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Salvatore G, Chibani Bahi Amar A, Canale-Tabet K, Fridi R, Tabet Aoul N, Saci S, Labarthe E, Palombo V, D'Andrea M, Vignal A, Faux P. Natural clines and human management impact the genetic structure of Algerian honey bee populations. Genet Sel Evol 2023; 55:94. [PMID: 38114899 PMCID: PMC10729559 DOI: 10.1186/s12711-023-00864-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 12/04/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The Algerian honey bee population is composed of two described subspecies A. m. intermissa and A. m. sahariensis, of which little is known regarding population genomics, both in terms of genetic differentiation and of possible contamination by exogenous stock. Moreover, the phenotypic differences between the two subspecies are expected to translate into genetic differences and possible adaptation to heat and drought in A. m. sahariensis. To shed light on the structure of this population and to integrate these two subspecies in the growing dataset of available haploid drone sequences, we performed whole-genome sequencing of 151 haploid drones. RESULTS Integrated analysis of our drone sequences with a similar dataset of European reference populations did not detect any significant admixture in the Algerian honey bees. Interestingly, most of the genetic variation was not found between the A. m. intermissa and A. m. sahariensis subspecies; instead, two main genetic clusters were found along an East-West axis. We found that the correlation between genetic and geographic distances was higher in the Western cluster and that close-family relationships were mostly detected in the Eastern cluster, sometimes at long distances. In addition, we selected a panel of 96 ancestry-informative markers to decide whether a sampled bee is Algerian or not, and tested this panel in simulated cases of admixture. CONCLUSIONS The differences between the two main genetic clusters suggest differential breeding management between eastern and western Algeria, with greater exchange of genetic material over long distances in the east. The lack of detected admixture events suggests that, unlike what is seen in many places worldwide, imports of queens from foreign countries do not seem to have occurred on a large scale in Algeria, a finding that is relevant for conservation purposes. In addition, the proposed panel of 96 markers was found effective to distinguish Algerian from European honey bees. Therefore, we conclude that applying this approach to other taxa is promising, in particular when genetic differentiation is difficult to capture.
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Affiliation(s)
- Giovanna Salvatore
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Via De Sanctis Snc, 86100, Campobasso, Italy.
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France.
| | - Amira Chibani Bahi Amar
- Laboratoire de Génétique Moléculaire et Cellulaire (LGMC), Département de Génétique Moléculaire Appliquée, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, USTOMB, BP 1505, El M'naouer, 31000, Oran, Algeria
| | - Kamila Canale-Tabet
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France
| | - Riad Fridi
- Laboratoire de Génétique Moléculaire et Cellulaire (LGMC), Département de Génétique Moléculaire Appliquée, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, USTOMB, BP 1505, El M'naouer, 31000, Oran, Algeria
| | - Nacera Tabet Aoul
- Laboratoire de Génétique Moléculaire et Cellulaire (LGMC), Département de Génétique Moléculaire Appliquée, Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf, USTOMB, BP 1505, El M'naouer, 31000, Oran, Algeria
- Department of Biotechnology, Faculty SNV, University of Oran1 Ahmed Ben Bella, Oran, Algeria
| | - Soumia Saci
- National Institute of Agronomic Research of Algeria (INRAA), El Harrach, Alger, Algeria
| | - Emmanuelle Labarthe
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France
| | - Valentino Palombo
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Via De Sanctis Snc, 86100, Campobasso, Italy
| | - Mariasilvia D'Andrea
- Department of Agricultural, Environmental and Food Sciences, University of Molise, Via De Sanctis Snc, 86100, Campobasso, Italy
| | - Alain Vignal
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France
| | - Pierre Faux
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet-Tolosan, France.
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Parejo M, Talenti A, Richardson M, Vignal A, Barnett M, Wragg D. AmelHap: Leveraging drone whole-genome sequence data to create a honey bee HapMap. Sci Data 2023; 10:198. [PMID: 37037860 PMCID: PMC10086014 DOI: 10.1038/s41597-023-02097-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/22/2023] [Indexed: 04/12/2023] Open
Abstract
Honey bee, Apis mellifera, drones are typically haploid, developing from an unfertilized egg, inheriting only their queen's alleles and none from the many drones she mated with. Thus the ordered combination or 'phase' of alleles is known, making drones a valuable haplotype resource. We collated whole-genome sequence data for 1,407 drones, including 45 newly sequenced Scottish drones, collectively representing 19 countries, 8 subspecies and various hybrids. Following alignment to Amel_HAv3.1, variant calling and quality filtering, we retained 17.4 M high quality variants across 1,328 samples with a genotyping rate of 98.7%. We demonstrate the utility of this haplotype resource, AmelHap, for genotype imputation, returning >95% concordance when up to 61% of data is missing in haploids and up to 12% of data is missing in diploids. AmelHap will serve as a useful resource for the community for imputation from low-depth sequencing or SNP chip data, accurate phasing of diploids for association studies, and as a comprehensive reference panel for population genetic and evolutionary analyses.
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Affiliation(s)
- M Parejo
- Applied Genomics and Bioinformatics, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - A Talenti
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, UK
| | - M Richardson
- University of Edinburgh, King's Buildings Campus, Edinburgh, UK
- Beebytes Analytics CIC, Roslin Innovation Centre, Easter Bush Campus, Midlothian, UK
| | - A Vignal
- GenPhySE, Université de Toulouse, INRAE, INPT, INP-ENVT, 31326, Castanet Tolosan, France
| | - M Barnett
- Beebytes Analytics CIC, Roslin Innovation Centre, Easter Bush Campus, Midlothian, UK
| | - D Wragg
- The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, UK.
- Beebytes Analytics CIC, Roslin Innovation Centre, Easter Bush Campus, Midlothian, UK.
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3
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Gmel AI, Guichard M, Dainat B, Williams GR, Eynard S, Vignal A, Servin B, Neuditschko M. Identification of runs of homozygosity in Western honey bees ( Apis mellifera) using whole-genome sequencing data. Ecol Evol 2023; 13:e9723. [PMID: 36694553 PMCID: PMC9843643 DOI: 10.1002/ece3.9723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 01/19/2023] Open
Abstract
Runs of homozygosity (ROH) are continuous homozygous segments that arise through the transmission of haplotypes that are identical by descent. The length and distribution of ROH segments provide insights into the genetic diversity of populations and can be associated with selection signatures. Here, we analyzed reconstructed whole-genome queen genotypes, from a pool-seq data experiment including 265 Western honeybee colonies from Apis mellifera mellifera and Apis mellifera carnica. Integrating individual ROH patterns and admixture levels in a dynamic population network visualization allowed us to ascertain major differences between the two subspecies. Within A. m. mellifera, we identified well-defined substructures according to the genetic origin of the queens. Despite the current applied conservation efforts, we pinpointed 79 admixed queens. Genomic inbreeding (F ROH) strongly varied within and between the identified subpopulations. Conserved A. m. mellifera from Switzerland had the highest mean F ROH (3.39%), while queens originating from a conservation area in France, which were also highly admixed, showed significantly lower F ROH (0.45%). The majority of A. m. carnica queens were also highly admixed, except 12 purebred queens with a mean F ROH of 2.33%. Within the breed-specific ROH islands, we identified 14 coding genes for A. m. mellifera and five for A. m. carnica, respectively. Local adaption of A. m. mellifera could be suggested by the identification of genes involved in the response to ultraviolet light (Crh-BP, Uvop) and body size (Hex70a, Hex70b), while the A. m. carnica specific genes Cpr3 and Cpr4 are most likely associated with the lighter striping pattern, a morphological phenotype expected in this subspecies. We demonstrated that queen genotypes derived from pooled workers are useful tool to unravel the population dynamics in A. mellifera and provide fundamental information to conserve native honey bees.
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Affiliation(s)
- Annik Imogen Gmel
- Animal GenoPhenomics, Animal Production Systems and Animal HealthAgroscopePosieuxSwitzerland
| | - Matthieu Guichard
- Animal GenoPhenomics, Animal Production Systems and Animal HealthAgroscopePosieuxSwitzerland
- Swiss Bee Research CentreAgroscopeLiebefeldSwitzerland
| | | | | | - Sonia Eynard
- GenPhySEINRAE, INPT, INPENVTUniversité de ToulouseCastanet‐TolosanFrance
- UMT PrADEProtection des Abeilles Dans L'EnvironnementAvignonFrance
| | - Alain Vignal
- GenPhySEINRAE, INPT, INPENVTUniversité de ToulouseCastanet‐TolosanFrance
- UMT PrADEProtection des Abeilles Dans L'EnvironnementAvignonFrance
| | - Bertrand Servin
- GenPhySEINRAE, INPT, INPENVTUniversité de ToulouseCastanet‐TolosanFrance
- UMT PrADEProtection des Abeilles Dans L'EnvironnementAvignonFrance
| | | | - Markus Neuditschko
- Animal GenoPhenomics, Animal Production Systems and Animal HealthAgroscopePosieuxSwitzerland
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Eynard SE, Vignal A, Basso B, Canale‐Tabet K, Le Conte Y, Decourtye A, Genestout L, Labarthe E, Mondet F, Servin B. Reconstructing queen genotypes by pool sequencing colonies in eusocial insects: Statistical Methods and their application to honeybee. Mol Ecol Resour 2022; 22:3035-3048. [PMID: 35816386 PMCID: PMC9796407 DOI: 10.1111/1755-0998.13685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 06/29/2022] [Accepted: 07/04/2022] [Indexed: 01/01/2023]
Abstract
Eusocial insects are crucial to many ecosystems, and particularly the honeybee (Apis mellifera). One approach to facilitate their study in molecular genetics, is to consider whole-colony genotyping by combining DNA of multiple individuals in a single pool sequencing experiment. Cheap and fast, this technique comes with the drawback of producing data requiring dedicated methods to be fully exploited. Despite this limitation, pool sequencing data have been shown to be informative and cost-effective when working on random mating populations. Here, we present new statistical methods for exploiting pool sequencing of eusocial colonies in order to reconstruct the genotypes of the queen of such colony. This leverages the possibility to monitor genetic diversity, perform genomic-based studies or implement selective breeding. Using simulations and honeybee real data, we show that the new methods allow for a fast and accurate estimation of the queen's genetic ancestry, with correlations of about 0.9 to that obtained from individual genotyping. Also, it allows for an accurate reconstruction of the queen genotypes, with about 2% genotyping error. We further validate these inferences using experimental data on colonies with both pool sequencing and individual genotyping of drones. In brief, in this study we present statistical models to accurately estimate the genetic ancestry and reconstruct the genotypes of the queen from pool sequencing data from workers of an eusocial colony. Such information allows to exploit pool sequencing for traditional population genetics analyses, association studies and for selective breeding. While validated in Apis mellifera, these methods are applicable to other eusocial hymenopterans.
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Affiliation(s)
- Sonia E. Eynard
- GenPhySE, INRAE, INP, ENVTUniversité de ToulouseCastanet‐TolosanFrance,LABOGENA DNAJouy‐en‐JosasFrance
| | - Alain Vignal
- GenPhySE, INRAE, INP, ENVTUniversité de ToulouseCastanet‐TolosanFrance
| | - Benjamin Basso
- Abeilles et EnvironnementINRAEAvignonFrance,ITSAPAvignonFrance
| | | | | | | | | | | | | | - Bertrand Servin
- GenPhySE, INRAE, INP, ENVTUniversité de ToulouseCastanet‐TolosanFrance
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5
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Wragg D, Eynard SE, Basso B, Canale‐Tabet K, Labarthe E, Bouchez O, Bienefeld K, Bieńkowska M, Costa C, Gregorc A, Kryger P, Parejo M, Pinto MA, Bidanel J, Servin B, Le Conte Y, Vignal A. Complex population structure and haplotype patterns in the Western European honey bee from sequencing a large panel of haploid drones. Mol Ecol Resour 2022; 22:3068-3086. [PMID: 35689802 PMCID: PMC9796960 DOI: 10.1111/1755-0998.13665] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 05/26/2022] [Accepted: 06/01/2022] [Indexed: 01/07/2023]
Abstract
Honey bee subspecies originate from specific geographical areas in Africa, Europe and the Middle East, and beekeepers interested in specific phenotypes have imported genetic material to regions outside of the bees' original range for use either in pure lines or controlled crosses. Moreover, imported drones are present in the environment and mate naturally with queens from the local subspecies. The resulting admixture complicates population genetics analyses, and population stratification can be a major problem for association studies. To better understand Western European honey bee populations, we produced a whole genome sequence and single nucleotide polymorphism (SNP) genotype data set from 870 haploid drones and demonstrate its utility for the identification of nine genetic backgrounds and various degrees of admixture in a subset of 629 samples. Five backgrounds identified correspond to subspecies, two to isolated populations on islands and two to managed populations. We also highlight several large haplotype blocks, some of which coincide with the position of centromeres. The largest is 3.6 Mb long and represents 21% of chromosome 11, with two major haplotypes corresponding to the two dominant genetic backgrounds identified. This large naturally phased data set is available as a single vcf file that can now serve as a reference for subsequent populations genomics studies in the honey bee, such as (i) selecting individuals of verified homogeneous genetic backgrounds as references, (ii) imputing genotypes from a lower-density data set generated by an SNP-chip or by low-pass sequencing, or (iii) selecting SNPs compatible with the requirements of genotyping chips.
