science, Shahid Bahonar University of Kerman, P.O. Box 76169133, Kerman, Iran.,Institute of Biotechnology, School of Agriculture, Shiraz University, P.O. Box 1585, Shiraz, Iran
| | - Lin Zeng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Yun-Mei Wang
- Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
| | - Ting-Ting Yin
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Min-Min Yang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Ming-Li Li
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Xue-Mei Lu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China
| | - Emiliano Lasagna
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, 06123, Perugia, Italy
| | - Simone Ceccobelli
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, 06123, Perugia, Italy
| | | | | | - Shao-Hong Feng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, 518083, China
| | - Hao Zhang
- Laboratory of Animal Genetics, Breeding and Reproduction, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Ministry of Agriculture of China, Beijing, 100193, China
| | | | | | | | - Le Thi Thuy
- National Institute of Animal Husbandry, Hanoi, Vietnam
| | - Okeyo A Mwai
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, 00100, Kenya
| | | | - Guojie Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China.,China National Genebank, BGI-Shenzhen, Shenzhen, 518083, China.,Centre for Social Evolution, Department of Biology, University of Copenhagen, DK-1870, Copenhagen, Denmark
| | - Kai-Xing Qu
- Yunnan Academy of Grassland and Animal Science, Kunming, 650212, China
| | - Olivier Hanotte
- Cells, Organisms and Molecular Genetics, School of Life Sciences, University of Nottingham, Nottingham, NG7 2RD, UK.,Livestock Genetics Program, International Livestock Research Institute (ILRI), P.O. Box 5689, Addis Ababa, Ethiopia
| | - Beth Shapiro
- Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA, 95064, USA.,Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, 95064, USA
| | - Mirte Bosse
- Wageningen University & Research - Animal Breeding and Genomics, 6708 PB, Wageningen, The Netherlands.
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China.
| | - Jian-Lin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China. .,Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, 00100, Kenya.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China. .,Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650204, China. .,State Key Laboratory for Conservation and Utilization of Bio-resource, Yunnan University, Kunming, 650091, China.
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16
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Wang YM, Khederzadeh S, Li SR, Otecko NO, Irwin DM, Thakur M, Ren XD, Wang MS, Wu DD, Zhang YP. Integrating Genomic and Transcriptomic Data to Reveal Genetic Mechanisms Underlying Piao Chicken Rumpless Trait. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:787-799. [PMID: 33631431 PMCID: PMC9170765 DOI: 10.1016/j.gpb.2020.06.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 04/14/2020] [Accepted: 06/10/2020] [Indexed: 11/19/2022]
Abstract
Piao chicken, a rare Chinese native poultry breed, lacks primary tail structures, such as pygostyle, caudal vertebra, uropygial gland, and tail feathers. So far, the molecular mechanisms underlying tail absence in this breed remain unclear. In this study, we comprehensively employed comparative transcriptomic and genomic analyses to unravel potential genetic underpinnings of rumplessness in Piao chicken. Our results reveal many biological factors involved in tail development and several genomic regions under strong positive selection in this breed. These regions contain candidate genes associated with rumplessness, including Irx4, Il18, Hspb2, and Cryab. Retrieval of quantitative trait loci (QTL) and gene functions implies that rumplessness might be consciously or unconsciously selected along with the high-yield traits in Piao chicken. We hypothesize that strong selection pressures on regulatory elements might lead to changes in gene activity in mesenchymal stem cells of the tail bud. The ectopic activity could eventually result in tail truncation by impeding differentiation and proliferation of the stem cells. Our study provides fundamental insights into early initiation and genetic basis of the rumpless phenotype in Piao chicken.
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Affiliation(s)
- Yun-Mei Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China; Center for Neurobiology and Brain Restoration, Skolkovo Institute of Science and Technology, Moscow 143026, Russia
| | - Saber Khederzadeh
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China
| | - Shi-Rong Li
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China
| | - Newton Otieno Otecko
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China
| | - David M Irwin
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto M5S 1A8, Canada
| | - Mukesh Thakur
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Zoological Survey of India, Kolkata 700053, India
| | - Xiao-Die Ren
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China
| | - Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China.
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China; Kunming College of Life Science, University of the Chinese Academy of Sciences, Kunming 650223, China.
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Fitzpatrick C, Ciresi CM, Wade MJ. The evolutionary genetics of paternal care: How good genes and extrapair copulation affect the trade-off between paternal care and mating success. Ecol Evol 2021; 11:1165-1174. [PMID: 33598121 PMCID: PMC7863384 DOI: 10.1002/ece3.7058] [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: 03/18/2020] [Revised: 09/21/2020] [Accepted: 09/28/2020] [Indexed: 11/06/2022] Open
Abstract
We investigate the evolution of a gene for paternal care, with pleiotropic effects on male mating fitness and offspring viability, with and without extrapair copulations (EPCs). We develop a population genetic model to examine how pleiotropic effects of a male mating advantage and paternal care are affected by "good genes" and EPCs. Using this approach, we show that the relative effects of each on fitness do not always predict the evolutionary change. We then find the line of combinations of mating success and paternal care that bisects the plane of possible values into regions of positive or negative gene frequency change. This line shifts when either good genes or EPCs are introduced, thereby expanding or contracting the region of positive gene frequency change and significantly affecting the evolution of paternal care. Predictably, a direct viability effect of "good genes" that enhances offspring viability constrains or expands the parameter space over which paternal care can evolve, depending on whether the viability effect is associated with the paternal care allele or not. In either case, the effect of a "good gene" that enhances offspring viability is substantial; when strong enough, it can even facilitate the evolution of poor paternal care, where males harm their young. When nonrandom mating is followed by random EPCs, the genetic regression between sire and offspring is reduced and, consequently, the relative strengths of selection are skewed away from paternal care and toward the male mating advantage. However, when random mating is followed by nonrandom EPCs, a situation called "trading up" by females, we show that selection is skewed in the opposite direction, away from male mating advantage and toward paternal care across the natural range of EPC frequencies.
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Genomic variations and signatures of selection in Wuhua yellow chicken. PLoS One 2020; 15:e0241137. [PMID: 33095808 PMCID: PMC7584229 DOI: 10.1371/journal.pone.0241137] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 10/08/2020] [Indexed: 02/08/2023] Open
Abstract
Wuhua yellow chicken (WHYC) is an important traditional yellow-feathered chicken from China, which is characterized by its white tail feathers, white flight feathers, and strong disease resistance. However, the genomic basis of these unique traits associated with WHYC is poorly understood. In this study, whole-genome resequencing was performed with an average coverage of 20.77-fold to investigate heritable variation and identify selection signals in WHYC. Reads were mapped onto the chicken reference genome (Galgal5) with a coverage of 85.95%. After quality control, 11,953,471 single nucleotide polymorphisms and 1,069,574 insertion/deletions were obtained. In addition, 41,408 structural variants and 33,278 copy number variants were found. Comparative genomic analysis of WHYC and other yellow-feathered chicken breeds showed that selected regions were enriched in genes involved in transport and catabolism, immune system, infectious diseases, signal transduction, and signaling molecules and interactions. Several genes associated with disease resistance were also identified, including IFNA, IFNB, CD86, IL18, IL11RA, VEGFC, and ATG10. Furthermore, our results suggest that PMEL and TYRP1 may contribute to the white feather coloring in WHYC. These findings can improve our understanding of the genetic characteristics of WHYC and may contribute to future breed improvement.
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Ma L, Li Y, Ma X, EER H. Genome-wide SNPs and indels characteristics of three chinese domestic sheep breeds from different ecoregions. Livest Sci 2020. [DOI: 10.1016/j.livsci.2020.104122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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20
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Wang MS, Thakur M, Peng MS, Jiang Y, Frantz LAF, Li M, Zhang JJ, Wang S, Peters J, Otecko NO, Suwannapoom C, Guo X, Zheng ZQ, Esmailizadeh A, Hirimuthugoda NY, Ashari H, Suladari S, Zein MSA, Kusza S, Sohrabi S, Kharrati-Koopaee H, Shen QK, Zeng L, Yang MM, Wu YJ, Yang XY, Lu XM, Jia XZ, Nie QH, Lamont SJ, Lasagna E, Ceccobelli S, Gunwardana HGTN, Senasige TM, Feng SH, Si JF, Zhang H, Jin JQ, Li ML, Liu YH, Chen HM, Ma C, Dai SS, Bhuiyan AKFH, Khan MS, Silva GLLP, Le TT, Mwai OA, Ibrahim MNM, Supple M, Shapiro B, Hanotte O, Zhang G, Larson G, Han JL, Wu DD, Zhang YP. 863 genomes reveal the origin and domestication of chicken. Cell Res 2020; 30:693-701. [PMID: 32581344 PMCID: PMC7395088 DOI: 10.1038/s41422-020-0349-y] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 05/20/2020] [Indexed: 01/10/2023] Open
Abstract
Despite the substantial role that chickens have played in human societies across the world, both the geographic and temporal origins of their domestication remain controversial. To address this issue, we analyzed 863 genomes from a worldwide sampling of chickens and representatives of all four species of wild jungle fowl and each of the five subspecies of red jungle fowl (RJF). Our study suggests that domestic chickens were initially derived from the RJF subspecies Gallus gallus spadiceus whose present-day distribution is predominantly in southwestern China, northern Thailand and Myanmar. Following their domestication, chickens were translocated across Southeast and South Asia where they interbred locally with both RJF subspecies and other jungle fowl species. In addition, our results show that the White Leghorn chicken breed possesses a mosaic of divergent ancestries inherited from other subspecies of RJF. Despite the strong episodic gene flow from geographically divergent lineages of jungle fowls, our analyses show that domestic chickens undergo genetic adaptations that underlie their unique behavioral, morphological and reproductive traits. Our study provides novel insights into the evolutionary history of domestic chickens and a valuable resource to facilitate ongoing genetic and functional investigations of the world's most numerous domestic animal.