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Affiliation(s)
- David Wragg
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance,Roslin InstituteUniversity of EdinburghMidlothianUK
| | - Sonia E. Eynard
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance
| | - Benjamin Basso
- Institut de l'abeille (ITSAP), UMT PrADEAvignonFrance,INRAE, UR 406 Abeilles et Environment, UMT PrADEAvignonFrance
| | | | | | | | | | | | - Cecilia Costa
- CREA Research Centre for Agriculture and EnvironmentBolognaItaly
| | - Aleš Gregorc
- Faculty of Agriculture and Life SciencesUniversity of MariborPivolaSlovenia
| | - Per Kryger
- Department of Agroecology, Science and TechnologyAarhus UniversitySlagelseDenmark
| | - Melanie Parejo
- Agroscope, Swiss Bee Research CentreBernSwitzerland,Applied Genomics and Bioinformatics, Department of Genetics, Physical Anthropology and Animal PhysiologyUniversity of the Basque CountryLeioaSpain
| | - M. Alice Pinto
- Centro de Investigação de Montanha (CIMO)Instituto Politécnico de BragançaBragançaPortugal
| | | | - Bertrand Servin
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance
| | - Yves Le Conte
- INRAE, UR 406 Abeilles et Environment, UMT PrADEAvignonFrance
| | - Alain Vignal
- GenPhySEUniversité de Toulouse, INRAE, INPT, INP‐ENVTCastanet TolosanFrance
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6
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Guichard M, Dainat B, Eynard S, Vignal A, Servin B, Neuditschko M. Two quantitative trait loci are associated with recapping of Varroa destructor-infested brood cells in Apis mellifera mellifera. Anim Genet 2021; 53:156-160. [PMID: 34729804 PMCID: PMC9297925 DOI: 10.1111/age.13150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/10/2021] [Indexed: 11/30/2022]
Abstract
Recapping of Varroa destructor-infested brood cells is a trait that has recently attracted interest in honey bee breeding to select mite-resistant Apis mellifera colonies. To investigate the genetic architecture of this trait, we evaluated a sample of A. mellifera mellifera colonies (N = 155) from Switzerland and France and performed a genome-wide association study, using a pool of 500 workers per colony for next-generation sequencing. The results revealed that two QTL were significantly (P < 0.05) associated with recapping of V. destructor-infested brood cells. The best-associated QTL is located on chromosome 5 in a region previously found to be associated with grooming behaviour, a resistance trait against V. destructor, in A. mellifera and Apis cerana. The second best-associated QTL is located on chromosome 4 in an intron of the Dscam gene, which is involved in neuronal wiring. Previous research demonstrated that genes involved in neuronal wiring are associated with recapping and varroa sensitive hygiene. Therefore, our study confirms the role of a gene region on chromosome 5 in social immunity and simultaneously provides novel insights into genetic interactions between common mite resistance traits in honey bees.
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Affiliation(s)
- M Guichard
- Agroscope, Swiss Bee Research Centre, Schwarzenburgstrasse 161, Bern, 3003, Switzerland.,Agroscope, Animal GenoPhenomics, Rte de la Tioleyre 4, Posieux, 1725, Switzerland
| | - B Dainat
- Agroscope, Swiss Bee Research Centre, Schwarzenburgstrasse 161, Bern, 3003, Switzerland
| | - S Eynard
- GenPhySE, INRA, INPT, INPENVT, Université de Toulouse, Castanet-Tolosan, 31320, France.,UMT PrADE, Protection des Abeilles Dans l'Environnement, Avignon, 84914, France
| | - A Vignal
- GenPhySE, INRA, INPT, INPENVT, Université de Toulouse, Castanet-Tolosan, 31320, France.,UMT PrADE, Protection des Abeilles Dans l'Environnement, Avignon, 84914, France
| | - B Servin
- GenPhySE, INRA, INPT, INPENVT, Université de Toulouse, Castanet-Tolosan, 31320, France.,UMT PrADE, Protection des Abeilles Dans l'Environnement, Avignon, 84914, France
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- Labogena, Domaine de Vilvert Bat 224 CS80009, Jouy-en-Josas CEDEX, 78353, France
| | - M Neuditschko
- Agroscope, Animal GenoPhenomics, Rte de la Tioleyre 4, Posieux, 1725, Switzerland
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7
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Guichard M, Dainat B, Eynard S, Vignal A, Servin B, Neuditschko M. Identification of quantitative trait loci associated with calmness and gentleness in honey bees using whole-genome sequences. Anim Genet 2021; 52:472-481. [PMID: 33970494 PMCID: PMC8360191 DOI: 10.1111/age.13070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2021] [Indexed: 01/05/2023]
Abstract
The identification of quantitative trait loci (QTL) through genome-wide association studies (GWAS) is a powerful method for unravelling the genetic background of selected traits and improving early-stage predictions. In honey bees (Apis mellifera), past genetic analyses have particularly focused on individual queens and workers. In this study, we used pooled whole-genome sequences to ascertain the genetic variation of the entire colony. In total, we sampled 216 Apis mellifera mellifera and 28 Apis mellifera carnica colonies. Different experts subjectively assessed the gentleness and calmness of the colonies using a standardised protocol. Conducting a GWAS for calmness on 211 purebred A. m. mellifera colonies, we identified three QTL, on chromosomes 8, 6, and 12. The two first QTL correspond to LOC409692 gene, coding for a disintegrin and metalloproteinase domain-containing protein 10, and to Abscam gene, coding for a Dscam family member Abscam protein, respectively. The last gene has been reported to be involved in the domestication of A. mellifera. The third QTL is located 13 kb upstream of LOC102655631, coding for a trehalose transporter. For gentleness, two QTL were identified on chromosomes 4 and 3. They are located within gene LOC413669, coding for a lap4 protein, and gene LOC413416, coding for a bicaudal C homolog 1-B protein, respectively. The identified positional candidate genes of both traits mainly affect the olfaction and nervous system of honey bees. Further research is needed to confirm the results and to better understand the genetic and phenotypic basis of calmness and gentleness.
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Affiliation(s)
- M Guichard
- Agroscope, Swiss Bee Research Centre, Schwarzenburgstrasse 161, Bern, 3003, Switzerland.,Agroscope, Animal GenoPhenomics, Rte de la Tioleyre 4, Posieux, 1725, Switzerland
| | - B Dainat
- Agroscope, Swiss Bee Research Centre, Schwarzenburgstrasse 161, Bern, 3003, Switzerland
| | - S Eynard
- GenPhySE, INRA, INPT, INPENVT, Université de Toulouse, Castanet-Tolosan, 31320, France.,UMT PrADE, Protection des Abeilles Dans l'Environnement, Avignon, 84914, France
| | - A Vignal
- GenPhySE, INRA, INPT, INPENVT, Université de Toulouse, Castanet-Tolosan, 31320, France.,UMT PrADE, Protection des Abeilles Dans l'Environnement, Avignon, 84914, France
| | - B Servin
- GenPhySE, INRA, INPT, INPENVT, Université de Toulouse, Castanet-Tolosan, 31320, France.,UMT PrADE, Protection des Abeilles Dans l'Environnement, Avignon, 84914, France
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- Domaine de Vilvert, Bat 224, CS80009, Jouy-en-Josas CEDEX, 78353, France
| | - M Neuditschko
- Agroscope, Animal GenoPhenomics, Rte de la Tioleyre 4, Posieux, 1725, Switzerland
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8
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Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH, Xie D, Chen G, Guo C, Faircloth BC, Petersen B, Wang Z, Zhou Q, Diekhans M, Chen W, Andreu-Sánchez S, Margaryan A, Howard JT, Parent C, Pacheco G, Sinding MHS, Puetz L, Cavill E, Ribeiro ÂM, Eckhart L, Fjeldså J, Hosner PA, Brumfield RT, Christidis L, Bertelsen MF, Sicheritz-Ponten T, Tietze DT, Robertson BC, Song G, Borgia G, Claramunt S, Lovette IJ, Cowen SJ, Njoroge P, Dumbacher JP, Ryder OA, Fuchs J, Bunce M, Burt DW, Cracraft J, Meng G, Hackett SJ, Ryan PG, Jønsson KA, Jamieson IG, da Fonseca RR, Braun EL, Houde P, Mirarab S, Suh A, Hansson B, Ponnikas S, Sigeman H, Stervander M, Frandsen PB, van der Zwan H, van der Sluis R, Visser C, Balakrishnan CN, Clark AG, Fitzpatrick JW, Bowman R, Chen N, Cloutier A, Sackton TB, Edwards SV, Foote DJ, Shakya SB, Sheldon FH, Vignal A, Soares AER, Shapiro B, González-Solís J, Ferrer-Obiol J, Rozas J, Riutort M, Tigano A, Friesen V, Dalén L, Urrutia AO, Székely T, Liu Y, Campana MG, Corvelo A, Fleischer RC, Rutherford KM, Gemmell NJ, Dussex N, Mouritsen H, Thiele N, Delmore K, Liedvogel M, Franke A, Hoeppner MP, Krone O, Fudickar AM, Milá B, Ketterson ED, Fidler AE, Friis G, Parody-Merino ÁM, Battley PF, Cox MP, Lima NCB, Prosdocimi F, Parchman TL, Schlinger BA, Loiselle BA, Blake JG, Lim HC, Day LB, Fuxjager MJ, Baldwin MW, Braun MJ, Wirthlin M, Dikow RB, Ryder TB, Camenisch G, Keller LF, DaCosta JM, Hauber ME, Louder MIM, Witt CC, McGuire JA, Mudge J, Megna LC, Carling MD, Wang B, Taylor SA, Del-Rio G, Aleixo A, Vasconcelos ATR, Mello CV, Weir JT, Haussler D, Li Q, Yang H, Wang J, Lei F, Rahbek C, Gilbert MTP, Graves GR, Jarvis ED, Paten B, Zhang G. Author Correction: Dense sampling of bird diversity increases power of comparative genomics. Nature 2021; 592:E24. [PMID: 33833441 PMCID: PMC8081657 DOI: 10.1038/s41586-021-03473-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Shaohong Feng
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.,BGI-Shenzhen, Shenzhen, China
| | - Josefin Stiller
- Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yuan Deng
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China.,Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Joel Armstrong
- UC Santa Cruz Genomics Institute, UC Santa Cruz, Santa Cruz, CA, USA
| | - Qi Fang
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China.,Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Andrew Hart Reeve
- Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
| | - Duo Xie
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China.,BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Guangji Chen
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China.,BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China
| | - Chunxue Guo
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | - Brant C Faircloth
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.,Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA
| | - Bent Petersen
- Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Sciences, AIMST University, Kedah, Malaysia.,Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Zongji Wang
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China.,MOE Laboratory of Biosystems Homeostasis and Protection, Life Sciences Institute, Zhejiang University, Hangzhou, China.,Department of Neuroscience and Developmental Biology, University of Vienna, Vienna, Austria
| | - Qi Zhou
- MOE Laboratory of Biosystems Homeostasis and Protection, Life Sciences Institute, Zhejiang University, Hangzhou, China.,Department of Neuroscience and Developmental Biology, University of Vienna, Vienna, Austria.,Center for Reproductive Medicine, The 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Mark Diekhans
- UC Santa Cruz Genomics Institute, UC Santa Cruz, Santa Cruz, CA, USA
| | - Wanjun Chen
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | - Sergio Andreu-Sánchez
- Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Ashot Margaryan
- Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Institute of Molecular Biology, National Academy of Sciences, Yerevan, Armenia
| | | | | | - George Pacheco
- Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mikkel-Holger S Sinding
- Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lara Puetz
- Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emily Cavill
- Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ângela M Ribeiro
- Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
| | - Leopold Eckhart
- Department of Dermatology, Medical University of Vienna, Vienna, Austria
| | - Jon Fjeldså
- Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.,Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Peter A Hosner
- Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark.,Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Robb T Brumfield
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.,Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA
| | - Les Christidis
- Southern Cross University, Coffs Harbour, New South Wales, Australia
| | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg, Denmark
| | - Thomas Sicheritz-Ponten
- Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio), Faculty of Applied Sciences, AIMST University, Kedah, Malaysia.,Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Gang Song
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Environmental Futures Research Institute, Griffith University, Nathan, Queensland, Australia
| | - Gerald Borgia
- Department of Biology, University of Maryland, College Park, MD, USA
| | - Santiago Claramunt
- Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada.,Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Irby J Lovette
- Cornell Lab of Ornithology, Cornell University, Ithaca, NY, USA
| | - Saul J Cowen
- Biodiversity and Conservation Science, Department of Biodiversity Conservation and Attractions, Perth, Western Australia, Australia
| | - Peter Njoroge
- Ornithology Section, Zoology Department, National Museums of Kenya, Nairobi, Kenya
| | | | - Oliver A Ryder
- San Diego Zoo Institute for Conservation Research, Escondido, CA, USA.,Evolution, Behavior, and Ecology, Division of Biology, University of California San Diego, La Jolla, CA, USA
| | - Jérôme Fuchs
- Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France
| | - Michael Bunce
- Trace and Environmental DNA (TrEnD) Laboratory, School of Molecular and Life Sciences, Curtin University, Western Australia, Perth, Australia
| | - David W Burt
- UQ Genomics, University of Queensland, Brisbane, Queensland, Australia
| | - Joel Cracraft
- Department of Ornithology, American Museum of Natural History, New York, NY, USA
| | | | - Shannon J Hackett
- Integrative Research Center, Field Museum of Natural History, Chicago, IL, USA
| | - Peter G Ryan
- FitzPatrick Institute of African Ornithology, University of Cape Town, Cape Town, South Africa
| | - Knud Andreas Jønsson
- Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
| | - Ian G Jamieson
- Department of Zoology, University of Otago, Dunedin, New Zealand
| | - Rute R da Fonseca
- Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Edward L Braun
- Department of Biology, University of Florida, Gainesville, FL, USA
| | - Peter Houde
- Department of Biology, New Mexico State University, Las Cruces, NM, USA
| | - Siavash Mirarab
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Alexander Suh
- Department of Ecology and Genetics - Evolutionary Biology, Evolutionary Biology Centre (EBC), Science for Life Laboratory, Uppsala University, Uppsala, Sweden.,Department of Organismal Biology - Systematic Biology, Evolutionary Biology Centre (EBC), Science for Life Laboratory, Uppsala University, Uppsala, Sweden.,School of Biological Sciences, University of East Anglia, Norwich, UK
| | - Bengt Hansson
- Department of Biology, Lund University, Lund, Sweden
| | - Suvi Ponnikas
- Department of Biology, Lund University, Lund, Sweden
| | - Hanna Sigeman
- Department of Biology, Lund University, Lund, Sweden
| | - Martin Stervander
- Department of Biology, Lund University, Lund, Sweden.,Institute of Ecology and Evolution, University of Oregon, Eugene, OR, USA
| | - Paul B Frandsen
- Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT, USA.,Data Science Lab, Office of the Chief Information Officer, Smithsonian Institution, Washington, DC, USA
| | | | - Rencia van der Sluis
- Focus Area for Human Metabolomics, North-West University, Potchefstroom, South Africa
| | - Carina Visser
- Department of Animal Sciences, University of Pretoria, Pretoria, South Africa
| | | | - Andrew G Clark
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA
| | | | - Reed Bowman
- Avian Ecology Program, Archbold Biological Station, Venus, FL, USA
| | - Nancy Chen
- Department of Biology, University of Rochester, Rochester, NY, USA
| | - Alison Cloutier
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.,Museum of Comparative Zoology, Harvard University, Cambridge, MA, USA
| | | | - Scott V Edwards
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.,Museum of Comparative Zoology, Harvard University, Cambridge, MA, USA
| | - Dustin J Foote
- Department of Biology, East Carolina University, Greenville, NC, USA.,Sylvan Heights Bird Park, Scotland Neck, NC, USA
| | - Subir B Shakya
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.,Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA
| | - Frederick H Sheldon
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA.,Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA
| | - Alain Vignal
- GenPhySE, INRA, INPT, INP-ENVT, Université de Toulouse, Castanet-Tolosan, France
| | - André E R Soares
- Laboratório Nacional de Computação Científica, Petrópolis, Brazil.,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
| | - Jacob González-Solís
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.,Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals (BEECA), Universitat de Barcelona, Barcelona, Spain
| | - Joan Ferrer-Obiol
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.,Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
| | - Julio Rozas
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.,Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
| | - Marta Riutort
- Institut de Recerca de la Biodiversitat (IRBio), Universitat de Barcelona, Barcelona, Spain.,Departament de Genètica, Microbiologia i Estadística, Universitat de Barcelona, Barcelona, Spain
| | - Anna Tigano
- Department of Molecular, Cellular and Biomedical Sciences, University of New Hampshire, Durham, NH, USA.,Department of Biology, Queen's University, Kingston, Ontario, Canada
| | - Vicki Friesen
- Department of Biology, Queen's University, Kingston, Ontario, Canada
| | - Love Dalén
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.,Centre for Palaeogenetics, Stockholm, Sweden
| | - Araxi O Urrutia
- Milner Centre for Evolution, University of Bath, Bath, UK.,Instituto de Ecologia, UNAM, Mexico City, Mexico
| | - Tamás Székely
- Milner Centre for Evolution, University of Bath, Bath, UK
| | - Yang Liu
- State Key Laboratory of Biocontrol, School of Ecology, Sun Yat-sen University, Guangzhou, China
| | - Michael G Campana
- Center for Conservation Genomics, Smithsonian Conservation Biology Institute, Smithsonian Institution, Washington, DC, USA
| | | | - Robert C Fleischer
- Center for Conservation Genomics, Smithsonian Conservation Biology Institute, Smithsonian Institution, Washington, DC, USA
| | - Kim M Rutherford
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - Neil J Gemmell
- Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - Nicolas Dussex
- Department of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.,Centre for Palaeogenetics, Stockholm, Sweden.,Department of Anatomy, University of Otago, Dunedin, New Zealand
| | - Henrik Mouritsen
- AG Neurosensory Sciences, Institut für Biologie und Umweltwissenschaften, University of Oldenburg, Oldenburg, Germany
| | - Nadine Thiele
- AG Neurosensory Sciences, Institut für Biologie und Umweltwissenschaften, University of Oldenburg, Oldenburg, Germany
| | - Kira Delmore
- Biology Department, Texas A&M University, College Station, TX, USA.,MPRG Behavioural Genomics, Max Planck Institute for Evolutionary Biology, Plön, Germany
| | | | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts- University of Kiel, Kiel, Germany
| | - Marc P Hoeppner
- Institute of Clinical Molecular Biology, Christian-Albrechts- University of Kiel, Kiel, Germany
| | - Oliver Krone
- Department of Wildlife Diseases, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
| | - Adam M Fudickar
- Environmental Resilience Institute, Indiana University, Bloomington, IN, USA
| | - Borja Milá
- National Museum of Natural Sciences, Spanish National Research Council (CSIC), Madrid, Spain
| | | | - Andrew Eric Fidler
- Institute of Marine Science, University of Auckland, Auckland, New Zealand
| | - Guillermo Friis
- Center for Genomics and Systems Biology, Department of Biology, New York University - Abu Dhabi, Abu Dhabi, UAE
| | | | - Phil F Battley
- Wildlife and Ecology Group, Massey University, Palmerston North, New Zealand
| | - Murray P Cox
- School of Fundamental Sciences, Massey University, Palmerston North, New Zealand
| | - Nicholas Costa Barroso Lima
- Laboratório Nacional de Computação Científica, Petrópolis, Brazil.,Departamento de Bioquímica e Biologia Molecular, Centro de Ciências, Universidade Federal do Ceará, Fortaleza, Brazil
| | - Francisco Prosdocimi
- Laboratório de Genômica e Biodiversidade, Instituto de Bioquímica Médica Leopoldo de Meis, Rio de Janeiro, Brazil
| | | | - Barney A Schlinger
- Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA, USA.,Smithsonian Tropical Research Institute, Panama City, Panama
| | - Bette A Loiselle
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA.,Center for Latin American Studies, University of Florida, Gainesville, FL, USA
| | - John G Blake
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | - Haw Chuan Lim
- Center for Conservation Genomics, Smithsonian Conservation Biology Institute, Smithsonian Institution, Washington, DC, USA.,Department of Biology, George Mason University, Fairfax, VA, USA
| | - Lainy B Day
- Department of Biology and Neuroscience Minor, University of Mississippi, University, MS, USA
| | - Matthew J Fuxjager
- Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
| | | | - Michael J Braun
- Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA.,Behavior, Ecology, Evolution and Systematics Program, University of Maryland, College Park, MD, USA
| | - Morgan Wirthlin
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Rebecca B Dikow
- Data Science Lab, Office of the Chief Information Officer, Smithsonian Institution, Washington, DC, USA
| | - T Brandt Ryder
- Migratory Bird Center, Smithsonian National Zoological Park and Conservation Biology Institute, Washington, DC, USA
| | - Glauco Camenisch
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Lukas F Keller
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | | | - Mark E Hauber
- Department of Evolution, Ecology, and Behavior, School of Integrative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Matthew I M Louder
- Department of Biology, East Carolina University, Greenville, NC, USA.,Department of Evolution, Ecology, and Behavior, School of Integrative Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,International Research Center for Neurointelligence, University of Tokyo, Tokyo, Japan
| | - Christopher C Witt
- Museum of Southwestern Biology, Department of Biology, University of New Mexico, Albuquerque, NM, USA
| | - Jimmy A McGuire
- Museum of Vertebrate Zoology, Department of Integrative Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Joann Mudge
- National Center for Genome Resources, Santa Fe, NM, USA
| | - Libby C Megna
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY, USA
| | - Matthew D Carling
- Department of Zoology and Physiology, University of Wyoming, Laramie, WY, USA
| | - Biao Wang
- School of BioSciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Scott A Taylor
- Department of Ecology and Evolutionary Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Glaucia Del-Rio
- Museum of Natural Science, Louisiana State University, Baton Rouge, LA, USA
| | - Alexandre Aleixo
- Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland
| | | | - Claudio V Mello
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Jason T Weir
- Department of Natural History, Royal Ontario Museum, Toronto, Ontario, Canada.,Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada.,Department of Biological Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - David Haussler
- UC Santa Cruz Genomics Institute, UC Santa Cruz, Santa Cruz, CA, USA
| | - Qiye Li
- China National GeneBank, BGI-Shenzhen, Shenzhen, China.,BGI-Shenzhen, Shenzhen, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen, China.,James D. Watson Institute of Genome Sciences, Hangzhou, China
| | | | - Fumin Lei
- Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| | - Carsten Rahbek
- Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark.,Institute of Ecology, Peking University, Beijing, China.,Department of Life Sciences, Imperial College London, Ascot, UK
| | - M Thomas P Gilbert
- Section for Evolutionary Genomics, The GLOBE Institute, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,University Museum, Norwegian University of Science and Technology, Trondheim, Norway
| | - Gary R Graves
- Center for Macroecology, Evolution, and Climate, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark.,Department of Vertebrate Zoology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Erich D Jarvis
- Duke University Medical Center, Durham, NC, USA.,The Rockefeller University, New York, NY, USA.,Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, UC Santa Cruz, Santa Cruz, CA, USA.
| | - Guojie Zhang
- China National GeneBank, BGI-Shenzhen, Shenzhen, China. .,State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China. .,Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark. .,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
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9
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Mariadassou M, Suez M, Sathyakumar S, Vignal A, Arca M, Nicolas P, Faraut T, Esquerré D, Nishibori M, Vieaud A, Chen CF, Manh Pham H, Roman Y, Hospital F, Zerjal T, Rognon X, Tixier-Boichard M. Unraveling the history of the genus Gallus through whole genome sequencing. Mol Phylogenet Evol 2020; 158:107044. [PMID: 33346111 DOI: 10.1016/j.ympev.2020.107044] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 10/23/2020] [Accepted: 12/14/2020] [Indexed: 12/16/2022]
Abstract
The genus Gallus is distributed across a large part of Southeast Asia and has received special interest because the domestic chicken, Gallus gallus domesticus, has spread all over the world and is a major protein source for humans. There are four species: the red junglefowl (G. gallus), the green junglefowl (G. varius), the Lafayette's junglefowl (G. lafayettii) and the grey junglefowl (G. sonneratii). The aim of this study is to reconstruct the history of these species by a whole genome sequencing approach and resolve inconsistencies between well supported topologies inferred using different data and methods. Using deep sequencing, we identified over 35 million SNPs and reconstructed the phylogeny of the Gallus genus using both distance (BioNJ) and maximum likelihood (ML) methods. We observed discrepancies according to reconstruction methods and genomic components. The two most supported topologies were previously reported and were discriminated by using phylogenetic and gene flow analyses, based on ABBA statistics. Terminology fix requested by the deputy editor led to support a scenario with G. gallus as the earliest branching lineage of the Gallus genus, instead of G. varius. We discuss the probable causes for the discrepancy. A likely one is that G. sonneratii samples from parks or private collections are all recent hybrids, with roughly 10% of their autosomal genome originating from G. gallus. The removal of those regions is needed to provide reliable data, which was not done in previous studies. We took care of this and additionally included two wild G. sonneratii samples from India, showing no trace of introgression. This reinforces the importance of carefully selecting and validating samples and genomic components in phylogenomics.
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Affiliation(s)
| | - Marie Suez
- Université Paris Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | | | - Alain Vignal
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326 Castanet Tolosan, France
| | - Mariangela Arca
- Université Paris Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Pierre Nicolas
- Université Paris Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
| | - Thomas Faraut
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326 Castanet Tolosan, France
| | - Diane Esquerré
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326 Castanet Tolosan, France; Get-PlaGe, INRAE, 31326 Castanet Tolosan, France
| | - Masahide Nishibori
- Lab. of Animal Genetics, Department of Animal Life Science, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima 739-8528, Japan
| | - Agathe Vieaud
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - Chih-Feng Chen
- Department of Animal Science, iEGG and Animal Biotechnology Center, National Chung-Hsing University, Taichung 40227, Taiwan
| | - Hung Manh Pham
- Faculty of Animal Science, Vietnam National University of Agriculture, Trau Quy Town, Gia Lam District, Ha Noi City, Viet Nam
| | | | - Frédéric Hospital
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - Tatiana Zerjal
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
| | - Xavier Rognon
- Université Paris Saclay, INRAE, AgroParisTech, GABI, 78350 Jouy-en-Josas, France
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10
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Eynard SE, Sann C, Basso B, Guirao AL, Le Conte Y, Servin B, Tison L, Vignal A, Mondet F. Descriptive Analysis of the Varroa Non-Reproduction Trait in Honey Bee Colonies and Association with Other Traits Related to Varroa Resistance. Insects 2020; 11:E492. [PMID: 32752279 PMCID: PMC7469219 DOI: 10.3390/insects11080492] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 07/27/2020] [Accepted: 07/30/2020] [Indexed: 11/17/2022]
Abstract
In the current context of worldwide honey bee colony losses, among which the varroa mite plays a major role, the hope to improve honey bee health lies in part in the breeding of varroa resistant colonies. To do so, methods used to evaluate varroa resistance need better understanding. Repeatability and correlations between traits such as mite non-reproduction (MNR), varroa sensitive hygiene (VSH), and hygienic behavior are poorly known, due to practical limitations and to their underlying complexity. We investigate (i) the variability, (ii) the repeatability of the MNR score, and (iii) its correlation with other resistance traits. To reduce the inherent variability of MNR scores, we propose to apply an empirical Bayes correction. In the short-term (ten days), MNR had a modest repeatability of 0.4, whereas in the long-term (a month), it had a low repeatability of 0.2, similar to other resistance traits. Within our dataset, there was no correlation between MNR and VSH. Although MNR is amongst the most popular varroa resistance estimates in field studies, its underlying complex mechanism is not fully understood. Its lack of correlation with better described resistance traits and low repeatability suggest that MNR needs to be interpreted cautiously, especially when used for selection.
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Affiliation(s)
- Sonia E. Eynard
- GenPhySe, Université de Toulouse, INRAE, ENVT, 31320 Castanet-Tolosan, France; (B.S.); (A.V.)
- UMT PrADE, Protection des Abeilles dans l’environnement, 84914 Avignon, France; (B.B.); (Y.L.C.); (L.T.); (F.M.)
| | | | - Benjamin Basso
- UMT PrADE, Protection des Abeilles dans l’environnement, 84914 Avignon, France; (B.B.); (Y.L.C.); (L.T.); (F.M.)
- ITSAP, 84914 Avignon, France;
- Abeilles et Environnement, INRAE Avignon, 84914 Avignon, France
| | | | - Yves Le Conte
- UMT PrADE, Protection des Abeilles dans l’environnement, 84914 Avignon, France; (B.B.); (Y.L.C.); (L.T.); (F.M.)
- Abeilles et Environnement, INRAE Avignon, 84914 Avignon, France
| | - Bertrand Servin
- GenPhySe, Université de Toulouse, INRAE, ENVT, 31320 Castanet-Tolosan, France; (B.S.); (A.V.)
- UMT PrADE, Protection des Abeilles dans l’environnement, 84914 Avignon, France; (B.B.); (Y.L.C.); (L.T.); (F.M.)
| | - Lea Tison
- UMT PrADE, Protection des Abeilles dans l’environnement, 84914 Avignon, France; (B.B.); (Y.L.C.); (L.T.); (F.M.)
- Abeilles et Environnement, INRAE Avignon, 84914 Avignon, France
- Santé et Agroécologie du Vignoble, INRAE Bordeaux, 33882 Villenave-d’Ornon, France
| | - Alain Vignal
- GenPhySe, Université de Toulouse, INRAE, ENVT, 31320 Castanet-Tolosan, France; (B.S.); (A.V.)
- UMT PrADE, Protection des Abeilles dans l’environnement, 84914 Avignon, France; (B.B.); (Y.L.C.); (L.T.); (F.M.)
| | - Fanny Mondet
- UMT PrADE, Protection des Abeilles dans l’environnement, 84914 Avignon, France; (B.B.); (Y.L.C.); (L.T.); (F.M.)