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Affiliation(s)
- Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
- Department of Ecology and Evolutionary Biology, Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Mukesh Thakur
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Zoological Survey of India, New Alipore, Kolkata, West Bengal, India
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, 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
| | - Laurent Alain François Frantz
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- The Palaeogenomics and Bio-Archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK
- School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
| | - Ming Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Jin-Jin Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Joris Peters
- ArchaeoBioCenter and Department of Veterinary Sciences, Institute of Palaeoanatomy, Domestication Research and the History of Veterinary Medicine, LMU Munich, Munich, Germany
- SNSB, Bavarian State Collection of Anthropology and Palaeoanatomy, Munich, Germany
| | - Newton Otieno Otecko
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | | | - Xing Guo
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Zhu-Qing Zheng
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, China
| | - Ali Esmailizadeh
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Department of Animal Science, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Nalini Yasoda Hirimuthugoda
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Faculty of Agriculture, University of Ruhuna, Matara, Sri Lanka
| | - Hidayat Ashari
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Indonesia
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China
| | - Sri Suladari
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Indonesia
| | - Moch Syamsul Arifin Zein
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Indonesia
| | - Szilvia Kusza
- Institute of Animal Husbandry, Biotechnology and Nature Conservation, University of Debrecen, Debrecen, Hungary
| | - Saeed Sohrabi
- Department of Animal Science, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Hamed Kharrati-Koopaee
- Department of Animal Science, Shahid Bahonar University of Kerman, Kerman, Iran
- Institute of Biotechnology, School of Agriculture, Shiraz University, Shiraz, Iran
| | - Quan-Kuan Shen
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Lin Zeng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Min-Min Yang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Ya-Jiang Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- State Key Laboratory for Conservation and Utilization of Bio-resource, Yunnan University, Kunming, Yunnan, China
| | - Xing-Yan Yang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- State Key Laboratory for Conservation and Utilization of Bio-resource, Yunnan University, Kunming, Yunnan, China
| | - Xue-Mei Lu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Xin-Zheng Jia
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, Kenya
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Qing-Hua Nie
- College of Animal Science, South China Agricultural University, Guangzhou, Guangdong, China
| | - Susan Joy Lamont
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Emiliano Lasagna
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, Perugia, Italy
| | - Simone Ceccobelli
- Dipartimento di Scienze Agrarie, Alimentarie Ambientali, University of Perugia, Perugia, Italy
| | | | | | - Shao-Hong Feng
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, Guangdong, China
| | - Jing-Fang Si
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Hao Zhang
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Jie-Qiong Jin
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Southeast Asia Biodiversity Research Institute, Chinese Academy of Sciences (CAS-SEABRI), Yezin, Myanmar
| | - Ming-Li Li
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Yan-Hu Liu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Hong-Man Chen
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Cheng Ma
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Shan-Shan Dai
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | | | | | | | - Thi-Thuy Le
- National Institute of Animal Husbandry, Hanoi, Vietnam
| | - Okeyo Ally Mwai
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, Kenya
| | | | - Megan Supple
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Beth Shapiro
- Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
- Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, USA
| | - Olivier Hanotte
- Cells, Organisms and Molecular Genetics, School of Life Sciences, University of Nottingham, Nottingham, UK
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Addis Ababa, Ethiopia
| | - Guojie Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China
- Department of Biology, Centre for Social Evolution, University of Copenhagen, Copenhagen, Denmark
- China National Genebank, BGI-Shenzhen, Shenzhen, Guangdong, China
| | - Greger Larson
- The Palaeogenomics and Bio-Archaeology Research Network, Research Laboratory for Archaeology and History of Art, University of Oxford, Oxford, UK
| | - Jian-Lin Han
- CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China.
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, Kenya.
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China.
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China.
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China.
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, Yunnan, China.
- State Key Laboratory for Conservation and Utilization of Bio-resource, Yunnan University, Kunming, Yunnan, China.
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21
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Alessandri G, Milani C, Mancabelli L, Mangifesta M, Lugli GA, Viappiani A, Duranti S, Turroni F, Ossiprandi MC, van Sinderen D, Ventura M. The impact of human-facilitated selection on the gut microbiota of domesticated mammals. FEMS Microbiol Ecol 2020; 95:5538759. [PMID: 31344227 DOI: 10.1093/femsec/fiz121] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 07/19/2019] [Indexed: 12/26/2022] Open
Abstract
Domestication is the process by which anthropogenic forces shape lifestyle and behavior of wild species to accommodate human needs. The impact of domestication on animal physiology and behavior has been extensively studied, whereas its effect on the gut microbiota is still largely unexplored. For this reason, 16S rRNA gene-based and internal transcribed spacer-mediated bifidobacterial profiling, together with shotgun metagenomics, was employed to investigate the taxonomic composition and metabolic repertoire of 146 mammalian fecal samples, corresponding to 12 domesticated-feral dyads. Our results revealed that changes induced by domestication have extensively shaped the taxonomic composition of the mammalian gut microbiota. In this context, the selection of microbial taxa linked to a more efficient feed conversion into body mass and putative horizontal transmission of certain bacterial genera from humans were observed in the fecal microbiota of domesticated animals when compared to their feral relatives and to humans. In addition, profiling of the metabolic arsenal through metagenomics highlighted extensive functional adaptation of the fecal microbial community of domesticated mammals to changes induced by domestication. Remarkably, domesticated animals showed, when compared to their feral relatives, increased abundance of specific glycosyl hydrolases, possibly due to the higher intake of complex plant carbohydrates typical of commercial animal feeds.
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Affiliation(s)
- Giulia Alessandri
- Department of Veterinary Science, University of Parma, Via del Taglio 8, 43100 Parma, Italy
| | - Christian Milani
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Leonardo Mancabelli
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Marta Mangifesta
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Gabriele Andrea Lugli
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Alice Viappiani
- GenProbio srl, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Sabrina Duranti
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Francesca Turroni
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy.,Microbiome Research Hub, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Maria Cristina Ossiprandi
- Department of Veterinary Science, University of Parma, Via del Taglio 8, 43100 Parma, Italy.,Microbiome Research Hub, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
| | - Douwe van Sinderen
- APC Microbiome Institute and School of Microbiology, Bioscience Institute, National University of Ireland, Western Road, Cork, Ireland
| | - Marco Ventura
- Laboratory of Probiogenomics, Department of Chemistry, Life Sciences, and Environmental Sustainability, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy.,Microbiome Research Hub, University of Parma, Parco Area delle Scienze 11a, 43124 Parma, Italy
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22
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Ahmad HI, Ahmad MJ, Jabbir F, Ahmar S, Ahmad N, Elokil AA, Chen J. The Domestication Makeup: Evolution, Survival, and Challenges. Front Ecol Evol 2020. [DOI: 10.3389/fevo.2020.00103] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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23
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Hou Y, Qi F, Bai X, Ren T, Shen X, Chu Q, Zhang X, Lu X. Genome-wide analysis reveals molecular convergence underlying domestication in 7 bird and mammals. BMC Genomics 2020; 21:204. [PMID: 32131728 PMCID: PMC7057487 DOI: 10.1186/s12864-020-6613-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Accepted: 02/24/2020] [Indexed: 12/19/2022] Open
Abstract
Background In response to ecological niche of domestication, domesticated mammals and birds developed adaptively phenotypic homoplasy in behavior modifications like fearlessness, altered sociability, exploration and cognition, which partly or indirectly result in consequences for economic productivity. Such independent adaptations provide an excellent model to investigate molecular mechanisms and patterns of evolutionary convergence driven by artificial selection. Results First performing population genomic and brain transcriptional comparisons in 68 wild and domesticated chickens, we revealed evolutionary trajectories, genetic architectures and physiologic bases of adaptively behavioral alterations. To extensively decipher molecular convergence on behavioral changes thanks to domestication, we investigated selection signatures in hundreds of genomes and brain transcriptomes across chicken and 6 other domesticated mammals. Although no shared substitution was detected, a common enrichment of the adaptive mutations in regulatory sequences was observed, presenting significance to drive adaptations. Strong convergent pattern emerged at levels of gene, gene family, pathway and network. Genes implicated in neurotransmission, semaphorin, tectonic protein and modules regulating neuroplasticity were central focus of selection, supporting molecular repeatability of homoplastic behavior reshapes. Genes at nodal positions in trans-regulatory networks were preferably targeted. Consistent down-regulation of majority brain genes may be correlated with reduced brain size during domestication. Up-regulation of splicesome genes in chicken rather mammals highlights splicing as an efficient way to evolve since avian-specific genomic contraction of introns and intergenics. Genetic burden of domestication elicits a general hallmark. The commonly selected genes were relatively evolutionary conserved and associated with analogous neuropsychiatric disorders in human, revealing trade-off between adaption to life with human at the cost of neural changes affecting fitness in wild. Conclusions After a comprehensive investigation on genomic diversity and evolutionary trajectories in chickens, we revealed basis, pattern and evolutionary significance of molecular convergence in domesticated bird and mammals, highlighted the genetic basis of a compromise on utmost adaptation to the lives with human at the cost of high risk of neurophysiological changes affecting animals’ fitness in wild.
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Affiliation(s)
- Yali Hou
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People's Republic of China. .,China National Center for Bioinformation, Beijing, People's Republic of China.
| | - Furong Qi
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People's Republic of China.,China National Center for Bioinformation, Beijing, People's Republic of China.,University of Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xue Bai
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People's Republic of China.,China National Center for Bioinformation, Beijing, People's Republic of China
| | - Tong Ren
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xu Shen
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Qin Chu
- Institute of Animal Husbandry and Veterinary Medicine, Beijing Academy of Agriculture and Forestry Sciences, Beijing, People's Republic of China
| | - Xiquan Zhang
- Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Lab of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, South China Agricultural University, Guangzhou, People's Republic of China.
| | - Xuemei Lu
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People's Republic of China. .,University of Chinese Academy of Sciences, Beijing, People's Republic of China. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, People's Republic of China.