- Abeilles et Environnement, INRAE Avignon, 84914 Avignon, France
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11
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Morris KM, Hindle MM, Boitard S, Burt DW, Danner AF, Eory L, Forrest HL, Gourichon D, Gros J, Hillier LW, Jaffredo T, Khoury H, Lansford R, Leterrier C, Loudon A, Mason AS, Meddle SL, Minvielle F, Minx P, Pitel F, Seiler JP, Shimmura T, Tomlinson C, Vignal A, Webster RG, Yoshimura T, Warren WC, Smith J. The quail genome: insights into social behaviour, seasonal biology and infectious disease response. BMC Biol 2020; 18:14. [PMID: 32050986 PMCID: PMC7017630 DOI: 10.1186/s12915-020-0743-4] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 01/24/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The Japanese quail (Coturnix japonica) is a popular domestic poultry species and an increasingly significant model species in avian developmental, behavioural and disease research. RESULTS We have produced a high-quality quail genome sequence, spanning 0.93 Gb assigned to 33 chromosomes. In terms of contiguity, assembly statistics, gene content and chromosomal organisation, the quail genome shows high similarity to the chicken genome. We demonstrate the utility of this genome through three diverse applications. First, we identify selection signatures and candidate genes associated with social behaviour in the quail genome, an important agricultural and domestication trait. Second, we investigate the effects and interaction of photoperiod and temperature on the transcriptome of the quail medial basal hypothalamus, revealing key mechanisms of photoperiodism. Finally, we investigate the response of quail to H5N1 influenza infection. In quail lung, many critical immune genes and pathways were downregulated after H5N1 infection, and this may be key to the susceptibility of quail to H5N1. CONCLUSIONS We have produced a high-quality genome of the quail which will facilitate further studies into diverse research questions using the quail as a model avian species.
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Affiliation(s)
- Katrina M Morris
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.
| | - Matthew M Hindle
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Simon Boitard
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - David W Burt
- The John Hay Building, Queensland Biosciences Precinct, 306 Carmody Road, The University of Queensland, QLD, St Lucia, 4072, Australia
| | - Angela F Danner
- Virology Division, Department of Infectious Diseases, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - Lel Eory
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Heather L Forrest
- Virology Division, Department of Infectious Diseases, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - David Gourichon
- PEAT Pôle d'Expérimentation Avicole de Tours, Centre de recherche Val de Loire, INRAE, 1295, Nouzilly, UE, France
| | - Jerome Gros
- Department of Developmental and Stem Cell Biology, Institut Pasteur, 25 rue du Docteur Roux, 75724, Cedex 15, Paris, France
- CNRS URA3738, 25 rue du Dr Roux, 75015, Paris, France
| | - LaDeana W Hillier
- McDonnell Genome Institute, Washington University School of Medicine, 4444 Forest Park Blvd, St Louis, MO, 63108, USA
| | - Thierry Jaffredo
- CNRS UMR7622, Inserm U 1156, Laboratoire de Biologie du Développement, Sorbonne Université, IBPS, 75005, Paris, France
| | - Hanane Khoury
- CNRS UMR7622, Inserm U 1156, Laboratoire de Biologie du Développement, Sorbonne Université, IBPS, 75005, Paris, France
| | - Rusty Lansford
- Department of Radiology and Developmental Neuroscience Program, Saban Research Institute, Children's Hospital Los Angeles and Keck School of Medicine of the University of Southern California, Los Angeles, CA, 90027, USA
| | - Christine Leterrier
- UMR85 Physiologie de la Reproduction et des Comportements, INRAE, CNRS, Université François Rabelais, IFCE, INRAE, Val de Loire, 37380, Nouzilly, Centre, France
| | - Andrew Loudon
- Centre for Biological Timing, Faculty of Biology, Medicine and Health, School of Medical Sciences, University of Manchester, 3.001, A.V. Hill Building, Oxford Road, Manchester, M13 9PT, UK
| | - Andrew S Mason
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Simone L Meddle
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Francis Minvielle
- GABI, INRAE, AgroParisTech, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Patrick Minx
- McDonnell Genome Institute, Washington University School of Medicine, 4444 Forest Park Blvd, St Louis, MO, 63108, USA
| | - Frédérique Pitel
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - J Patrick Seiler
- Virology Division, Department of Infectious Diseases, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - Tsuyoshi Shimmura
- Department of Biological Production, Tokyo University of Agriculture and Technology, 3-8-1 Harumi-cho, Fuchu, Tokyo, 183-8538, Japan
| | - Chad Tomlinson
- McDonnell Genome Institute, Washington University School of Medicine, 4444 Forest Park Blvd, St Louis, MO, 63108, USA
| | - Alain Vignal
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Robert G Webster
- Virology Division, Department of Infectious Diseases, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105, USA
| | - Takashi Yoshimura
- Institute of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Wesley C Warren
- Department of Animal Sciences, Department of Surgery, Institute for Data Science and Informatics, University of Missouri, Bond Life Sciences Center, 1201 Rollins Street, Columbia, MO, 65211, USA
| | - Jacqueline Smith
- The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
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12
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Dyomin A, Galkina S, Fillon V, Cauet S, Lopez-Roques C, Rodde N, Klopp C, Vignal A, Sokolovskaya A, Saifitdinova A, Gaginskaya E. Structure of the intergenic spacers in chicken ribosomal DNA. Genet Sel Evol 2019; 51:59. [PMID: 31655542 PMCID: PMC6815422 DOI: 10.1186/s12711-019-0501-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 10/14/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Ribosomal DNA (rDNA) repeats are situated in the nucleolus organizer regions (NOR) of chromosomes and transcribed into rRNA for ribosome biogenesis. Thus, they are an essential component of eukaryotic genomes. rDNA repeat units consist of rRNA gene clusters that are transcribed into single pre-rRNA molecules, each separated by intergenic spacers (IGS) that contain regulatory elements for rRNA gene cluster transcription. Because of their high repeat content, rDNA sequences are usually absent from genome assemblies. In this work, we used the long-read sequencing technology to describe the chicken IGS and fill the knowledge gap on rDNA sequences of one of the key domesticated animals. METHODS We used the long-read PacBio RSII technique to sequence the BAC clone WAG137G04 (Wageningen BAC library) known to contain chicken NOR elements and the HGAP workflow software suit to assemble the PacBio RSII reads. Whole-genome sequence contigs homologous to the chicken rDNA repetitive unit were identified based on the Gallus_gallus-5.0 assembly with BLAST. We used the Geneious 9.0.5 and Mega software, maximum likelihood method and Chickspress project for sequence evolution analysis, phylogenetic tree construction and analysis of the raw transcriptome data. RESULTS Three complete IGS sequences in the White Leghorn chicken genome and one IGS sequence in the red junglefowl contig AADN04001305.1 (Gallus_gallus-5.0) were detected. They had various lengths and contained three groups of tandem repeats (some of them being very GC rich) that form highly organized arrays. Initiation and termination sites of rDNA transcription were located within small and large unique regions (SUR and LUR), respectively. No functionally significant sites were detected within the tandem repeat sequences. CONCLUSIONS Due to the highly organized GC-rich repeats, the structure of the chicken IGS differs from that of IGS in human, apes, Xenopus or fish rDNA. However, the chicken IGS shares some molecular organization features with that of the turtles, which are other representatives of the Sauropsida clade that includes birds and reptiles. Our current results on the structure of chicken IGS together with the previously reported ribosomal gene cluster sequence provide sufficient data to consider that the complete chicken rDNA sequence is assembled with confidence in terms of molecular DNA organization.
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Affiliation(s)
- Alexander Dyomin
- Saint Petersburg State University, Universitetskaya emb. 7/9, Saint Petersburg, 199034, Russian Federation.,Saratov State Medical University, Bolshaya Kazachia Str. 112, Saratov, Russian Federation
| | - Svetlana Galkina
- Saint Petersburg State University, Universitetskaya emb. 7/9, Saint Petersburg, 199034, Russian Federation
| | - Valerie Fillon
- INRA, GenPhySE, 24 Chemin de Borde Rouge, Auzeville, 31326, Castanet Tolosan, France
| | - Stephane Cauet
- INRA, CNRGV, 24 Chemin de Borde Rouge, Auzeville, 31326, Castanet Tolosan, France
| | - Celine Lopez-Roques
- INRA, GeT-PlaGe, 24 Chemin de Borde Rouge, Auzeville, 31326, Castanet Tolosan, France
| | - Nathalie Rodde
- INRA, CNRGV, 24 Chemin de Borde Rouge, Auzeville, 31326, Castanet Tolosan, France
| | - Christophe Klopp
- INRA, Sigenae, MIAT, 24 Chemin de Borde Rouge, Auzeville, 31326, Castanet Tolosan, France
| | - Alain Vignal
- INRA, GenPhySE, 24 Chemin de Borde Rouge, Auzeville, 31326, Castanet Tolosan, France
| | - Anastasia Sokolovskaya
- Saint Petersburg State University, Universitetskaya emb. 7/9, Saint Petersburg, 199034, Russian Federation
| | - Alsu Saifitdinova
- Herzen State Pedagogical University of Russia, Moika Emb. 48, Saint Petersburg, 191186, Russian Federation
| | - Elena Gaginskaya
- Saint Petersburg State University, Universitetskaya emb. 7/9, Saint Petersburg, 199034, Russian Federation.
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13
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Vignal A, Boitard S, Thébault N, Dayo GK, Yapi-Gnaore V, Youssao Abdou Karim I, Berthouly-Salazar C, Pálinkás-Bodzsár N, Guémené D, Thibaud-Nissen F, Warren WC, Tixier-Boichard M, Rognon X. A guinea fowl genome assembly provides new evidence on evolution following domestication and selection in galliformes. Mol Ecol Resour 2019; 19:997-1014. [PMID: 30945415 PMCID: PMC6579635 DOI: 10.1111/1755-0998.13017] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 03/19/2019] [Accepted: 03/25/2019] [Indexed: 01/25/2023]
Abstract
The helmeted guinea fowl Numida meleagris belongs to the order Galliformes. Its natural range includes a large part of sub‐Saharan Africa, from Senegal to Eritrea and from Chad to South Africa. Archaeozoological and artistic evidence suggest domestication of this species may have occurred about 2,000 years BP in Mali and Sudan primarily as a food resource, although villagers also benefit from its capacity to give loud alarm calls in case of danger, of its ability to consume parasites such as ticks and to hunt snakes, thus suggesting its domestication may have resulted from a commensal association process. Today, it is still farmed in Africa, mainly as a traditional village poultry, and is also bred more intensively in other countries, mainly France and Italy. The lack of available molecular genetic markers has limited the genetic studies conducted to date on guinea fowl. We present here a first‐generation whole‐genome sequence draft assembly used as a reference for a study by a Pool‐seq approach of wild and domestic populations from Europe and Africa. We show that the domestic populations share a higher genetic similarity between each other than they do to wild populations living in the same geographical area. Several genomic regions showing selection signatures putatively related to domestication or importation to Europe were detected, containing candidate genes, most notably EDNRB2, possibly explaining losses in plumage coloration phenotypes in domesticated populations.
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Affiliation(s)
- Alain Vignal
- GenPhySE, INRA, INPT, INP-ENVT, Université de Toulouse, Castanet Tolosan, France
| | - Simon Boitard
- GenPhySE, INRA, INPT, INP-ENVT, Université de Toulouse, Castanet Tolosan, France
| | - Noémie Thébault
- GenPhySE, INRA, INPT, INP-ENVT, Université de Toulouse, Castanet Tolosan, France
| | | | | | | | | | | | | | - Francoise Thibaud-Nissen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - Wesley C Warren
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri.,Bond Life Sciences Center, University of Missouri, Columbia, Missouri
| | | | - Xavier Rognon
- GABI, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
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14
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Robic A, Morisson M, Leroux S, Gourichon D, Vignal A, Thebault N, Fillon V, Minvielle F, Bed’Hom B, Zerjal T, Pitel F. Two new structural mutations in the 5' region of the ASIP gene cause diluted feather color phenotypes in Japanese quail. Genet Sel Evol 2019; 51:12. [PMID: 30987584 PMCID: PMC6466734 DOI: 10.1186/s12711-019-0458-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 04/03/2019] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND In quail, two feather colour phenotypes i.e. fawn-2/beige and yellow are associated with the ASIP locus. The aim of our study was to characterize the structural modifications within this locus that explain the yellow mutation (large deletion) and the fawn-2/beige mutation (assumed to be caused by a different structural modification). RESULTS For the yellow phenotype, we identified a complex mutation that involves a 141,162-bp long deletion. For the fawn-2/beige phenotype, we identified a 71-kb tandem duplication that comprises one unchanged copy of ASIP and one copy present in the ITCH-ASIP fusion gene, which leads to a transcript coding for a normal ASIP protein. Although this agrees with previous reports that reported an increased level of ASIP transcripts in the skin of mutant animals, we show that in the skin from fawn-2/beige embryos, this level is higher than expected with a simple duplication of the ASIP gene. Thus, we hypothesize that the 5' region of the ITCH-ASIP fusion gene leads to a higher transcription level than the 5' region of the ASIP gene. CONCLUSIONS We were able to conclude that the fawn-2 and beige phenotypes are caused by the same allele at the ASIP locus. Both of the associated mutations fawn-2/beige and yellow lead to the formation of a fusion gene, which encodes a transcript for the ASIP protein. In both cases, transcription of ASIP depends on the promoter of a different gene, which includes alternative up-regulating sequences. However, we cannot exclude the possibility that the loss of the 5' region of the ASIP gene itself has additional impacts, especially for the fawn-2/beige mutation. In addition, in several other species including mammals, the existence of other dominant gain-of-function structural modifications that are localized upstream of the ASIP coding sequences has been reported, which supports our hypothesis that repressors in the 5' region of ASIP are absent in the fawn-2/beige mutant.