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24
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Zou A, Nadeau K, Wang PW, Lee JY, Guttman DS, Sharif S, Korver DR, Brumell JH, Parkinson J. Accumulation of genetic variants associated with immunity in the selective breeding of broilers. BMC Genet 2020; 21:5. [PMID: 31952471 PMCID: PMC6969402 DOI: 10.1186/s12863-020-0807-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 01/03/2020] [Indexed: 12/19/2022] Open
Abstract
Background To satisfy an increasing demand for dietary protein, the poultry industry has employed genetic selection to increase the growth rate of broilers by over 400% in the past 50 years. Although modern broilers reach a marketable weight of ~ 2 kg in a short span of 35 days, a speed twice as fast as a broiler 50 years ago, the expedited growth has been associated with several negative detrimental consequences. Aside from heart and musculoskeletal problems, which are direct consequences of additional weight, the immune response is also thought to be altered in modern broilers. Results Given that identifying the underlying genetic basis responsible for a less sensitive innate immune response would be economically beneficial for poultry breeding, we decided to compare the genomes of two unselected meat control strains that are representative of broilers from 1957 and 1978, and a current commercial broiler line. Through analysis of genetic variants, we developed a custom prioritization strategy to identify genes and pathways that have accumulated genetic changes and are biologically relevant to immune response and growth performance. Our results highlight two genes, TLR3 and PLIN3, with genetic variants that are predicted to enhance growth performance at the expense of immune function. Conclusions Placing these new genomes in the context of other chicken lines, reveal genetic changes that have specifically arisen in selective breeding programs that were implemented in the last 50 years.
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Affiliation(s)
- Angela Zou
- Department of Biochemistry, University of Toronto, Toronto, M5S 1A8, ON, Canada.,Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON, M5G 0A4, Canada
| | - Kerry Nadeau
- Department of Agricultural Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Pauline W Wang
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, 25 Willcocks Street, Toronto, M5S 3G5, ON, Canada
| | - Jee Yeon Lee
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, 25 Willcocks Street, Toronto, M5S 3G5, ON, Canada
| | - David S Guttman
- Centre for the Analysis of Genome Evolution & Function, University of Toronto, 25 Willcocks Street, Toronto, M5S 3G5, ON, Canada.,Department of Cell and Systems Biology, University of Toronto, Toronto, M5S 3G5, ON, Canada
| | - Shayan Sharif
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, N1G 2W1, ON, Canada
| | - Doug R Korver
- Department of Agricultural Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - John H Brumell
- Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, ON, Canada.,Program in Cell Biology, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON, M5G 0A4, Canada
| | - John Parkinson
- Department of Biochemistry, University of Toronto, Toronto, M5S 1A8, ON, Canada. .,Program in Molecular Medicine, Hospital for Sick Children, Peter Gilgan Center for Research and Learning, 686 Bay Street, Toronto, ON, M5G 0A4, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, M5S 1A8, ON, Canada.
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Identification of Copy Number Variation in Domestic Chicken Using Whole-Genome Sequencing Reveals Evidence of Selection in the Genome. Animals (Basel) 2019; 9:ani9100809. [PMID: 31618984 PMCID: PMC6826909 DOI: 10.3390/ani9100809] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 10/13/2019] [Accepted: 10/14/2019] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Chickens have been bred for meat and egg production as a source of animal protein. With the increase of productivity as the main purpose of domestication, factors such as metabolism and immunity were boosted, which are detectable signs of selection on the genome. This study focused on copy number variation (CNV) to find evidence of domestication on the genome. CNV was detected from whole-genome sequencing of 65 chickens including Red Jungle Fowl, broilers, and layers. After that, CNV region, the overlapping region of CNV between individuals, was made to identify which genomic regions showed copy number differentiation. The 663 domesticated-specific CNV regions were associated with various functions such as metabolism and organ development. Also, by performing population differentiation analyses such as clustering analysis and ANOVA test, we found that there are a lot of genomic regions with different copy number patterns between broilers and layers. This result indicates that different genetic variations can be found, depending on the purpose of artificial selection and provides considerations for future animal breeding. Abstract Copy number variation (CNV) has great significance both functionally and evolutionally. Various CNV studies are in progress to find the cause of human disease and to understand the population structure of livestock. Recent advances in next-generation sequencing (NGS) technology have made CNV detection more reliable and accurate at whole-genome level. However, there is a lack of CNV studies on chickens using NGS. Therefore, we obtained whole-genome sequencing data of 65 chickens including Red Jungle Fowl, Cornish (broiler), Rhode Island Red (hybrid), and White Leghorn (layer) from the public databases for CNV region (CNVR) detection. Using CNVnator, a read-depth based software, a total of 663 domesticated-specific CNVRs were identified across autosomes. Gene ontology analysis of genes annotated in CNVRs showed that mainly enriched terms involved in organ development, metabolism, and immune regulation. Population analysis revealed that CN and RIR are closer to each other than WL, and many genes (LOC772271, OR52R1, RD3, ADH6, TLR2B, PRSS2, TPK1, POPDC3, etc.) with different copy numbers between breeds found. In conclusion, this study has helped to understand the genetic characteristics of domestic chickens at CNV level, which may provide useful information for the development of breeding systems in chickens.
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Manjula P, Cho S, Suh KJ, Seo D, Lee JH. Single Nucleotide Polymorphism of TBC1D1 Gene Association with Growth
Traits and Serum Clinical-Chemical Traits in Chicken. ACTA ACUST UNITED AC 2018. [DOI: 10.5536/kjps.2018.45.4.291] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Drobik-Czwarno W, Wolc A, Fulton JE, Dekkers JCM. Detection of copy number variations in brown and white layers based on genotyping panels with different densities. Genet Sel Evol 2018; 50:54. [PMID: 30400769 PMCID: PMC6219011 DOI: 10.1186/s12711-018-0428-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 10/23/2018] [Indexed: 11/13/2022] Open
Abstract
Background Copy number variations (CNV) are an important source of genetic variation that has gained increasing attention over the last couple of years. In this study, we performed CNV detection and functional analysis for 18,719 individuals from four pure lines and one commercial cross of layer chickens. Samples were genotyped on four single nucleotide polymorphism (SNP) genotyping platforms, i.e. the Illumina 42K, Affymetrix 600K, and two different customized Affymetrix 50K chips. CNV recovered from the Affymetrix chips were identified by using the Axiom® CNV Summary Tools and PennCNV software and those from the Illumina chip were identified by using the cnvPartition in the Genome Studio software. Results The mean number of CNV per individual varied from 0.50 to 4.87 according to line or cross and size of the SNP genotyping set. The length of the detected CNV across all datasets ranged from 1.2 kb to 3.2 Mb. The number of duplications exceeded the number of deletions for most lines. Between the lines, there were considerable differences in the number of detected CNV and their distribution. Most of the detected CNV had a low frequency, but 19 CNV were identified with a frequency higher than 5% in birds that were genotyped on the 600K panel, with the most common CNV being detected in 734 birds from three lines. Conclusions Commonly used SNP genotyping platforms can be used to detect segregating CNV in chicken layer lines. The sample sizes for this study enabled a detailed characterization of the CNV landscape within commercially relevant lines. The size of the SNP panel used affected detection efficiency, with more CNV detected per individual on the higher density 600K panel. In spite of the high level of inter-individual diversity and a large number of CNV observed within individuals, we were able to detect 19 frequent CNV, of which, 57.9% overlapped with annotated genes and 89% overlapped with known quantitative trait loci. Electronic supplementary material The online version of this article (10.1186/s12711-018-0428-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Wioleta Drobik-Czwarno
- Department of Animal Science, Iowa State University, 806 Stange Road, 239E Kildee Hall, Ames, IA, 50010, USA. .,Department of Animal Genetics and Breeding, Faculty of Animal Science, Warsaw University of Life Sciences, Ciszewskiego 8, 02-786, Warsaw, Poland.
| | - Anna Wolc
- Department of Animal Science, Iowa State University, 806 Stange Road, 239E Kildee Hall, Ames, IA, 50010, USA.,Hy-Line International, 2583 240th Street, Dallas Center, IA, 50063, USA
| | - Janet E Fulton
- Hy-Line International, 2583 240th Street, Dallas Center, IA, 50063, USA
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, 806 Stange Road, 239E Kildee Hall, Ames, IA, 50010, USA
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28
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Khatri B, Hayden AM, Anthony NB, Kong BC. Whole Genome Resequencing of Arkansas Progressor and Regressor Line Chickens to Identify SNPs Associated with Tumor Regression. Genes (Basel) 2018; 9:genes9100512. [PMID: 30347774 PMCID: PMC6210987 DOI: 10.3390/genes9100512] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 10/12/2018] [Accepted: 10/16/2018] [Indexed: 12/16/2022] Open
Abstract
Arkansas Regressor (AR) chickens, unlike Arkansas Progressor (AP) chickens, regress tumors induced by the v-src oncogene. To better understand the genetic factors responsible for this tumor regression property, whole genome resequencing was conducted using Illumina Hi-Seq 2 × 100 bp paired-end read method (San Diego, CA, USA) with AR (confirmed tumor regression property) and AP chickens. Sequence reads were aligned to the chicken reference genome (galgal5) and produced coverage of 11× and 14× in AR and AP, respectively. A total of 7.1 and 7.3 million single nucleotide polymorphisms (SNPs) were present in AR and AP genomes, respectively. Through a series of filtration processes, a total of 12,242 SNPs were identified in AR chickens that were associated with non-synonymous, frameshift, nonsense, no-start and no-stop mutations. Further filtering of SNPs based on read depth ≥ 10, SNP% ≥ 0.75, and non-synonymous mutations identified 63 reliable marker SNPs which were chosen for gene network analysis. The network analysis revealed that the candidate genes identified in AR chickens play roles in networks centered to ubiquitin C (UBC), phosphoinositide 3-kinases (PI3K), and nuclear factor kappa B (NF-kB) complexes suggesting that the tumor regression property in AR chickens might be associated with ubiquitylation, PI3K, and NF-kB signaling pathways. This study provides an insight into genetic factors that could be responsible for the tumor regression property.