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Affiliation(s)
- Annie Robic
- GenPhySE, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
| | - Mireille Morisson
- GenPhySE, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
| | - Sophie Leroux
- GenPhySE, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
| | | | - Alain Vignal
- GenPhySE, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
| | - Noémie Thebault
- GenPhySE, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
| | - Valérie Fillon
- GenPhySE, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
| | - Francis Minvielle
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Bertrand Bed’Hom
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Tatiana Zerjal
- GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France
| | - Frédérique Pitel
- GenPhySE, Université de Toulouse, INRA, ENVT, 31326 Castanet-Tolosan, France
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15
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Wragg D, Techer MA, Canale-Tabet K, Basso B, Bidanel JP, Labarthe E, Bouchez O, Le Conte Y, Clémencet J, Delatte H, Vignal A. Autosomal and Mitochondrial Adaptation Following Admixture: A Case Study on the Honeybees of Reunion Island. Genome Biol Evol 2018; 10:220-238. [PMID: 29202174 PMCID: PMC5814903 DOI: 10.1093/gbe/evx247] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2017] [Indexed: 12/28/2022] Open
Abstract
The honeybee population of the tropical Reunion Island is a genetic admixture of the Apis mellifera unicolor subspecies, originally described in Madagascar, and of European subspecies, mainly A. m. carnica and A. m. ligustica, regularly imported to the island since the late 19th century. We took advantage of this population to study genetic admixing of the tropical-adapted indigenous and temperate-adapted European genetic backgrounds. Whole genome sequencing of 30 workers and 6 males from Reunion, compared with samples from Europe, Madagascar, Mauritius, Rodrigues, and the Seychelles, revealed the Reunion honeybee population to be composed on an average of 53.2 ± 5.9% A. m. unicolor nuclear genomic background, the rest being mainly composed of A. m. carnica and to a lesser extent A. m. ligustica. In striking contrast to this, only 1 out of the 36 honeybees from Reunion had a mitochondrial genome of European origin, suggesting selection has favored the A. m. unicolor mitotype, which is possibly better adapted to the island’s bioclimate. Local ancestry was determined along the chromosomes for all Reunion samples, and a test for preferential selection for the A. m. unicolor or European background revealed 15 regions significantly associated with the A. m. unicolor lineage and 9 regions with the European lineage. Our results provide insights into the long-term consequences of introducing exotic specimen on the nuclear and mitochondrial genomes of locally adapted populations.
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Affiliation(s)
- David Wragg
- GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, Castanet Tolosan, France.,The Roslin Institute, University of Edinburgh, Midlothian, United Kingdom
| | - Maéva Angélique Techer
- CIRAD, UMR PVBMT, Saint Pierre, La Réunion, France.,UMR PVBMT, Université de La Réunion, Saint Pierre, La Réunion, France.,Ecology and Evolution Unit, Okinawa Institute of Science and Technology Graduate University, Kunigami-gun, Okinawa, Japan
| | - Kamila Canale-Tabet
- GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, Castanet Tolosan, France
| | - Benjamin Basso
- Institut de l'abeille (ITSAP), UMT PrADE, Avignon, France
| | | | - Emmanuelle Labarthe
- GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, Castanet Tolosan, France
| | - Olivier Bouchez
- INRA, US 1426, GeT-PlaGe, Genotoul, Castanet-Tolosan, France
| | - Yves Le Conte
- INRA, UR 406 Abeilles et Environnement, UMT PrADE, Avignon, France
| | - Johanna Clémencet
- UMR PVBMT, Université de La Réunion, Saint Pierre, La Réunion, France
| | | | - Alain Vignal
- GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, Castanet Tolosan, France
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16
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Henriques D, Parejo M, Vignal A, Wragg D, Wallberg A, Webster MT, Pinto MA. Developing reduced SNP assays from whole-genome sequence data to estimate introgression in an organism with complex genetic patterns, the Iberian honeybee ( Apis mellifera iberiensis). Evol Appl 2018; 11:1270-1282. [PMID: 30151039 PMCID: PMC6099811 DOI: 10.1111/eva.12623] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 02/11/2018] [Indexed: 01/01/2023] Open
Abstract
The most important managed pollinator, the honeybee (Apis mellifera L.), has been subject to a growing number of threats. In western Europe, one such threat is large-scale introductions of commercial strains (C-lineage ancestry), which is leading to introgressive hybridization and even the local extinction of native honeybee populations (M-lineage ancestry). Here, we developed reduced assays of highly informative SNPs from 176 whole genomes to estimate C-lineage introgression in the most diverse and evolutionarily complex subspecies in Europe, the Iberian honeybee (Apis mellifera iberiensis). We started by evaluating the effects of sample size and sampling a geographically restricted area on the number of highly informative SNPs. We demonstrated that a bias in the number of fixed SNPs (FST = 1) is introduced when the sample size is small (N ≤ 10) and when sampling only captures a small fraction of a population's genetic diversity. These results underscore the importance of having a representative sample when developing reliable reduced SNP assays for organisms with complex genetic patterns. We used a training data set to design four independent SNP assays selected from pairwise FST between the Iberian and C-lineage honeybees. The designed assays, which were validated in holdout and simulated hybrid data sets, proved to be highly accurate and can be readily used for monitoring populations not only in the native range of A. m. iberiensis in Iberia but also in the introduced range in the Balearic islands, Macaronesia and South America, in a time- and cost-effective manner. While our approach used the Iberian honeybee as model system, it has a high value in a wide range of scenarios for the monitoring and conservation of potentially hybridized domestic and wildlife populations.
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Affiliation(s)
- Dora Henriques
- Mountain Research Centre (CIMO)Polytechnic Institute of BragançaBragançaPortugal
- Centre of Molecular and Environmental Biology (CBMA)University of MinhoBragaPortugal
| | - Melanie Parejo
- AgroscopeSwiss Bee Research CentreBernSwitzerland
- Institute of Bee HealthVetsuisse FacultyUniversity of BernBernSwitzerland
| | - Alain Vignal
- GenPhySEUniversité de ToulouseINRAINPTINP‐ENVTCastanet TolosanFrance
| | - David Wragg
- The Roslin InstituteUniversity of EdinburghEdinburghUK
| | - Andreas Wallberg
- Department of Medical Biochemistry and MicrobiologyScience for Life LaboratoryUppsala UniversityUppsalaSweden
| | - Matthew T. Webster
- Department of Medical Biochemistry and MicrobiologyScience for Life LaboratoryUppsala UniversityUppsalaSweden
| | - M. Alice Pinto
- Mountain Research Centre (CIMO)Polytechnic Institute of BragançaBragançaPortugal
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17
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Parejo M, Wragg D, Henriques D, Vignal A, Neuditschko M. Genome-wide scans between two honeybee populations reveal putative signatures of human-mediated selection. Anim Genet 2017; 48:704-707. [PMID: 28872253 PMCID: PMC5697678 DOI: 10.1111/age.12599] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/05/2017] [Indexed: 12/26/2022]
Abstract
Human‐mediated selection has left signatures in the genomes of many domesticated animals, including the European dark honeybee, Apis mellifera mellifera, which has been selected by apiculturists for centuries. Using whole‐genome sequence information, we investigated selection signatures in spatially separated honeybee subpopulations (Switzerland, n = 39 and France, n = 17). Three different test statistics were calculated in windows of 2 kb (fixation index, cross‐population extended haplotype homozygosity and cross‐population composite likelihood ratio) and combined into a recently developed composite selection score. Applying a stringent false discovery rate of 0.01, we identified six significant selective sweeps distributed across five chromosomes covering eight genes. These genes are associated with multiple molecular and biological functions, including regulation of transcription, receptor binding and signal transduction. Of particular interest is a selection signature on chromosome 1, which corresponds to the WNT4 gene, the family of which is conserved across the animal kingdom with a variety of functions. In Drosophila melanogaster, WNT4 alleles have been associated with differential wing, cross vein and abdominal phenotypes. Defining phenotypic characteristics of different Apis mellifera ssp., which are typically used as selection criteria, include colour and wing venation pattern. This signal is therefore likely to be a good candidate for human mediated‐selection arising from different applied breeding practices in the two managed populations.
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Affiliation(s)
- M Parejo
- Agroscope, Swiss Bee Research Centre, 3003, Bern, Switzerland.,Institute of Bee Health, Vetsuisse Faculty, University of Bern, 3003, Bern, Switzerland
| | - D Wragg
- Institut National de la Recherche Agronomique, 31326, Castanet-Tolosan, France.,The Roslin Institute, University of Edinburgh, EH25 9RG, Edinburgh, UK
| | - D Henriques
- Mountain Research Centre (CIMO), Polytechnic Institute of Bragança, 5301-855, Bragança, Portugal
| | - A Vignal
- Institut National de la Recherche Agronomique, 31326, Castanet-Tolosan, France
| | - M Neuditschko
- Agroscope, Swiss Bee Research Centre, 3003, Bern, Switzerland
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18
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Parejo M, Wragg D, Gauthier L, Vignal A, Neumann P, Neuditschko M. Using Whole-Genome Sequence Information to Foster Conservation Efforts for the European Dark Honey Bee, Apis mellifera mellifera. Front Ecol Evol 2016. [DOI: 10.3389/fevo.2016.00140] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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19
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Romé H, Varenne A, Hérault F, Chapuis H, Alleno C, Dehais P, Vignal A, Burlot T, Le Roy P. GWAS analyses reveal QTL in egg layers that differ in response to diet differences. Genet Sel Evol 2015; 47:83. [PMID: 26482360 PMCID: PMC4617898 DOI: 10.1186/s12711-015-0160-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 10/06/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The genetic architecture of egg production and egg quality traits, i.e. the quantitative trait loci (QTL) that influence these traits, is still poorly known. To date, 33 studies have focused on the detection of QTL for laying traits in chickens, but less than 10 genes have been identified. The availability of a high-density SNP (single nucleotide polymorphism) chicken array developed by Affymetrix, i.e. the 600K Affymetrix(®) Axiom(®) HD genotyping array offers the possibility to narrow down the localization of previously detected QTL and to detect new QTL. This high-density array is also anticipated to take research beyond the classical hypothesis of additivity of QTL effects or of QTL and environmental effects. The aim of our study was to search for QTL that influence laying traits using the 600K SNP chip and to investigate whether the effects of these QTL differed between diets and age at egg collection. RESULTS One hundred and thirty-one QTL were detected for 16 laying traits and were spread across all marked chromosomes, except chromosomes 16 and 25. The percentage of variance explained by a QTL varied from 2 to 10 % for the various traits, depending on diet and age at egg collection. Chromosomes 3, 9, 10 and Z were overrepresented, with more than eight QTL on each one. Among the 131 QTL, 60 had a significantly different effect, depending on diet or age at egg collection. For egg production traits, when the QTL × environment interaction was significant, numerous inversions of sign of the SNP effects were observed, whereas for egg quality traits, the QTL × environment interaction was mostly due to a difference of magnitude of the SNP effects. CONCLUSIONS Our results show that numerous QTL influence egg production and egg quality traits and that the genomic regions, which are involved in shaping the ability of layer chickens to adapt to their environment for egg production, vary depending on the environmental conditions. The next question will be to address what the impact of these genotype × environment interactions is on selection.
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Affiliation(s)
- Hélène Romé
- INRA, UMR1348 PEGASE, Domaine de La Prise, 35590, Saint-Gilles, France. .,Agrocampus Ouest, UMR1348 PEGASE, 65 Rue de Saint Brieuc, 35042, Rennes, France.
| | | | - Frédéric Hérault
- INRA, UMR1348 PEGASE, Domaine de La Prise, 35590, Saint-Gilles, France. .,Agrocampus Ouest, UMR1348 PEGASE, 65 Rue de Saint Brieuc, 35042, Rennes, France.
| | - Hervé Chapuis
- SYSAAF, INRA UR83 Recherches Avicoles, 37380, Nouzilly, France.
| | - Christophe Alleno
- Zootests, Parc Technologique Du Zoopôle, 5 Rue Gabriel Calloet Kerbrat, 22440, Ploufragan, France.
| | - Patrice Dehais
- INRA, UMR1388 GenPhySe, Auzeville BP52627, 31326, Castanet-Tolosan, France.
| | - Alain Vignal
- INRA, UMR1388 GenPhySe, Auzeville BP52627, 31326, Castanet-Tolosan, France.
| | | | - Pascale Le Roy
- INRA, UMR1348 PEGASE, Domaine de La Prise, 35590, Saint-Gilles, France. .,Agrocampus Ouest, UMR1348 PEGASE, 65 Rue de Saint Brieuc, 35042, Rennes, France.
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20
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Recoquillay J, Pitel F, Arnould C, Leroux S, Dehais P, Moréno C, Calandreau L, Bertin A, Gourichon D, Bouchez O, Vignal A, Fariello MI, Minvielle F, Beaumont C, Leterrier C, Le Bihan-Duval E. A medium density genetic map and QTL for behavioral and production traits in Japanese quail. BMC Genomics 2015; 16:10. [PMID: 25609057 PMCID: PMC4307178 DOI: 10.1186/s12864-014-1210-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Accepted: 12/30/2014] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Behavioral traits such as sociability, emotional reactivity and aggressiveness are major factors in animal adaptation to breeding conditions. In order to investigate the genetic control of these traits as well as their relationships with production traits, a study was undertaken on a large second generation cross (F2) between two lines of Japanese Quail divergently selected on their social reinstatement behavior. All the birds were measured for several social behaviors (social reinstatement, response to social isolation, sexual motivation, aggression), behaviors measuring the emotional reactivity of the birds (reaction to an unknown object, tonic immobility reaction), and production traits (body weight and egg production). RESULTS We report the results of the first genome-wide QTL detection based on a medium density SNP panel obtained from whole genome sequencing of a pool of individuals from each divergent line. A genetic map was constructed using 2145 markers among which 1479 could be positioned on 28 different linkage groups. The sex-averaged linkage map spanned a total of 3057 cM with an average marker spacing of 2.1 cM. With the exception of a few regions, the marker order was the same in Japanese Quail and the chicken, which confirmed a well conserved synteny between the two species. The linkage analyses performed using QTLMAP software revealed a total of 45 QTLs related either to behavioral (23) or production (22) traits. The most numerous QTLs (15) concerned social motivation traits. Interestingly, our results pinpointed putative pleiotropic regions which controlled emotional reactivity and body-weight of birds (on CJA5 and CJA8) or their social motivation and the onset of egg laying (on CJA19). CONCLUSION This study identified several QTL regions for social and emotional behaviors in the Quail. Further research will be needed to refine the QTL and confirm or refute the role of candidate genes, which were suggested by bioinformatics analysis. It can be hoped that the identification of genes and polymorphisms related to behavioral traits in the quail will have further applications for other poultry species (especially the chicken) and will contribute to solving animal welfare issues in poultry production.
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Affiliation(s)
| | - Frédérique Pitel
- UMR INRA/Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENSAT / Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENVT Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
| | - Cécile Arnould
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, F-37380, Nouzilly, France.