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Affiliation(s)
- Bhuwan Khatri
- Department of Poultry Science, Center of Excellence for Poultry Science, University of Arkansas, Fayetteville, NC AR 72701, USA.
| | - Ashley M Hayden
- Department of Poultry Science, Center of Excellence for Poultry Science, University of Arkansas, Fayetteville, NC AR 72701, USA.
| | - Nicholas B Anthony
- Department of Poultry Science, Center of Excellence for Poultry Science, University of Arkansas, Fayetteville, NC AR 72701, USA.
| | - Byungwhi C Kong
- Department of Poultry Science, Center of Excellence for Poultry Science, University of Arkansas, Fayetteville, NC AR 72701, USA.
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Bhanuprakash V, Chhotaray S, Pruthviraj DR, Rawat C, Karthikeyan A, Panigrahi M. Copy number variation in livestock: A mini review. Vet World 2018; 11:535-541. [PMID: 29805222 PMCID: PMC5960796 DOI: 10.14202/vetworld.2018.535-541] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Accepted: 03/31/2018] [Indexed: 01/22/2023] Open
Abstract
Copy number variation (CNV) is a phenomenon in which sections of the genome, ranging from one kilo base pair (Kb) to several million base pairs (Mb), are repeated and the number of repeats vary between the individuals in a population. It is an important source of genetic variation in an individual which is now being utilized rather than single nucleotide polymorphisms (SNPs), as it covers the more genomic region. CNVs alter the gene expression and change the phenotype of an individual due to deletion and duplication of genes in the copy number variation regions (CNVRs). Earlier, researchers extensively utilized SNPs as the main source of genetic variation. But now, the focus is on identification of CNVs associated with complex traits. With the recent advances and reduction in the cost of sequencing, arrays are developed for genotyping which cover the maximum number of SNPs at a time that can be used for detection of CNVRs and underlying quantitative trait loci (QTL) for the complex traits to accelerate genetic improvement. CNV studies are also being carried out to understand the evolutionary mechanism in the domestication of livestock and their adaptation to the different environmental conditions. The main aim of the study is to review the available data on CNV and its role in genetic variation among the livestock.
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Affiliation(s)
- V Bhanuprakash
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Izatnagar, Bareilly - 243122, Uttar Pradesh, India
| | - Supriya Chhotaray
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Izatnagar, Bareilly - 243122, Uttar Pradesh, India
| | - D R Pruthviraj
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Izatnagar, Bareilly - 243122, Uttar Pradesh, India
| | - Chandrakanta Rawat
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Izatnagar, Bareilly - 243122, Uttar Pradesh, India
| | - A Karthikeyan
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Izatnagar, Bareilly - 243122, Uttar Pradesh, India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Izatnagar, Bareilly - 243122, Uttar Pradesh, India
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Sohrabi SS, Mohammadabadi M, Wu DD, Esmailizadeh A. Detection of breed-specific copy number variations in domestic chicken genome. Genome 2017; 61:7-14. [PMID: 28961404 DOI: 10.1139/gen-2017-0016] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Copy number variations (CNVs) are important large-scale variants. They are widespread in the genome and may contribute to phenotypic variation. Detection and characterization of CNVs can provide new insights into the genetic basis of important traits. Here, we perform whole-genome short read sequence analysis to identify CNVs in two indigenous and commercial chicken breeds to evaluate the impact of the identified CNVs on breed-specific traits. After filtration, a total of 12 955 CNVs spanning (on average) about 9.42% of the chicken genome were found that made up 5467 CNV regions (CNVRs). Chicken quantitative trait loci (QTL) datasets and Ensembl gene annotations were used as resources for the estimation of potential phenotypic effects of our CNVRs on breed-specific traits. In total, 34% of our detected CNVRs were also detected in earlier CNV studies. These CNVRs partly overlap several previously reported QTL and gene ontology terms associated with some important traits, including shank length QTL in Creeper-specific CNVRs and body weight and egg production characteristics, as well as muscle and body organ growth, in the Arian commercial breed. Our findings provide new insights into the genomic structure of the chicken genome for an improved understanding of the potential roles of CNVRs in differentiating between breeds or lines.
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Affiliation(s)
- Saeed S Sohrabi
- a Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, PB 76169-133, Kerman, Iran.,b Young Researchers Society, Shahid Bahonar University of Kerman, PB 76169-133, Kerman, Iran
| | - Mohammadreza Mohammadabadi
- a Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, PB 76169-133, Kerman, Iran
| | - Dong-Dong Wu
- c State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,d Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Ali Esmailizadeh
- a Department of Animal Science, Faculty of Agriculture, Shahid Bahonar University of Kerman, PB 76169-133, Kerman, Iran.,c State Key Laboratory of Genetic Resources and Evolution & Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
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Lien CY, Tixier-Boichard M, Wu SW, Wang WF, Ng CS, Chen CF. Detection of QTL for traits related to adaptation to sub-optimal climatic conditions in chickens. Genet Sel Evol 2017; 49:39. [PMID: 28427323 PMCID: PMC5399330 DOI: 10.1186/s12711-017-0314-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Accepted: 03/31/2017] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Growth traits can be used as indicators of adaptation to sub-optimal conditions. The current study aimed at identifying quantitative trait loci (QTL) that control performance under variable temperature conditions in chickens. METHODS An F2 population was produced by crossing the Taiwan Country chicken L2 line (selected for body weight, comb area, and egg production) with an experimental line of Rhode Island Red layer R- (selected for low residual feed consumption). A total of 844 animals were genotyped with the 60 K Illumina single nucleotide polymorphism (SNP) chip. Whole-genome interval linkage mapping and a genome-wide association study (GWAS) were performed for body weight at 0, 4, 8, 12, and 16 weeks of age, shank length at 8 weeks of age, size of comb area at 16 weeks of age, and antibody response to sheep red blood cells at 11 weeks of age (7 and 14 days after primary immunization). Relevant genes were identified based on functional annotation of candidate genes and potentially relevant SNPs were detected by comparing whole-genome sequences of several birds between the parental lines. RESULTS Whole-genome QTL analysis revealed 47 QTL and 714 effects associated with 178 SNPs were identified by GWAS with 5% Bonferroni genome-wide significance. Little overlap was observed between the QTL and GWAS results, with only two chromosomal regions detected by both approaches, i.e. one on GGA24 (GGA for Gallus gallus chromosome) for BW04 and one on GGAZ for six growth-related traits. Based on whole-genome sequence, differences between the parental lines based on several birds were screened in the genome-wide QTL regions and in a region detected by both methods, resulting in the identification of 106 putative candidate genes with a total of 15,443 SNPs, of which 41 were missense and 1698 were not described in the dbSNP archive. CONCLUSIONS The QTL detected in this study for growth and morphological traits likely influence adaptation of chickens to sub-tropical climate. Using whole-genome sequence data, we identified candidate SNPs for further confirmation of QTL in the F2 design. A strong QTL effect found on GGAZ underlines the importance of sex-linked inheritance for growth traits in chickens.
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Affiliation(s)
- Ching-Yi Lien
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France.,Department of Animal Science, National Chung Hsing University, 145 Xingda Rd., South District, Taichung, 40227, Taiwan.,Livestock Research Institute, Council of Agriculture, Executive Yuan, 112 Muchang, Xinhua District, Tainan, 71246, Taiwan
| | | | - Shih-Wen Wu
- Fonghuanggu Bird and Ecology Park, National Museum of Natural Science, 1-9 Renyi Rd., Lugu Township, Nantou County, 55841, Taiwan
| | - Woei-Fuh Wang
- Biodiversity Research Center, Academia Sinica, 128 Academia Rd., Section 2, Nankang, Taipei, 11529, Taiwan
| | - Chen Siang Ng
- Institute of Molecular and Cellular Biology, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Rd., Hsinchu, 30013, Taiwan
| | - Chih-Feng Chen
- Department of Animal Science, National Chung Hsing University, 145 Xingda Rd., South District, Taichung, 40227, Taiwan. .,Center for the Integrative and Evolutionary Galliformes Genomics, National Chung Hsing University, No. 250, Guoguang Rd., South District, Taichung, 40227, Taiwan.
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Dharmayanthi AB, Terai Y, Sulandari S, Zein MSA, Akiyama T, Satta Y. The origin and evolution of fibromelanosis in domesticated chickens: Genomic comparison of Indonesian Cemani and Chinese Silkie breeds. PLoS One 2017; 12:e0173147. [PMID: 28379963 PMCID: PMC5381777 DOI: 10.1371/journal.pone.0173147] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 02/15/2017] [Indexed: 12/30/2022] Open
Abstract
Like Chinese Silkie, Indonesian Ayam Cemani exhibits fibromelanosis or dermal hyperpigmentation and possesses complex segmental duplications on chromosome 20 that involve the endothelin 3 gene, EDN3. A genomic region, DR1 of 127 kb, together with another region, DR2 of 171 kb, was duplicated by unequal crossing over, accompanied by inversion of one DR2. Quantitative PCR and copy number variation analyses on the Cemani genome sequence confirmed the duplication of EDN3. These genetic arrangements are identical in Cemani and Silkie, indicating a single origin of the genetic cause of Fm. The two DR1s harbor two distinct EDN3 haplotypes in a form of permanent heterozygosity, although they remain allelic in the ancestral Red Jungle Fowl population and some domesticated chicken breeds, with their allelic divergence time being as recent as 0.3 million years ago. In Cemani and Silkie breeds, artificial selection favoring the Fm phenotype has left an unambiguous record for selective sweep that extends in both directions from tandemly duplicated EDN3 loci. This highly homozygous tract is different in length between Cemani and Silkie, reflecting their distinct breeding histories. It is estimated that the Fm phenotype came into existence at least 6600-9100 years ago, prior to domestication of Cemani and Silkie, and that throughout domestication there has been intense artificial selection with strength s > 50% in each breed.
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Affiliation(s)
- Anik Budhi Dharmayanthi
- Department of Evolutionary Studies of Biosystems, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa, Japan
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Indonesia
| | - Yohei Terai
- Department of Evolutionary Studies of Biosystems, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa, Japan
| | - Sri Sulandari
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Indonesia
| | - M. Syamsul Arifin Zein
- Museum Zoologicum Bogoriense, Research Center for Biology, Indonesian Institute of Science (LIPI), Cibinong, Indonesia
| | | | - Yoko Satta
- Department of Evolutionary Studies of Biosystems, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa, Japan
- * E-mail:
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Goto T, Tsudzuki M. Genetic Mapping of Quantitative Trait Loci for Egg Production and Egg Quality Traits in Chickens: a Review. J Poult Sci 2017; 54:1-12. [PMID: 32908402 PMCID: PMC7477176 DOI: 10.2141/jpsa.0160121] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 10/24/2016] [Indexed: 12/11/2022] Open
Abstract
Chickens display a wide spectrum of phenotypic variations in quantitative traits such as egg-related traits. Quantitative trait locus (QTL) analysis is a statistical method used to understand the relationship between phenotypic (trait measurements) and genotypic data (molecular markers). We have performed QTL analyses for egg-related traits using an original resource population based on the Japanese Large Game (Oh-Shamo) and the White Leghorn breeds of chickens. In this article, we summarize the results of our extensive QTL analyses for 11 and 66 traits for egg production and egg quality, respectively. We reveal that at least 30 QTL regions on 17 different chromosomes affect phenotypic variation in egg-related traits. Each locus had an age-specific effect on traits, and a variety in effects was also apparent, such as additive, dominance, and epistatic-interaction effects. Although genome-wide association study (GWAS) is suitable for gene-level resolution mapping of GWAS loci with additive effects, QTL mapping studies enable us to comprehensively understand genetic control, such as chromosomal regions, genetic contribution to phenotypic variance, mode of inheritance, and age-specificity of both common and rare alleles. QTL analyses also describe the relationship between genotypes and phenotypes in experimental populations. Accumulation of QTL information, including GWAS loci, is also useful for studies of population genomics approached without phenotypic data in order to validate the identified genomic signatures of positive selection. The combination of QTL studies and next-generation sequencing techniques with uncharacterized genetic resources will enhance current understanding of the relationship between genotypes and phenotypes in livestock animals.
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Affiliation(s)
- Tatsuhiko Goto
- Genetics, Ecology and Evolution, School of Life Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8528, Japan
- Present address: Department of Life Science and Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Inada-cho, Obihiro, Hokkaido 080-8555, Japan
| | - Masaoki Tsudzuki
- Japanese Avian Bioresource Project Research Center, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8528, Japan
- Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8528, Japan
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Abstract
In the majority of vertebrates, survival of offspring to sexual maturation is important for increasing population size, and parental investment in the young is important for reproductive success. Consequently, parental care is critical for the survival of offspring in many species, and many vertebrates have adapted this behavior to their social and ecological environments. Parental care is defined as any behavior that is performed in association with one's offspring (Rosenblatt, Mayer, Siegel. Maternal behavior among nonprimate mammals. In: Adler, Pfaff, Goy, editors. Handbook of behavioral neurobiology. New York: Plenum; 1985. p. 229-98) and is well characterized in mammals and birds. In birds (class Aves), this is due to the high level of diversity across species. Parental behavior in birds protects the young from intruders, and generally involves nest building, incubation, and broody behavior which protect their young from an intruder, and the offspring are reared to independence. Broodiness is complexly regulated by the central nervous system and is associated with multiple hormones and neurotransmitters produced by the hypothalamus and pituitary gland. The mechanism of this behavior has been extensively characterized in domestic chicken (Gallus domesticus), turkey (Meleagris gallopavo), and pigeons and doves (family Columbidae). This chapter summarizes broodiness in birds from a physiology, genetics, and molecular biology perspective.
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Affiliation(s)
- Takeshi Ohkubo
- College of Agriculture, Ibaraki University, Ibaraki, Japan.
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Jiang J, Gao Y, Hou Y, Li W, Zhang S, Zhang Q, Sun D. Whole-Genome Resequencing of Holstein Bulls for Indel Discovery and Identification of Genes Associated with Milk Composition Traits in Dairy Cattle. PLoS One 2016; 11:e0168946. [PMID: 28030618 PMCID: PMC5193355 DOI: 10.1371/journal.pone.0168946] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 12/08/2016] [Indexed: 11/18/2022] Open
Abstract
The use of whole-genome resequencing to obtain more information on genetic variation could produce a range of benefits for the dairy cattle industry, especially with regard to increasing milk production and improving milk composition. In this study, we sequenced the genomes of eight Holstein bulls from four half- or full-sib families, with high and low estimated breeding values (EBVs) of milk protein percentage and fat percentage at an average effective depth of 10×, using Illumina sequencing. Over 0.9 million nonredundant short insertions and deletions (indels) [1–49 base pairs (bp)] were obtained. Among them, 3,625 indels that were polymorphic between the high and low groups of bulls were revealed and subjected to further analysis. The vast majority (76.67%) of these indels were novel. Follow-up validation assays confirmed that most (70%) of the randomly selected indels represented true variations. The indels that were polymorphic between the two groups were annotated based on the cattle genome sequence assembly (UMD3.1.69); as a result, nearly 1,137 of them were found to be located within 767 annotated genes, only 5 (0.138%) of which were located in exons. Then, by integrated analysis of the 767 genes with known quantitative trait loci (QTL); significant single-nucleotide polymorphisms (SNPs) previously identified by genome-wide association studies (GWASs) to be associated with bovine milk protein and fat traits; and the well-known pathways involved in protein, fat synthesis, and metabolism, we identified a total of 11 promising candidate genes potentially affecting milk composition traits. These were FCGR2B, CENPE, RETSAT, ACSBG2, NFKB2, TBC1D1, NLK, MAP3K1, SLC30A2, ANGPT1 and UGDH. Our findings provide a basis for further study and reveal key genes for milk composition traits in dairy cattle.
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Affiliation(s)
- Jianping Jiang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
| | - Yahui Gao
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
| | - Yali Hou
- Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Wenhui Li
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
| | - Shengli Zhang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
| | - Qin Zhang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
| | - Dongxiao Sun
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China
- * E-mail:
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36
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Liu Z, Ji Z, Wang G, Chao T, Hou L, Wang J. Genome-wide analysis reveals signatures of selection for important traits in domestic sheep from different ecoregions. BMC Genomics 2016; 17:863. [PMID: 27809776 PMCID: PMC5094087 DOI: 10.1186/s12864-016-3212-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2016] [Accepted: 10/25/2016] [Indexed: 12/22/2022] Open
Abstract
Background Throughout a long period of adaptation and selection, sheep have thrived in a diverse range of ecological environments. Mongolian sheep is the common ancestor of the Chinese short fat-tailed sheep. Migration to different ecoregions leads to changes in selection pressures and results in microevolution. Mongolian sheep and its subspecies differ in a number of important traits, especially reproductive traits. Genome-wide intraspecific variation is required to dissect the genetic basis of these traits. Results This research resequenced 3 short fat-tailed sheep breeds with a 43.2-fold coverage of the sheep genome. We report more than 17 million single nucleotide polymorphisms and 2.9 million indels and identify 143 genomic regions with reduced pooled heterozygosity or increased genetic distance to each other breed that represent likely targets for selection during the migration. These regions harbor genes related to developmental processes, cellular processes, multicellular organismal processes, biological regulation, metabolic processes, reproduction, localization, growth and various components of the stress responses. Furthermore, we examined the haplotype diversity of 3 genomic regions involved in reproduction and found significant differences in TSHR and PRL gene regions among 8 sheep breeds. Conclusions Our results provide useful genomic information for identifying genes or causal mutations associated with important economic traits in sheep and for understanding the genetic basis of adaptation to different ecological environments. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3212-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhaohua Liu
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian, Shandong, 271018, China
| | - Zhibin Ji
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian, Shandong, 271018, China
| | - Guizhi Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian, Shandong, 271018, China
| | - Tianle Chao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian, Shandong, 271018, China
| | - Lei Hou
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian, Shandong, 271018, China
| | - Jianmin Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Technology, Shandong Agricultural University, Taian, Shandong, 271018, China.
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37
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Wang MS, Huo YX, Li Y, Otecko NO, Su LY, Xu HB, Wu SF, Peng MS, Liu HQ, Zeng L, Irwin DM, Yao YG, Wu DD, Zhang YP. Comparative population genomics reveals genetic basis underlying body size of domestic chickens. J Mol Cell Biol 2016; 8:542-552. [PMID: 27744377 DOI: 10.1093/jmcb/mjw044] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2016] [Revised: 08/16/2016] [Accepted: 10/14/2016] [Indexed: 12/30/2022] Open
Abstract
Body size is the most important economic trait for animal production and breeding. Several hundreds of loci have been reported to be associated with growth trait and body weight in chickens. The loci are mapped to large genomic regions due to the low density and limited number of genetic markers in previous studies. Herein, we employed comparative population genomics to identify genetic basis underlying the small body size of Yuanbao chicken (a famous ornamental chicken) based on 89 whole genomes. The most significant signal was mapped to the BMP10 gene, whose expression was upregulated in the Yuanbao chicken. Overexpression of BMP10 induced a significant decrease in body length by inhibiting angiogenic vessel development in zebrafish. In addition, three other loci on chromosomes 1, 2, and 24 were also identified to be potentially involved in the development of body size. Our results provide a paradigm shift in identification of novel loci controlling body size variation, availing a fast and efficient strategy. These loci, particularly BMP10, add insights into ongoing research of the evolution of body size under artificial selection and have important implications for future chicken breeding.