- CNRS, UMR7247, F-37380, Nouzilly, France.
- Université François Rabelais de Tours, F-37000, Tours, France.
- IFCE, F-37380, Nouzilly, France.
| | - Sophie Leroux
- UMR INRA/Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENSAT / Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENVT Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
| | - Patrice Dehais
- UMR INRA/Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENSAT / Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENVT Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INRA, Sigenae UR875 Biométrie et Intelligence Artificielle, F-31326, Castanet-Tolosan, France.
| | - Carole Moréno
- UMR INRA/Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENSAT / Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENVT Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
| | - Ludovic Calandreau
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, F-37380, Nouzilly, France.
- CNRS, UMR7247, F-37380, Nouzilly, France.
- Université François Rabelais de Tours, F-37000, Tours, France.
- IFCE, F-37380, Nouzilly, France.
| | - Aline Bertin
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, F-37380, Nouzilly, France.
- CNRS, UMR7247, F-37380, Nouzilly, France.
- Université François Rabelais de Tours, F-37000, Tours, France.
- IFCE, F-37380, Nouzilly, France.
| | - David Gourichon
- UE1295 Pôle d'Expérimentation Avicole de Tours, F-37380, Nouzilly, France.
| | - Olivier Bouchez
- UMR INRA/Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENSAT / Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENVT Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INRA, GeT-PlaGe Genotoul, F-31326, Castanet-Tolosan, France.
| | - Alain Vignal
- UMR INRA/Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENSAT / Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENVT Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
| | - Maria Ines Fariello
- UMR INRA/Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENSAT / Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- INPT ENVT Génétique Physiologie et Systèmes d'Elevage, INRA, F-31326, Castanet-Tolosan, France.
- Institut Pasteur, Montevideo, Uruguay.
| | - Francis Minvielle
- INRA, UMR1313 GABI Génétique Animale et Biologie Intégrative, F-78530, Jouy-en-Josas, France.
| | | | - Christine Leterrier
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, F-37380, Nouzilly, France.
- CNRS, UMR7247, F-37380, Nouzilly, France.
- Université François Rabelais de Tours, F-37000, Tours, France.
- IFCE, F-37380, Nouzilly, France.
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21
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François Y, Marie-Etancelin C, Vignal A, Viala D, Davail S, Molette C. Mule duck "foie gras" shows different metabolic states according to its quality phenotype by using a proteomic approach. J Agric Food Chem 2014; 62:7140-7150. [PMID: 24976256 DOI: 10.1021/jf5006963] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
This study aimed at identifying the mechanisms implicated in "foie gras" quality variability through the study of the relationships between liver protein compositions and four liver quality phenotypes: liver weight, melting rate, and protein contents on crude or dry matter. Spots of soluble proteins were separated by bidimensional electrophoresis, and the relative abundance of proteins according to quality traits values was investigated. Twenty-three protein spots (19 unique identified proteins) showed different levels of abundance according to one or more of the traits' values. These abundance differences highlighted two groups of livers with opposite trends of abundance levels. Proteins of the first group, associated with low liver weight and melting rate, are involved in synthesis and anabolism processes, whereas proteins of the second group, associated with high liver weight and melting rate, are proteins involved in stress response. Altogether, these results highlight the variations in metabolic states underlying foie gras quality traits.
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Affiliation(s)
- Yoannah François
- Université de Pau et des Pays de l'Adour, UMR5254 Institut Pluridisciplinaire de Recherche sur l'Environnement et les Matériaux - Equipe Environnement et Microbiologie (IPREM-EEM), 40004 Mont de Marsan Cedex, France
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22
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Lee MO, Yang E, Morisson M, Vignal A, Huang YZ, Cheng HH, Muir WM, Lamont SJ, Lillehoj HS, Lee SH, Womack JE. Mapping and genotypic analysis of the NK-lysin gene in chicken. Genet Sel Evol 2014; 46:43. [PMID: 25001618 PMCID: PMC4120735 DOI: 10.1186/1297-9686-46-43] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Accepted: 05/13/2014] [Indexed: 11/10/2022] Open
Abstract
Background Antimicrobial peptides (AMP) are important elements of the first line of defence against pathogens in animals. NK-lysin is a cationic AMP that plays a critical role in innate immunity. The chicken NK-lysin gene has been cloned and its antimicrobial and anticancer activity has been described but its location in the chicken genome remains unknown. Here, we mapped the NK-lysin gene and examined the distribution of a functionally significant single nucleotide polymorphism (SNP) among different chicken inbred lines and heritage breeds. Results A 6000 rad radiation hybrid panel (ChickRH6) was used to map the NK-lysin gene to the distal end of chromosome 22. Two additional genes, the adipocyte enhancer-binding protein 1-like gene (AEBP1) and the DNA polymerase delta subunit 2-like (POLD2) gene, are located in the same NW_003779909 contig as NK-lysin, and were thus indirectly mapped to chromosome 22 as well. Previously, we reported a functionally significant SNP at position 271 of the NK-lysin coding sequence in two different chicken breeds. Here, we examined this SNP and found that the A allele appears to be more common than the G allele in these heritage breeds and inbred lines. Conclusions The chicken NK-lysin gene mapped to the distal end of chromosome 22. Two additional genes, AEBP1 and POLD2, were indirectly mapped to chromosome 22 also. SNP analyses revealed that the A allele, which encodes a peptide with a higher antimicrobial activity, is more common than the G allele in our tested inbred lines and heritage breeds.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - James E Womack
- Department of Veterinary Pathobiology, Texas A & M University, College Station, TX 77843, USA.
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23
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Frésard L, Leroux S, Servin B, Gourichon D, Dehais P, Cristobal MS, Marsaud N, Vignoles F, Bed'hom B, Coville JL, Hormozdiari F, Beaumont C, Zerjal T, Vignal A, Morisson M, Lagarrigue S, Pitel F. Transcriptome-wide investigation of genomic imprinting in chicken. Nucleic Acids Res 2014; 42:3768-82. [PMID: 24452801 PMCID: PMC3973300 DOI: 10.1093/nar/gkt1390] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Genomic imprinting is an epigenetic mechanism by which alleles of some specific genes are expressed in a parent-of-origin manner. It has been observed in mammals and marsupials, but not in birds. Until now, only a few genes orthologous to mammalian imprinted ones have been analyzed in chicken and did not demonstrate any evidence of imprinting in this species. However, several published observations such as imprinted-like QTL in poultry or reciprocal effects keep the question open. Our main objective was thus to screen the entire chicken genome for parental-allele-specific differential expression on whole embryonic transcriptomes, using high-throughput sequencing. To identify the parental origin of each observed haplotype, two chicken experimental populations were used, as inbred and as genetically distant as possible. Two families were produced from two reciprocal crosses. Transcripts from 20 embryos were sequenced using NGS technology, producing ∼200 Gb of sequences. This allowed the detection of 79 potentially imprinted SNPs, through an analysis method that we validated by detecting imprinting from mouse data already published. However, out of 23 candidates tested by pyrosequencing, none could be confirmed. These results come together, without a priori, with previous statements and phylogenetic considerations assessing the absence of genomic imprinting in chicken.
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Affiliation(s)
- Laure Frésard
- INRA, UMR444 Laboratoire de Génétique Cellulaire, Castanet-Tolosan F-31326, France, ENVT, UMR444 Laboratoire de Génétique Cellulaire, Toulouse F-31076, France, INRA, PEAT Pôle d'Expérimentation Avicole de Tours, Nouzilly F- 37380, France, INRA, Sigenae UR875 Biométrie et Intelligence Artificielle, Castanet-Tolosan F-31326, France, INRA, GeT-PlaGe Genotoul, Castanet-Tolosan F-31326, France, INRA, UMR1313 Génétique animale et biologie intégrative, Jouy en Josas F-78350, France, AgroParisTech, UMR1313 Génétique animale et biologie intégrative, Jouy en Josas F-78350, France, Department of Computer Sciences, University of California, Los Angeles, CA 90095, USA, INRA, UR83 Recherche Avicoles, Nouzilly F- 37380, France and Agrocampus Ouest, UMR1348 Physiologie, Environnement et Génétique pour l'Animal et les Systèmes d'Élevage, Animal Genetics Laboratory, Rennes F-35000, France
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24
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Recoquillay J, Leterrier C, Calandreau L, Bertin A, Pitel F, Gourichon D, Vignal A, Beaumont C, Le Bihan-Duval E, Arnould C. Evidence of phenotypic and genetic relationships between sociality, emotional reactivity and production traits in Japanese quail. PLoS One 2013; 8:e82157. [PMID: 24324761 PMCID: PMC3852745 DOI: 10.1371/journal.pone.0082157] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 10/22/2013] [Indexed: 11/23/2022] Open
Abstract
The social behavior of animals, which is partially controlled by genetics, is one of the factors involved in their adaptation to large breeding groups. To understand better the relationships between different social behaviors, fear behaviors and production traits, we analyzed the phenotypic and genetic correlations of these traits in Japanese quail by a second generation crossing of two lines divergently selected for their social reinstatement behavior. Analyses of results for 900 individuals showed that the phenotypic correlations between behavioral traits were low with the exception of significant correlations between sexual behavior and aggressive pecks both at phenotypic (0.51) and genetic (0.90) levels. Significant positive genetic correlations were observed between emotional reactivity toward a novel object and sexual (0.89) or aggressive (0.63) behaviors. The other genetic correlations were observed mainly between behavioral and production traits. Thus, the level of emotional reactivity, estimated by the duration of tonic immobility, was positively correlated with weight at 17 and 65 days of age (0.76 and 0.79, respectively) and with delayed egg laying onset (0.74). In contrast, a higher level of social reinstatement behavior was associated with an earlier egg laying onset (-0.71). In addition, a strong sexual motivation was correlated with an earlier laying onset (-0.68) and a higher number of eggs laid (0.82). A low level of emotional reactivity toward a novel object and also a higher aggressive behavior were genetically correlated with a higher number of eggs laid (0.61 and 0.58, respectively). These results bring new insights into the complex determinism of social and emotional reactivity behaviors in birds and their relationships with production traits. Furthermore, they highlight the need to combine animal welfare and production traits in selection programs by taking into account traits of sociability and emotional reactivity.
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Affiliation(s)
| | - Christine Leterrier
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, Nouzilly, France
- CNRS, UMR7247, Nouzilly, France
- Université François Rabelais de Tours, Tours, France
- IFCE, Nouzilly, France
| | - Ludovic Calandreau
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, Nouzilly, France
- CNRS, UMR7247, Nouzilly, France
- Université François Rabelais de Tours, Tours, France
- IFCE, Nouzilly, France
| | - Aline Bertin
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, Nouzilly, France
- CNRS, UMR7247, Nouzilly, France
- Université François Rabelais de Tours, Tours, France
- IFCE, Nouzilly, France
| | - Frédérique Pitel
- INRA-ENVT, UMR444 Génétique Cellulaire, Castanet-Tolosan, France
| | - David Gourichon
- UE1295 Pôle d’Expérimentation Avicole de Tours, Nouzilly, France
| | - Alain Vignal
- INRA-ENVT, UMR444 Génétique Cellulaire, Castanet-Tolosan, France
| | | | | | - Cécile Arnould
- INRA, UMR85 Physiologie de la Reproduction et des Comportements, Nouzilly, France
- CNRS, UMR7247, Nouzilly, France
- Université François Rabelais de Tours, Tours, France
- IFCE, Nouzilly, France
- * E-mail:
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25
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Huang Y, Li Y, Burt DW, Chen H, Zhang Y, Qian W, Kim H, Gan S, Zhao Y, Li J, Yi K, Feng H, Zhu P, Li B, Liu Q, Fairley S, Magor KE, Du Z, Hu X, Goodman L, Tafer H, Vignal A, Lee T, Kim KW, Sheng Z, An Y, Searle S, Herrero J, Groenen MAM, Crooijmans RPMA, Faraut T, Cai Q, Webster RG, Aldridge JR, Warren WC, Bartschat S, Kehr S, Marz M, Stadler PF, Smith J, Kraus RHS, Zhao Y, Ren L, Fei J, Morisson M, Kaiser P, Griffin DK, Rao M, Pitel F, Wang J, Li N. The duck genome and transcriptome provide insight into an avian influenza virus reservoir species. Nat Genet 2013; 45:776-783. [PMID: 23749191 PMCID: PMC4003391 DOI: 10.1038/ng.2657] [Citation(s) in RCA: 242] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2012] [Accepted: 05/08/2013] [Indexed: 12/19/2022]
Abstract
The duck (Anas platyrhynchos) is one of the principal natural hosts of influenza A viruses. We present the duck genome sequence and perform deep transcriptome analyses to investigate immune-related genes. Our data indicate that the duck possesses a contractive immune gene repertoire, as in chicken and zebra finch, and this repertoire has been shaped through lineage-specific duplications. We identify genes that are responsive to influenza A viruses using the lung transcriptomes of control ducks and ones that were infected with either a highly pathogenic (A/duck/Hubei/49/05) or a weakly pathogenic (A/goose/Hubei/65/05) H5N1 virus. Further, we show how the duck's defense mechanisms against influenza infection have been optimized through the diversification of its β-defensin and butyrophilin-like repertoires. These analyses, in combination with the genomic and transcriptomic data, provide a resource for characterizing the interaction between host and influenza viruses.