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Affiliation(s)
- Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Yong-Xia Huo
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- College of Life Science, Anhui University, Hefei 230601, China
| | - Yan Li
- Laboratory for Conservation and Utilization of Bio-Resource, Yunnan University, Kunming 650091, China
| | - Newton O Otecko
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Ling-Yan Su
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming 650223, China
| | - Hai-Bo Xu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Shi-Fang Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - He-Qun Liu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Lin Zeng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - David M Irwin
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
- Banting and Best Diabetes Centre, University of Toronto, Toronto, Ontario M5G 2C4, Canada
| | - Yong-Gang Yao
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming 650223, China
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650223, China
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming 650204, China
- Laboratory for Conservation and Utilization of Bio-Resource, Yunnan University, Kunming 650091, China
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38
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Fleming DS, Koltes JE, Markey AD, Schmidt CJ, Ashwell CM, Rothschild MF, Persia ME, Reecy JM, Lamont SJ. Genomic analysis of Ugandan and Rwandan chicken ecotypes using a 600 k genotyping array. BMC Genomics 2016; 17:407. [PMID: 27230772 PMCID: PMC4882793 DOI: 10.1186/s12864-016-2711-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Accepted: 05/06/2016] [Indexed: 02/07/2023] Open
Abstract
Background Indigenous populations of animals have developed unique adaptations to their local environments, which may include factors such as response to thermal stress, drought, pathogens and suboptimal nutrition. The survival and subsequent evolution within these local environments can be the result of both natural and artificial selection driving the acquisition of favorable traits, which over time leave genomic signatures in a population. This study’s goals are to characterize genomic diversity and identify selection signatures in chickens from equatorial Africa to identify genomic regions that may confer adaptive advantages of these ecotypes to their environments. Results Indigenous chickens from Uganda (n = 72) and Rwanda (n = 100), plus Kuroilers (n = 24, an Indian breed imported to Africa), were genotyped using the Axiom® 600 k Chicken Genotyping Array. Indigenous ecotypes were defined based upon location of sampling within Africa. The results revealed the presence of admixture among the Ugandan, Rwandan, and Kuroiler populations. Genes within runs of homozygosity consensus regions are linked to gene ontology (GO) terms related to lipid metabolism, immune functions and stress-mediated responses (FDR < 0.15). The genes within regions of signatures of selection are enriched for GO terms related to health and oxidative stress processes. Key genes in these regions had anti-oxidant, apoptosis, and inflammation functions. Conclusions The study suggests that these populations have alleles under selective pressure from their environment, which may aid in adaptation to harsh environments. The correspondence in gene ontology terms connected to stress-mediated processes across the populations could be related to the similarity of environments or an artifact of the detected admixture. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2711-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
| | - J E Koltes
- Iowa State University, Ames, IA, USA.,University of Arkansas, Fayetteville, AR, USA
| | | | | | - C M Ashwell
- North Carolina State University, Raleigh, NC, USA
| | | | - M E Persia
- Virginia Polytechnic University, Blacksburg, VA, USA
| | - J M Reecy
- Iowa State University, Ames, IA, USA
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Wang MS, Zhang RW, Su LY, Li Y, Peng MS, Liu HQ, Zeng L, Irwin DM, Du JL, Yao YG, Wu DD, Zhang YP. Positive selection rather than relaxation of functional constraint drives the evolution of vision during chicken domestication. Cell Res 2016; 26:556-73. [PMID: 27033669 PMCID: PMC4856766 DOI: 10.1038/cr.2016.44] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 01/18/2016] [Accepted: 02/22/2016] [Indexed: 01/05/2023] Open
Abstract
As noted by Darwin, chickens have the greatest phenotypic diversity of all birds, but an interesting evolutionary difference between domestic chickens and their wild ancestor, the Red Junglefowl, is their comparatively weaker vision. Existing theories suggest that diminished visual prowess among domestic chickens reflect changes driven by the relaxation of functional constraints on vision, but the evidence identifying the underlying genetic mechanisms responsible for this change has not been definitively characterized. Here, a genome-wide analysis of the domestic chicken and Red Junglefowl genomes showed significant enrichment for positively selected genes involved in the development of vision. There were significant differences between domestic chickens and their wild ancestors regarding the level of mRNA expression for these genes in the retina. Numerous additional genes involved in the development of vision also showed significant differences in mRNA expression between domestic chickens and their wild ancestors, particularly for genes associated with phototransduction and photoreceptor development, such as RHO (rhodopsin), GUCA1A, PDE6B and NR2E3. Finally, we characterized the potential role of the VIT gene in vision, which experienced positive selection and downregulated expression in the retina of the village chicken. Overall, our results suggest that positive selection, rather than relaxation of purifying selection, contributed to the evolution of vision in domestic chickens. The progenitors of domestic chickens harboring weaker vision may have showed a reduced fear response and vigilance, making them easier to be unconsciously selected and/or domesticated.
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Affiliation(s)
- Ming-Shan Wang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Rong-wei Zhang
- Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ling-Yan Su
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Yan Li
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Min-Sheng Peng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - He-Qun Liu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Lin Zeng
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - David M Irwin
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Banting and Best Diabetes Centre, University of Toronto, Toronto, Canada
| | - Jiu-Lin Du
- Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yong-Gang Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
| | - Dong-Dong Wu
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Kunming, Yunnan 650223, China
| | - Ya-Ping Zhang
- State Key Laboratory of Genetic Resources and Evolution, Yunnan Laboratory of Molecular Biology of Domestic Animals
- Kunming College of Life Science, Unisversity of Chinese Academy of Sciences, Kunming, Yunnan 650204, China
- Laboratory for Conservation and Utilization of Bio-resource, Yunnan University, Kunming, Yunnan 650091, China
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Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJM, Gianola D. Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens. Genet Sel Evol 2016; 48:10. [PMID: 26842494 PMCID: PMC4739338 DOI: 10.1186/s12711-016-0187-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 01/15/2016] [Indexed: 11/15/2022] Open
Abstract
Background Genome-wide association studies in humans have found enrichment of trait-associated single nucleotide polymorphisms (SNPs) in coding regions of the genome and depletion of these in intergenic regions. However, a recent release of the ENCyclopedia of DNA elements showed that ~80 % of the human genome has a biochemical function. Similar studies on the chicken genome are lacking, thus assessing the relative contribution of its genic and non-genic regions to variation is relevant for biological studies and genetic improvement of chicken populations. Methods A dataset including 1351 birds that were genotyped with the 600K Affymetrix platform was used. We partitioned SNPs according to genome annotation data into six classes to characterize the relative contribution of genic and non-genic regions to genetic variation as well as their predictive power using all available quality-filtered SNPs. Target traits were body weight, ultrasound measurement of breast muscle and hen house egg production in broiler chickens. Six genomic regions were considered: intergenic regions, introns, missense, synonymous, 5′ and 3′ untranslated regions, and regions that are located 5 kb upstream and downstream of coding genes. Genomic relationship matrices were constructed for each genomic region and fitted in the models, separately or simultaneously. Kernel-based ridge regression was used to estimate variance components and assess predictive ability. Contribution of each class of genomic regions to dominance variance was also considered. Results Variance component estimates indicated that all genomic regions contributed to marked additive genetic variation and that the class of synonymous regions tended to have the greatest contribution. The marked dominance genetic variation explained by each class of genomic regions was similar and negligible (~0.05). In terms of prediction mean-square error, the whole-genome approach showed the best predictive ability. Conclusions All genic and non-genic regions contributed to phenotypic variation for the three traits studied. Overall, the contribution of additive genetic variance to the total genetic variance was much greater than that of dominance variance. Our results show that all genomic regions are important for the prediction of the targeted traits, and the whole-genome approach was reaffirmed as the best tool for genome-enabled prediction of quantitative traits.
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Affiliation(s)
- Rostam Abdollahi-Arpanahi
- Department of Animal Sciences, University of Wisconsin, Madison, WI, USA. .,Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, Pakdasht, Iran.
| | - Gota Morota
- Department of Animal Science, University of Nebraska, Lincoln, NE, USA.
| | - Bruno D Valente
- Department of Animal Sciences, University of Wisconsin, Madison, WI, USA. .,Department of Dairy Science, University of Wisconsin, Madison, WI, USA.
| | - Andreas Kranis
- Aviagen Ltd, Midlothian, UK. .,The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, UK.
| | - Guilherme J M Rosa
- Department of Animal Sciences, University of Wisconsin, Madison, WI, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
| | - Daniel Gianola
- Department of Animal Sciences, University of Wisconsin, Madison, WI, USA. .,Department of Dairy Science, University of Wisconsin, Madison, WI, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
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Basheer A, Haley CS, Law A, Windsor D, Morrice D, Talbot R, Wilson PW, Sharp PJ, Dunn IC. Genetic loci inherited from hens lacking maternal behaviour both inhibit and paradoxically promote this behaviour. Genet Sel Evol 2015; 47:100. [PMID: 26718134 PMCID: PMC4697313 DOI: 10.1186/s12711-015-0180-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2015] [Accepted: 12/17/2015] [Indexed: 01/18/2023] Open
Abstract
Background A major step towards the success of chickens as a domesticated species was the separation between maternal care and reproduction. Artificial incubation replaced the natural maternal behaviour of incubation and, thus, in certain breeds, it became possible to breed chickens with persistent egg production and no incubation behaviour; a typical example is the White Leghorn strain. Conversely, some strains, such as the Silkie breed, are prized for their maternal behaviour and their willingness to incubate eggs. This is often colloquially known as broodiness. Results Using an F2 linkage mapping approach and a cross between White Leghorn and Silkie chicken breeds, we have mapped, for the first time, genetic loci that affect maternal behaviour on chromosomes 1, 5, 8, 13, 18 and 19 and linkage group E22C19W28. Paradoxically, heterozygous and White Leghorn homozygous genotypes were associated with an increased incidence of incubation behaviour, which exceeded that of the Silkie homozygotes for most loci. In such cases, it is likely that the loci involved are associated with increased egg production. Increased egg production increases the probability of incubation behaviour occurring because egg laying must precede incubation. For the loci on chromosomes 8 and 1, alleles from the Silkie breed promote incubation behaviour and influence maternal behaviour (these explain 12 and 26 % of the phenotypic difference between the two founder breeds, respectively). Conclusions The over-dominant locus on chromosome 5 coincides with the strongest selective sweep reported in chickens and together with the loci on chromosomes 1 and 8, they include genes of the thyrotrophic axis. This suggests that thyroid hormones may play a critical role in the loss of incubation behaviour and the improved egg laying behaviour of the White Leghorn breed. Our findings support the view that loss of maternal incubation behaviour in the White Leghorn breed is the result of selection for fertility and egg laying persistency and against maternal incubation behaviour.
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Affiliation(s)
- Atia Basheer
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK. .,Animal Breeding and Genetics Section, Department of Livestock Production, University of Veterinary and Animal Sciences, Ravi campus, Lahore, Pakistan.
| | - Chris S Haley
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK.
| | - Andy Law
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK.
| | - Dawn Windsor
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK.
| | - David Morrice
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK. .,Edinburgh Genomics, Ashworth Laboratories, The University of Edinburgh, Edinburgh, EH9 3JT, UK.
| | - Richard Talbot
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK. .,Edinburgh Genomics, Ashworth Laboratories, The University of Edinburgh, Edinburgh, EH9 3JT, UK.
| | - Peter W Wilson
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK.
| | - Peter J Sharp
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK.
| | - Ian C Dunn
- Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh Easter Bush, Midlothian, EH25 9RG, Scotland, UK.
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Yan Y, Yang N, Cheng HH, Song J, Qu L. Genome-wide identification of copy number variations between two chicken lines that differ in genetic resistance to Marek's disease. BMC Genomics 2015; 16:843. [PMID: 26492869 PMCID: PMC4619206 DOI: 10.1186/s12864-015-2080-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 10/13/2015] [Indexed: 11/10/2022] Open
Abstract
Background Copy number variation (CNV) is a major source of genome polymorphism that directly contributes to phenotypic variation such as resistance to infectious diseases. Lines 63 and 72 are two highly inbred experimental chicken lines that differ greatly in susceptibility to Marek’s disease (MD), and have been used extensively in efforts to identify the genetic and molecular basis for genetic resistance to MD. Using next generation sequencing, we present a genome-wide assessment of CNVs that are potentially associated with genetic resistance to MD. Methods Three chickens randomly selected from each line were sequenced to an average depth of 20×. Two popular software, CNVnator and Pindel, were used to call genomic CNVs separately. The results were combined to obtain a union set of genomic CNVs in the two chicken lines. Results A total of 5,680 CNV regions (CNVRs) were identified after merging the two datasets, of which 1,546 and 1,866 were specific to the MD resistant or susceptible line, respectively. Over half of the line-specific CNVRs were shared by 2 or more chickens, reflecting the reduced diversity in both inbred lines. The CNVRs fixed in the susceptible lines were significantly enriched in genes involved in MAPK signaling pathway. We also found 67 CNVRs overlapping with 62 genes previously shown to be strong candidates of the underlying genes responsible for the susceptibility to MD. Conclusions Our findings provide new insights into the genetic architecture of the two chicken lines and additional evidence that MAPK signaling pathway may play an important role in host response to MD virus infection. The rich source of line-specific CNVs is valuable for future disease-related association studies in the two chicken lines. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2080-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yiyuan Yan
- Department of Animal Genetics and Breeding, College of Animal Science, China Agricultural University, Beijing, 100193, China.
| | - Ning Yang
- Department of Animal Genetics and Breeding, College of Animal Science, China Agricultural University, Beijing, 100193, China.
| | - Hans H Cheng
- USDA, ARS, Avian Disease and Oncology Laboratory, East Lansing, MI, 48823, USA.
| | - Jiuzhou Song
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, 20742, USA.
| | - Lujiang Qu
- Department of Animal Genetics and Breeding, College of Animal Science, China Agricultural University, Beijing, 100193, China.
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Wragg D, Mason AS, Yu L, Kuo R, Lawal RA, Desta TT, Mwacharo JM, Cho CY, Kemp S, Burt DW, Hanotte O. Genome-wide analysis reveals the extent of EAV-HP integration in domestic chicken. BMC Genomics 2015; 16:784. [PMID: 26466991 PMCID: PMC4607243 DOI: 10.1186/s12864-015-1954-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 09/02/2015] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND EAV-HP is an ancient retrovirus pre-dating Gallus speciation, which continues to circulate in modern chicken populations, and led to the emergence of avian leukosis virus subgroup J causing significant economic losses to the poultry industry. We mapped EAV-HP integration sites in Ethiopian village chickens, a Silkie, Taiwan Country chicken, red junglefowl Gallus gallus and several inbred experimental lines using whole-genome sequence data. RESULTS An average of 75.22 ± 9.52 integration sites per bird were identified, which collectively group into 279 intervals of which 5 % are common to 90 % of the genomes analysed and are suggestive of pre-domestication integration events. More than a third of intervals are specific to individual genomes, supporting active circulation of EAV-HP in modern chickens. Interval density is correlated with chromosome length (P < 2.31(-6)), and 27 % of intervals are located within 5 kb of a transcript. Functional annotation clustering of genes reveals enrichment for immune-related functions (P < 0.05). CONCLUSIONS Our results illustrate a non-random distribution of EAV-HP in the genome, emphasising the importance it may have played in the adaptation of the species, and provide a platform from which to extend investigations on the co-evolutionary significance of endogenous retroviral genera with their hosts.
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Affiliation(s)
- David Wragg
- Ecology and Evolution, School of Life Sciences, University of Nottingham, University Park, Nottingham, UK.
- Institut National de la Recherche Agronomique (INRA), UMR 1338 GenPhySE, 31326, Castanet-Tolosan, France.
| | - Andrew S Mason
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, Edinburgh, UK.
| | - Le Yu
- GAIC Co. Ltd. Jing Chen Buiding, Science Park, South Street, Chao Yang District, Beijing, People's Republic Popular of China.
| | - Richard Kuo
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, Edinburgh, UK.
| | - Raman A Lawal
- Ecology and Evolution, School of Life Sciences, University of Nottingham, University Park, Nottingham, UK.
| | - Takele Taye Desta
- Ecology and Evolution, School of Life Sciences, University of Nottingham, University Park, Nottingham, UK.
| | - Joram M Mwacharo
- Ecology and Evolution, School of Life Sciences, University of Nottingham, University Park, Nottingham, UK.
- International Centre for Agricultural Research in Dry Areas, c/o International Livestock Research Institute, Addis Ababa, Ethiopia.
| | - Chang-Yeon Cho
- Animal Genetic Resources Station, National Institute of Animal Science, Namwon, Republic of Korea.
| | - Steve Kemp
- International Livestock Research Institute, Naivasha Road, P.O. Box 30709, Nairobi, Kenya.
| | - David W Burt
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, Edinburgh, UK.
| | - Olivier Hanotte
- Ecology and Evolution, School of Life Sciences, University of Nottingham, University Park, Nottingham, UK.
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Boschiero C, Gheyas AA, Ralph HK, Eory L, Paton B, Kuo R, Fulton J, Preisinger R, Kaiser P, Burt DW. Detection and characterization of small insertion and deletion genetic variants in modern layer chicken genomes. BMC Genomics 2015; 16:562. [PMID: 26227840 PMCID: PMC4563830 DOI: 10.1186/s12864-015-1711-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2014] [Accepted: 06/22/2015] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Small insertions and deletions (InDels) constitute the second most abundant class of genetic variants and have been found to be associated with many traits and diseases. The present study reports on the detection and characterisation of about 883 K high quality InDels from the whole-genome analysis of several modern layer chicken lines from diverse breeds. RESULTS To reduce the error rates seen in InDel detection, this study used the consensus set from two InDel-calling packages: SAMtools and Dindel, as well as stringent post-filtering criteria. By analysing sequence data from 163 chickens from 11 commercial and 5 experimental layer lines, this study detected about 883 K high quality consensus InDels with 93% validation rate and an average density of 0.78 InDels/kb over the genome. Certain chromosomes, viz, GGAZ, 16, 22 and 25 showed very low densities of InDels whereas the highest rate was observed on GGA6. In spite of the higher recombination rates on microchromosomes, the InDel density on these chromosomes was generally lower relative to macrochromosomes possibly due to their higher gene density. About 43-87% of the InDels were found to be fixed within each line. The majority of detected InDels (86%) were 1-5 bases and about 63% were non-repetitive in nature while the rest were tandem repeats of various motif types. Functional annotation identified 613 frameshift, 465 non-frameshift and 10 stop-gain/loss InDels. Apart from the frameshift and stopgain/loss InDels that are expected to affect the translation of protein sequences and their biological activity, 33% of the non-frameshift were predicted as evolutionary intolerant with potential impact on protein functions. Moreover, about 2.5% of the InDels coincided with the most-conserved elements previously mapped on the chicken genome and are likely to define functional elements. InDels potentially affecting protein function were found to be enriched for certain gene-classes e.g. those associated with cell proliferation, chromosome and Golgi organization, spermatogenesis, and muscle contraction. CONCLUSIONS The large catalogue of InDels presented in this study along with their associated information such as functional annotation, estimated allele frequency, etc. are expected to serve as a rich resource for application in future research and breeding in the chicken.