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Affiliation(s)
- Yinhua Huang
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China.,The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
| | | | - David W Burt
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
| | - Hualan Chen
- National Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Harbin, China
| | | | | | - Heebal Kim
- Department of Agricultural Biotechnology, Seoul National University, Seoul, Korea
| | - Shangquan Gan
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Yiqiang Zhao
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | | | - Kang Yi
- BGI-Shenzhen, Shenzhen, China
| | - Huapeng Feng
- National Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Harbin, China
| | - Pengyang Zhu
- National Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Harbin, China
| | - Bo Li
- BGI-Shenzhen, Shenzhen, China
| | - Qiuyue Liu
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Suan Fairley
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Katharine E Magor
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Zhenlin Du
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Xiaoxiang Hu
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | | | - Hakim Tafer
- Department of Computer Science, University of Leipzig, Leipzig, Germany.,Department of Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Alain Vignal
- Laboratoire de Génétique Cellulaire, Institut National de la Recherche Agronomique (INRA), Castanet-Tolosan, France
| | - Taeheon Lee
- Department of Agricultural Biotechnology, Seoul National University, Seoul, Korea
| | - Kyu-Won Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Zheya Sheng
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Yang An
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Steve Searle
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Javier Herrero
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | - Martien A M Groenen
- Animal Breeding and Genomics Centre, Wageningen University, Wageningen, The Netherlands
| | | | - Thomas Faraut
- Laboratoire de Génétique Cellulaire, Institut National de la Recherche Agronomique (INRA), Castanet-Tolosan, France
| | | | - Robert G Webster
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Jerry R Aldridge
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Wesley C Warren
- The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA
| | | | - Stephanie Kehr
- Department of Computer Science, University of Leipzig, Leipzig, Germany
| | - Manja Marz
- Department of Computer Science, University of Leipzig, Leipzig, Germany
| | - Peter F Stadler
- Department of Computer Science, University of Leipzig, Leipzig, Germany.,Department of Theoretical Chemistry, University of Vienna, Vienna, Austria
| | - Jacqueline Smith
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
| | - Robert H S Kraus
- Resource Ecology Group, Wageningen University, Wageningen, The Netherlands.,Conservation Genetics Group, Senckenberg Research Institute and Natural History Museum, Gelnhausen, Germany
| | - Yaofeng Zhao
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Liming Ren
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Jing Fei
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Mireille Morisson
- Laboratoire de Génétique Cellulaire, Institut National de la Recherche Agronomique (INRA), Castanet-Tolosan, France
| | - Pete Kaiser
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK
| | | | - Man Rao
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
| | - Frederique Pitel
- Laboratoire de Génétique Cellulaire, Institut National de la Recherche Agronomique (INRA), Castanet-Tolosan, France
| | - Jun Wang
- BGI-Shenzhen, Shenzhen, China.,Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Ning Li
- State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, China
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26
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Kileh-Wais M, Elsen JM, Vignal A, Feves K, Vignoles F, Fernandez X, Manse H, Davail S, André JM, Bastianelli D, Bonnal L, Filangi O, Baéza E, Guéméné D, Genêt C, Bernadet MD, Dubos F, Marie-Etancelin C. Detection of QTL controlling metabolism, meat quality, and liver quality traits of the overfed interspecific hybrid mule duck. J Anim Sci 2012; 91:588-604. [PMID: 23148259 DOI: 10.2527/jas.2012-5411] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The mule duck, an interspecific hybrid obtained by crossing common duck (Anas platyrhynchos) females with Muscovy (Cairina moschata) drakes, is widely used for fatty liver production. The purpose of the present study was to detect and map single and pleiotropic QTL that segregate in the common duck species, and influence the expression of traits in their overfed mule duck offspring. To this end, we generated a common duck backcross (BC) population by crossing Kaiya and heavy Pekin experimental lines, which differ notably in regard to the BW and overfeeding ability of their mule progeny. The BC females were mated to Muscovy drakes and, on average, 4 male mule ducks hatched per BC female (1600 in total) and were measured for growth, metabolism during growth and the overfeeding period, overfeeding ability, and the quality of their breast meat and fatty liver. The phenotypic value of BC females was estimated for each trait by assigning to each female the mean value of the phenotypes of her offspring. Estimations allowed for variance, which depended on the number of male offspring per BC and the heritability of the trait considered. The genetic map used for QTL detection consisted of 91 microsatellite markers aggregated into 16 linkage groups (LG) covering a total of 778 cM. Twenty-two QTL were found to be significant at the 1% chromosome-wide threshold level using the single-trait detection option of the QTLMap software. Most of the QTL detected were related to the quality of breast meat and fatty liver: QTL for meat pH 20 min post mortem were mapped to LG4 (at the 1% genome-wide significance level), and QTL for meat lipid content and cooking losses were mapped to LG2a. The QTL related to fatty liver weight and liver protein and lipid content were for the most part detected on LG2c and LG9. Multitrait analysis highlighted the pleiotropic effects of QTL in these chromosome regions. Apart from the strong QTL for plasma triglyceride content at the end of the overfeeding period mapped to chromosome Z using single-trait analysis, all metabolic trait QTL were detected with the multitrait approach: the QTL mapped to LG14 and LG21 affected the plasma cholesterol and triglyceride contents, whereas the QTL mapped to LG2a seemed to impact glycemia and the basal plasma corticosterone content. A greater density genetic map will be needed to further fine map the QTL.
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Affiliation(s)
- M Kileh-Wais
- Institut National de la Recherche Agronomique, SAGA Station d'Amélioration Génétique des Animaux, UR631, 31 326 Castanet Tolosan, France
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Frésard L, Leroux S, Dehais P, Servin B, Gilbert H, Bouchez O, Klopp C, Cabau C, Vignoles F, Feve K, Ricros A, Gourichon D, Diot C, Richard S, Leterrier C, Beaumont C, Vignal A, Minvielle F, Pitel F. Fine mapping of complex traits in non-model species: using next generation sequencing and advanced intercross lines in Japanese quail. BMC Genomics 2012; 13:551. [PMID: 23066875 PMCID: PMC3534603 DOI: 10.1186/1471-2164-13-551] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2012] [Accepted: 10/08/2012] [Indexed: 11/16/2022] Open
Abstract
Background As for other non-model species, genetic analyses in quail will benefit greatly from a higher marker density, now attainable thanks to the evolution of sequencing and genotyping technologies. Our objective was to obtain the first genome wide panel of Japanese quail SNP (Single Nucleotide Polymorphism) and to use it for the fine mapping of a QTL for a fear-related behaviour, namely tonic immobility, previously localized on Coturnix japonica chromosome 1. To this aim, two reduced representations of the genome were analysed through high-throughput 454 sequencing: AFLP (Amplified Fragment Length Polymorphism) fragments as representatives of genomic DNA, and EST (Expressed Sequence Tag) as representatives of the transcriptome. Results The sequencing runs produced 399,189 and 1,106,762 sequence reads from cDNA and genomic fragments, respectively. They covered over 434 Mb of sequence in total and allowed us to detect 17,433 putative SNP. Among them, 384 were used to genotype two Advanced Intercross Lines (AIL) obtained from three quail lines differing for duration of tonic immobility. Despite the absence of genotyping for founder individuals in the analysis, the previously identified candidate region on chromosome 1 was refined and led to the identification of a candidate gene. Conclusions These data confirm the efficiency of transcript and AFLP-sequencing for SNP discovery in a non-model species, and its application to the fine mapping of a complex trait. Our results reveal a significant association of duration of tonic immobility with a genomic region comprising the DMD (dystrophin) gene. Further characterization of this candidate gene is needed to decipher its putative role in tonic immobility in Coturnix.
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Affiliation(s)
- Laure Frésard
- INRA, UMR444 Laboratoire de Génétique Cellulaire, Castanet-Tolosan, F-31326, France
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Rao M, Morisson M, Faraut T, Bardes S, Fève K, Labarthe E, Fillon V, Huang Y, Li N, Vignal A. A duck RH panel and its potential for assisting NGS genome assembly. BMC Genomics 2012; 13:513. [PMID: 23020625 PMCID: PMC3496577 DOI: 10.1186/1471-2164-13-513] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Accepted: 08/29/2012] [Indexed: 11/13/2022] Open
Abstract
Background Owing to the low cost of the high throughput Next Generation Sequencing (NGS) technology, more and more species have been and will be sequenced. However, de novo assemblies of large eukaryotic genomes thus produced are composed of a large number of contigs and scaffolds of medium to small size, having no chromosomal assignment. Radiation hybrid (RH) mapping is a powerful tool for building whole genome maps and has been used for several animal species, to help assign sequence scaffolds to chromosomes and determining their order. Results We report here a duck whole genome RH panel obtained by fusing female duck embryonic fibroblasts irradiated at a dose of 6,000 rads, with HPRT-deficient Wg3hCl2 hamster cells. The ninety best hybrids, having an average retention of 23.6% of the duck genome, were selected for the final panel. To allow the genotyping of large numbers of markers, as required for whole genome mapping, without having to cultivate the hybrid clones on a large scale, three different methods involving Whole Genome Amplification (WGA) and/or scaling down PCR volumes by using the Fluidigm BioMarkTM Integrated Fluidic Circuits (IFC) Dynamic ArrayTM for genotyping were tested. RH maps of APL12 and APL22 were built, allowing the detection of intrachromosomal rearrangements when compared to chicken. Finally, the panel proved useful for checking the assembly of sequence scaffolds and for mapping EST located on one of the smallest microchromosomes. Conclusion The Fluidigm BioMarkTM Integrated Fluidic Circuits (IFC) Dynamic ArrayTM genotyping by quantitative PCR provides a rapid and cost-effective method for building RH linkage groups. Although the vast majority of genotyped markers exhibited a picture coherent with their associated scaffolds, a few of them were discordant, pinpointing potential assembly errors. Comparative mapping with chicken chromosomes GGA21 and GGA11 allowed the detection of the first chromosome rearrangements on microchromosomes between duck and chicken. As in chicken, the smallest duck microchromosomes appear missing in the assembly and more EST data will be needed for mapping them. Altogether, this underlines the added value of RH mapping to improve genome assemblies.
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Affiliation(s)
- Man Rao
- UMR INRA/ENVT Laboratoire de Génétique Cellulaire, INRA, Castanet-Tolosan 31326, France
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Douaud M, Feve K, Pituello F, Gourichon D, Boitard S, Leguern E, Coquerelle G, Vieaud A, Batini C, Naquet R, Vignal A, Tixier-Boichard M, Pitel F. Epilepsy caused by an abnormal alternative splicing with dosage effect of the SV2A gene in a chicken model. PLoS One 2011; 6:e26932. [PMID: 22046416 PMCID: PMC3203167 DOI: 10.1371/journal.pone.0026932] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Accepted: 10/06/2011] [Indexed: 11/18/2022] Open
Abstract
Photosensitive reflex epilepsy is caused by the combination of an individual's enhanced sensitivity with relevant light stimuli, such as stroboscopic lights or video games. This is the most common reflex epilepsy in humans; it is characterized by the photoparoxysmal response, which is an abnormal electroencephalographic reaction, and seizures triggered by intermittent light stimulation. Here, by using genetic mapping, sequencing and functional analyses, we report that a mutation in the acceptor site of the second intron of SV2A (the gene encoding synaptic vesicle glycoprotein 2A) is causing photosensitive reflex epilepsy in a unique vertebrate model, the Fepi chicken strain, a spontaneous model where the neurological disorder is inherited as an autosomal recessive mutation. This mutation causes an aberrant splicing event and significantly reduces the level of SV2A mRNA in homozygous carriers. Levetiracetam, a second generation antiepileptic drug, is known to bind SV2A, and SV2A knock-out mice develop seizures soon after birth and usually die within three weeks. The Fepi chicken survives to adulthood and responds to levetiracetam, suggesting that the low-level expression of SV2A in these animals is sufficient to allow survival, but does not protect against seizures. Thus, the Fepi chicken model shows that the role of the SV2A pathway in the brain is conserved between birds and mammals, in spite of a large phylogenetic distance. The Fepi model appears particularly useful for further studies of physiopathology of reflex epilepsy, in comparison with induced models of epilepsy in rodents. Consequently, SV2A is a very attractive candidate gene for analysis in the context of both mono- and polygenic generalized epilepsies in humans.
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Affiliation(s)
- Marine Douaud
- INRA-ENVT, Laboratoire de Génétique Cellulaire, Castanet-Tolosan, France
| | - Katia Feve
- INRA-ENVT, Laboratoire de Génétique Cellulaire, Castanet-Tolosan, France
| | - Fabienne Pituello
- CNRS-Université Toulouse III, Centre de Biologie du Développement, Toulouse, France
| | - David Gourichon
- INRA PEAT, Pôle d'Expérimentation Avicole de Tours, Nouzilly, France
| | - Simon Boitard
- INRA-ENVT, Laboratoire de Génétique Cellulaire, Castanet-Tolosan, France
| | - Eric Leguern
- INSERM, Neurogénétique Moléculaire et Cellulaire, Paris, France
| | - Gérard Coquerelle
- INRA, Génétique Animale et Biologie Intégrative, Jouy-en-Josas, France
| | - Agathe Vieaud
- INRA, Génétique Animale et Biologie Intégrative, Jouy-en-Josas, France
| | - Cesira Batini
- CNRS, Laboratoire de Génétique Moléculaire de la Neurotransmission et des Processus Neurodégénératifs, Paris, France
| | - Robert Naquet
- CNRS, Institut de Neurobiologie Alfred Fessard, Gif-sur-Yvette, France
| | - Alain Vignal
- INRA-ENVT, Laboratoire de Génétique Cellulaire, Castanet-Tolosan, France
| | | | - Frédérique Pitel
- INRA-ENVT, Laboratoire de Génétique Cellulaire, Castanet-Tolosan, France
- * E-mail:
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Solinhac R, Leroux S, Galkina S, Chazara O, Feve K, Vignoles F, Morisson M, Derjusheva S, Bed'hom B, Vignal A, Fillon V, Pitel F. Integrative mapping analysis of chicken microchromosome 16 organization. BMC Genomics 2010; 11:616. [PMID: 21050458 PMCID: PMC3091757 DOI: 10.1186/1471-2164-11-616] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2010] [Accepted: 11/04/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The chicken karyotype is composed of 39 chromosome pairs, of which 9 still remain totally absent from the current genome sequence assembly, despite international efforts towards complete coverage. Some others are only very partially sequenced, amongst which microchromosome 16 (GGA16), particularly under-represented, with only 433 kb assembled for a full estimated size of 9 to 11 Mb. Besides the obvious need of full genome coverage with genetic markers for QTL (Quantitative Trait Loci) mapping and major genes identification studies, there is a major interest in the detailed study of this chromosome because it carries the two genetically independent MHC complexes B and Y. In addition, GGA16 carries the ribosomal RNA (rRNA) genes cluster, also known as the NOR (nucleolus organizer region). The purpose of the present study is to construct and present high resolution integrated maps of GGA16 to refine its organization and improve its coverage with genetic markers. RESULTS We developed 79 STS (Sequence Tagged Site) markers to build a physical RH (radiation hybrid) map and 34 genetic markers to extend the genetic map of GGA16. We screened a BAC (Bacterial Artificial Chromosome) library with markers for the MHC-B, MHC-Y and rRNA complexes. Selected clones were used to perform high resolution FISH (Fluorescent In Situ Hybridization) mapping on giant meiotic lampbrush chromosomes, allowing meiotic mapping in addition to the confirmation of the order of the three clusters along the chromosome. A region with high recombination rates and containing PO41 repeated elements separates the two MHC complexes. CONCLUSIONS The three complementary mapping strategies used refine greatly our knowledge of chicken microchromosome 16 organisation. The characterisation of the recombination hotspots separating the two MHC complexes demonstrates the presence of PO41 repetitive sequences both in tandem and inverted orientation. However, this region still needs to be studied in more detail.