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Affiliation(s)
- Clarissa Boschiero
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK. .,Current Address: Departamento de Zootecnia, University of Sao Paulo/ESALQ, Piracicaba, SP, 13418-900, Brazil.
| | - Almas A Gheyas
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
| | - Hannah K Ralph
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
| | - Lel Eory
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
| | - Bob Paton
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
| | - Richard Kuo
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
| | | | | | - Pete Kaiser
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
| | - David W Burt
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK.
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Schmid M, Smith J, Burt DW, Aken BL, Antin PB, Archibald AL, Ashwell C, Blackshear PJ, Boschiero C, Brown CT, Burgess SC, Cheng HH, Chow W, Coble DJ, Cooksey A, Crooijmans RPMA, Damas J, Davis RVN, de Koning DJ, Delany ME, Derrien T, Desta TT, Dunn IC, Dunn M, Ellegren H, Eöry L, Erb I, Farré M, Fasold M, Fleming D, Flicek P, Fowler KE, Frésard L, Froman DP, Garceau V, Gardner PP, Gheyas AA, Griffin DK, Groenen MAM, Haaf T, Hanotte O, Hart A, Häsler J, Hedges SB, Hertel J, Howe K, Hubbard A, Hume DA, Kaiser P, Kedra D, Kemp SJ, Klopp C, Kniel KE, Kuo R, Lagarrigue S, Lamont SJ, Larkin DM, Lawal RA, Markland SM, McCarthy F, McCormack HA, McPherson MC, Motegi A, Muljo SA, Münsterberg A, Nag R, Nanda I, Neuberger M, Nitsche A, Notredame C, Noyes H, O'Connor R, O'Hare EA, Oler AJ, Ommeh SC, Pais H, Persia M, Pitel F, Preeyanon L, Prieto Barja P, Pritchett EM, Rhoads DD, Robinson CM, Romanov MN, Rothschild M, Roux PF, Schmidt CJ, Schneider AS, Schwartz MG, Searle SM, Skinner MA, Smith CA, Stadler PF, Steeves TE, Steinlein C, Sun L, Takata M, Ulitsky I, Wang Q, Wang Y, Warren WC, Wood JMD, Wragg D, Zhou H. Third Report on Chicken Genes and Chromosomes 2015. Cytogenet Genome Res 2015; 145:78-179. [PMID: 26282327 PMCID: PMC5120589 DOI: 10.1159/000430927] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Michael Schmid
- Department of Human Genetics, University of Würzburg, Würzburg, Germany
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Integrating transcriptome and genome re-sequencing data to identify key genes and mutations affecting chicken eggshell qualities. PLoS One 2015; 10:e0125890. [PMID: 25974068 PMCID: PMC4431873 DOI: 10.1371/journal.pone.0125890] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Accepted: 03/24/2015] [Indexed: 12/21/2022] Open
Abstract
Eggshell damages lead to economic losses in the egg production industry and are a threat to human health. We examined 49-wk-old Rhode Island White hens (Gallus gallus) that laid eggs having shells with significantly different strengths and thicknesses. We used HiSeq 2000 (Illumina) sequencing to characterize the chicken transcriptome and whole genome to identify the key genes and genetic mutations associated with eggshell calcification. We identified a total of 14,234 genes expressed in the chicken uterus, representing 89% of all annotated chicken genes. A total of 889 differentially expressed genes were identified by comparing low eggshell strength (LES) and normal eggshell strength (NES) genomes. The DEGs are enriched in calcification-related processes, including calcium ion transport and calcium signaling pathways as reveled by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Some important matrix proteins, such as OC-116, LTF and SPP1, were also expressed differentially between two groups. A total of 3,671,919 single-nucleotide polymorphisms (SNPs) and 508,035 Indels were detected in protein coding genes by whole-genome re-sequencing, including 1775 non-synonymous variations and 19 frame-shift Indels in DEGs. SNPs and Indels found in this study could be further investigated for eggshell traits. This is the first report to integrate the transcriptome and genome re-sequencing to target the genetic variations which decreased the eggshell qualities. These findings further advance our understanding of eggshell calcification in the chicken uterus.
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Xing K, Zhu F, Zhai L, Liu H, Wang Y, Wang Z, Chen S, Hou Z, Wang C. Integration of transcriptome and whole genomic resequencing data to identify key genes affecting swine fat deposition. PLoS One 2015; 10:e0122396. [PMID: 25849573 PMCID: PMC4388518 DOI: 10.1371/journal.pone.0122396] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2014] [Accepted: 02/21/2015] [Indexed: 12/17/2022] Open
Abstract
Fat deposition is highly correlated with the growth, meat quality, reproductive performance and immunity of pigs. Fatty acid synthesis takes place mainly in the adipose tissue of pigs; therefore, in this study, a high-throughput massively parallel sequencing approach was used to generate adipose tissue transcriptomes from two groups of Songliao black pigs that had opposite backfat thickness phenotypes. The total number of paired-end reads produced for each sample was in the range of 39.29-49.36 millions. Approximately 188 genes were differentially expressed in adipose tissue and were enriched for metabolic processes, such as fatty acid biosynthesis, lipid synthesis, metabolism of fatty acids, etinol, caffeine and arachidonic acid and immunity. Additionally, many genetic variations were detected between the two groups through pooled whole-genome resequencing. Integration of transcriptome and whole-genome resequencing data revealed important genomic variations among the differentially expressed genes for fat deposition, for example, the lipogenic genes. Further studies are required to investigate the roles of candidate genes in fat deposition to improve pig breeding programs.
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Affiliation(s)
- Kai Xing
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, Department of Animal Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Feng Zhu
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, Department of Animal Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Liwei Zhai
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, Department of Animal Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Huijie Liu
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, Department of Animal Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Yuan Wang
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, Department of Animal Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Zhijun Wang
- Tianjin Ninghe primary pig breeding farm, Ninghe, 301500, Tianjin, China
| | - Shaokang Chen
- Animal husbandry and veterinary station of Beijing, Beijing, 100125, Beijing, China
| | - Zhuocheng Hou
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, Department of Animal Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Chuduan Wang
- National Engineering Laboratory for Animal Breeding and MOA Key Laboratory of Animal Genetics and Breeding, Department of Animal Genetics and Breeding, China Agricultural University, Beijing, 100193, China
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48
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Wang MS, Li Y, Peng MS, Zhong L, Wang ZJ, Li QY, Tu XL, Dong Y, Zhu CL, Wang L, Yang MM, Wu SF, Miao YW, Liu JP, Irwin DM, Wang W, Wu DD, Zhang YP. Genomic Analyses Reveal Potential Independent Adaptation to High Altitude in Tibetan Chickens. Mol Biol Evol 2015; 32:1880-9. [DOI: 10.1093/molbev/msv071] [Citation(s) in RCA: 125] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
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49
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Yi G, Qu L, Chen S, Xu G, Yang N. Genome-wide copy number profiling using high-density SNP array in chickens. Anim Genet 2015; 46:148-57. [PMID: 25662183 DOI: 10.1111/age.12267] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/03/2014] [Indexed: 01/04/2023]
Abstract
Phenotypic diversity is a direct consequence resulting mainly from the impact of underlying genetic variation, and recent studies have shown that copy number variation (CNV) is emerging as an important contributor to both phenotypic variability and disease susceptibility. Herein, we performed a genome-wide CNV scan in 96 chickens from 12 diversified breeds, benefiting from the high-density Affymetrix 600 K SNP arrays. We identified a total of 231 autosomal CNV regions (CNVRs) encompassing 5.41 Mb of the chicken genome and corresponding to 0.59% of the autosomal sequence. The length of these CNVRs ranged from 2.6 to 586.2 kb with an average of 23.4 kb, including 130 gain, 93 loss and eight both gain and loss events. These CNVRs, especially deletions, had lower GC content and were located particularly in gene deserts. In particular, 102 CNVRs harbored 128 chicken genes, most of which were enriched in immune responses. We obtained 221 autosomal CNVRs after converting probe coordinates to Galgal3, and comparative analysis with previous studies illustrated that 153 of these CNVRs were regarded as novel events. Furthermore, qPCR assays were designed for 11 novel CNVRs, and eight (72.73%) were validated successfully. In this study, we demonstrated that the high-density 600 K SNP array can capture CNVs with higher efficiency and accuracy and highlighted the necessity of integrating multiple technologies and algorithms. Our findings provide a pioneering exploration of chicken CNVs based on a high-density SNP array, which contributes to a more comprehensive understanding of genetic variation in the chicken genome and is beneficial to unearthing potential CNVs underlying important traits of chickens.
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Affiliation(s)
- G Yi
- 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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50
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Stainton JJ, Haley CS, Charlesworth B, Kranis A, Watson K, Wiener P. Detecting signatures of selection in nine distinct lines of broiler chickens. Anim Genet 2014; 46:37-49. [PMID: 25515710 DOI: 10.1111/age.12252] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2014] [Indexed: 01/26/2023]
Abstract
Modern commercial chickens have been bred for one of two specific purposes: meat production (broilers) or egg production (layers). This has led to large phenotypic changes, so that the genomic signatures of selection may be detectable using statistical techniques. Genetic differentiation between nine distinct broiler lines was calculated using Weir and Cockerham's pairwise FST estimator for 11 003 genome-wide markers to identify regions showing evidence of differential selection across lines. Differentiation measures were averaged into overlapping sliding windows for each line, and a permutation approach was used to determine the significance of each window. A total of 51 regions were found to show significant differentiation between the lines. Several lines were consistently found to share significant regions, suggesting that the pattern of line divergence is related to selection for broiler traits. The majority of the 51 regions contain QTL relating to broiler traits, but only five of them were found to be significantly enriched for broiler QTL, including a region on chromosome 27 containing 39 broiler QTL and 114 genes. Additionally, a number of these regions have been identified by other selection mapping studies. This study has identified a large number of potential selection signatures, and further tests with higher-density marker data may narrow these regions down to individual genes.
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Affiliation(s)
- John J Stainton
- The Roslin Institute and R(D)SVS, University of Edinburgh, Midlothian, EH25 9RG, UK
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