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Affiliation(s)
- Romain Solinhac
- UMR INRA/ENVT Laboratoire de Génétique Cellulaire, INRA, Castanet-Tolosan, 31326, France
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Leroux S, Feve K, Vignoles F, Bouchez O, Klopp C, Noirot C, Gourichon D, Richard S, Leterrier C, Beaumont C, Minvielle F, Vignal A, Pitel F. Non PCR-amplified Transcripts and AFLP fragments as reduced representations of the quail genome for 454 Titanium sequencing. BMC Res Notes 2010; 3:214. [PMID: 20667075 PMCID: PMC2919564 DOI: 10.1186/1756-0500-3-214] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2010] [Accepted: 07/28/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND SNP (Single Nucleotide Polymorphism) discovery is now routinely performed using high-throughput sequencing of reduced representation libraries. Our objective was to adapt 454 GS FLX based sequencing methodologies in order to obtain the largest possible dataset from two reduced representations libraries, produced by AFLP (Amplified Fragment Length Polymorphism) for genomic DNA, and EST (Expressed Sequence Tag) for the transcribed fraction of the genome. FINDINGS The expressed fraction was obtained by preparing cDNA libraries without PCR amplification from quail embryo and brain. To optimize the information content for SNP analyses, libraries were prepared from individuals selected in three quail lines and each individual in the AFLP library was tagged. Sequencing runs produced 399,189 sequence reads from cDNA and 373,484 from genomic fragments, covering close to 250 Mb of sequence in total. CONCLUSIONS Both methods used to obtain reduced representations for high-throughput sequencing were successful after several improvements.The protocols may be used for several sequencing applications, such as de novo sequencing, tagged PCR fragments or long fragment sequencing of cDNA.
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Affiliation(s)
- Sophie Leroux
- UMR INRA/ENVT Laboratoire de Génétique Cellulaire, INRA, 31326 Castanet-Tolosan, France.
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Calenge F, Kaiser P, Vignal A, Beaumont C. Genetic control of resistance to salmonellosis and to Salmonella carrier-state in fowl: a review. Genet Sel Evol 2010; 42:11. [PMID: 20429884 PMCID: PMC2873309 DOI: 10.1186/1297-9686-42-11] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2009] [Accepted: 04/29/2010] [Indexed: 12/30/2022] Open
Abstract
Salmonellosis is a frequent disease in poultry stocks, caused by several serotypes of the bacterial species Salmonella enterica and sometimes transmitted to humans through the consumption of contaminated meat or eggs. Symptom-free carriers of the bacteria contribute greatly to the propagation of the disease in poultry stocks. So far, several candidate genes and quantitative trait loci (QTL) for resistance to carrier state or to acute disease have been identified using artificial infection of S. enterica serovar Enteritidis or S. enterica serovar Typhimurium strains in diverse genetic backgrounds, with several different infection procedures and phenotypic assessment protocols. This diversity in experimental conditions has led to a complex sum of results, but allows a more complete description of the disease. Comparisons among studies show that genes controlling resistance to Salmonella differ according to the chicken line studied, the trait assessed and the chicken's age. The loci identified are located on 25 of the 38 chicken autosomal chromosomes. Some of these loci are clustered in several genomic regions, indicating the possibility of a common genetic control for different models. In particular, the genomic regions carrying the candidate genes TLR4 and SLC11A1, the Major Histocompatibility Complex (MHC) and the QTL SAL1 are interesting for more in-depth studies. This article reviews the main Salmonella infection models and chicken lines studied under a historical perspective and then the candidate genes and QTL identified so far.
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Affiliation(s)
- Fanny Calenge
- INRA, UR Unité de Recherches Avicoles, Nouzilly, France.
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Ankra-Badu GA, Shriner D, Le Bihan-Duval E, Mignon-Grasteau S, Pitel F, Beaumont C, Duclos MJ, Simon J, Porter TE, Vignal A, Cogburn LA, Allison DB, Yi N, Aggrey SE. Mapping main, epistatic and sex-specific QTL for body composition in a chicken population divergently selected for low or high growth rate. BMC Genomics 2010; 11:107. [PMID: 20149241 PMCID: PMC2830984 DOI: 10.1186/1471-2164-11-107] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2009] [Accepted: 02/11/2010] [Indexed: 11/30/2022] Open
Abstract
Background Delineating the genetic basis of body composition is important to agriculture and medicine. In addition, the incorporation of gene-gene interactions in the statistical model provides further insight into the genetic factors that underlie body composition traits. We used Bayesian model selection to comprehensively map main, epistatic and sex-specific QTL in an F2 reciprocal intercross between two chicken lines divergently selected for high or low growth rate. Results We identified 17 QTL with main effects across 13 chromosomes and several sex-specific and sex-antagonistic QTL for breast meat yield, thigh + drumstick yield and abdominal fatness. Different sets of QTL were found for both breast muscles [Pectoralis (P) major and P. minor], which suggests that they could be controlled by different regulatory mechanisms. Significant interactions of QTL by sex allowed detection of sex-specific and sex-antagonistic QTL for body composition and abdominal fat. We found several female-specific P. major QTL and sex-antagonistic P. minor and abdominal fatness QTL. Also, several QTL on different chromosomes interact with each other to affect body composition and abdominal fatness. Conclusions The detection of main effects, epistasis and sex-dimorphic QTL suggest complex genetic regulation of somatic growth. An understanding of such regulatory mechanisms is key to mapping specific genes that underlie QTL controlling somatic growth in an avian model.
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Affiliation(s)
- Georgina A Ankra-Badu
- Department of Poultry Science/Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA
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Ankra-Badu GA, Bihan-Duval EL, Mignon-Grasteau S, Pitel F, Beaumont C, Duclos MJ, Simon J, Carré W, Porter TE, Vignal A, Cogburn LA, Aggrey SE. Mapping QTL for growth and shank traits in chickens divergently selected for high or low body weight. Anim Genet 2010; 41:400-5. [DOI: 10.1111/j.1365-2052.2009.02017.x] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Nadaf J, Pitel F, Gilbert H, Duclos MJ, Vignoles F, Beaumont C, Vignal A, Porter TE, Cogburn LA, Aggrey SE, Simon J, Le Bihan-Duval E. QTL for several metabolic traits map to loci controlling growth and body composition in an F2 intercross between high- and low-growth chicken lines. Physiol Genomics 2009; 38:241-9. [DOI: 10.1152/physiolgenomics.90384.2008] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Quantitative trait loci (QTL) for metabolic and body composition traits were mapped at 7 and 9 wk, respectively, in an F2 intercross between high-growth and low-growth chicken lines. These lines also diverged for abdominal fat percentage (AFP) and plasma insulin-like growth factor-I (IGF-I), insulin, and glucose levels. Genotypings were performed with 129 microsatellite markers covering 21 chromosomes. A total of 21 QTL with genomewide level of significance were detected by single-trait analyses for body weight (BW), breast muscle weight (BMW) and percentage (BMP), AF weight (AFW) and percentage (AFP), shank length (ShL) and diameter (ShD), fasting plasma glucose level (Gluc), and body temperature (Tb). Other suggestive QTL were identified for these parameters and for plasma IGF-I and nonesterified fatty acid levels. QTL controlling adiposity and Gluc were colocalized on GGA3 and GGA5 and QTL for BW, ShL and ShD, adiposity, and Tb on GGA4. Multitrait analyses revealed two QTL controlling Gluc and AFP on GGA5 and Gluc and Tb on GGA26. Significant effects of the reciprocal cross were observed on BW, ShD, BMW, and Gluc, which may result from mtDNA and/or maternal effects. Most QTL regions for Gluc and adiposity harbor genes for which alleles have been associated with increased susceptibility to diabetes and/or obesity in humans. Identification of genes responsible for these metabolic QTL will increase our understanding of the constitutive “hyperglycemia” found in chickens. Furthermore, a comparative approach could provide new information on the genetic causes of diabetes and obesity in humans.
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Affiliation(s)
- Javad Nadaf
- Institut National de la Recherche Agronomique (INRA, UR83) Recherches Avicoles, Nouzilly
| | | | - Hélène Gilbert
- INRA, UMR1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, France
| | - Michel J. Duclos
- Institut National de la Recherche Agronomique (INRA, UR83) Recherches Avicoles, Nouzilly
| | | | - Catherine Beaumont
- Institut National de la Recherche Agronomique (INRA, UR83) Recherches Avicoles, Nouzilly
| | - Alain Vignal
- INRA, ENVT, UMR444 Génétique Cellulaire, Castanet-Tolosan
| | - Tom E. Porter
- Department of Animal and Avian Sciences, University of Maryland, College Park, Maryland
| | - Larry A. Cogburn
- Department of Animal and Food Sciences, University of Delaware, Newark, Delaware
| | - Samuel E. Aggrey
- Department of Poultry Science, University of Georgia, Athens, Georgia
| | - Jean Simon
- Institut National de la Recherche Agronomique (INRA, UR83) Recherches Avicoles, Nouzilly
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Calenge F, Lecerf F, Demars J, Feve K, Vignoles F, Pitel F, Vignal A, Velge P, Sellier N, Beaumont C. QTL for resistance to Salmonella carrier state confirmed in both experimental and commercial chicken lines. Anim Genet 2009; 40:590-7. [PMID: 19422366 DOI: 10.1111/j.1365-2052.2009.01884.x] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The ability of chickens to carry Salmonella without displaying disease symptoms is responsible for Salmonella propagation in poultry stocks and for subsequent human contamination through the consumption of contaminated eggs or meat. The selection of animals more resistant to carrier state might be a way to decrease the propagation of Salmonella in poultry stocks and its transmission to humans. Five QTL controlling variation for resistance to carrier state in a chicken F(2) progeny derived from the White Leghorn inbred lines N and 6(1) had been previously identified using a selective genotyping approach. Here, a second analysis on the whole progeny was performed, which led to the confirmation of two QTL on chromosomes 2 and 16. To assess the utility of these genomic regions for selection in commercial lines, we tested them together with other QTL identified in an [Nx6(1)] x N backcross progeny and with the candidate genes SLC11A1 and TLR4. We used a commercial line divergently selected for either low or high carrier-state resistance both in young chicks and in adult hens. In divergent chick lines, one QTL on chromosome 1 and one in the SLC11A1 region were significantly associated with carrier-state resistance variations; in divergent adult lines, one QTL located in the major histocompatibility complex on chromosome 16 and one in the SLC11A1 region were involved in these variations. Genetic studies conducted on experimental lines can therefore be of potential interest for marker-assisted selection in commercial lines.
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Affiliation(s)
- F Calenge
- INRA, Unité de Recherches Avicoles, 37380 Nouzilly, France.
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Faraut T, de Givry S, Hitte C, Lahbib-Mansais Y, Morisson M, Milan D, Schiex T, Servin B, Vignal A, Galibert F, Yerle M. Contribution of Radiation Hybrids to Genome Mapping in Domestic Animals. Cytogenet Genome Res 2009; 126:21-33. [DOI: 10.1159/000245904] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2009] [Indexed: 11/19/2022] Open
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Griffin DK, Robertson LB, Tempest HG, Vignal A, Fillon V, Crooijmans RPMA, Groenen MAM, Deryusheva S, Gaginskaya E, Carré W, Waddington D, Talbot R, Völker M, Masabanda JS, Burt DW. Whole genome comparative studies between chicken and turkey and their implications for avian genome evolution. BMC Genomics 2008; 9:168. [PMID: 18410676 PMCID: PMC2375447 DOI: 10.1186/1471-2164-9-168] [Citation(s) in RCA: 96] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2007] [Accepted: 04/14/2008] [Indexed: 11/25/2022] Open
Abstract
Background Comparative genomics is a powerful means of establishing inter-specific relationships between gene function/location and allows insight into genomic rearrangements, conservation and evolutionary phylogeny. The availability of the complete sequence of the chicken genome has initiated the development of detailed genomic information in other birds including turkey, an agriculturally important species where mapping has hitherto focused on linkage with limited physical information. No molecular study has yet examined conservation of avian microchromosomes, nor differences in copy number variants (CNVs) between birds. Results We present a detailed comparative cytogenetic map between chicken and turkey based on reciprocal chromosome painting and mapping of 338 chicken BACs to turkey metaphases. Two inter-chromosomal changes (both involving centromeres) and three pericentric inversions have been identified between chicken and turkey; and array CGH identified 16 inter-specific CNVs. Conclusion This is the first study to combine the modalities of zoo-FISH and array CGH between different avian species. The first insight into the conservation of microchromosomes, the first comparative cytogenetic map of any bird and the first appraisal of CNVs between birds is provided. Results suggest that avian genomes have remained relatively stable during evolution compared to mammalian equivalents.
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Affiliation(s)
- Darren K Griffin
- Department of Biosciences, University of Kent, Canterbury, Kent, CT2 7NJ, UK.
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Fillon V, Vignoles M, Crooijmans RPMA, Groenen MAM, Zoorob R, Vignal A. FISH mapping of 57 BAC clones reveals strong conservation of synteny between Galliformes and Anseriformes. Anim Genet 2008; 38:303-7. [PMID: 17539975 DOI: 10.1111/j.1365-2052.2007.01578.x] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Karyotypes of chicken (Gallus gallus domesticus; 2n = 78) and mallard duck (Anas platyrhynchos; 2n = 80) share the typical organization of avian karyotypes including a few macrochromosome pairs, numerous indistinguishable microchromosomes, and Z and W sex chromosomes. Previous banding studies revealed great similarities between chickens and ducks, but it was not possible to use comparative banding for the microchromosomes. In order to establish precise chromosome correspondences between these two species, particularly for microchromosomes, we hybridized 57 BAC clones previously assigned to the chicken genome to duck metaphase spreads. Although most of the clones showed similar localizations, we found a few intrachromosomal rearrangements of the macrochromosomes and an additional microchromosome pair in ducks. BAC clones specific for chicken microchromosomes were localized to separate duck microchromosomes and clones mapping to the same chicken microchromosome hybridized to the same duck microchromosome, demonstrating a high conservation of synteny. These results demonstrate that the evolution of karyotypes in avian species is the result of fusion and/or fission processes and not translocations.
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Affiliation(s)
- V Fillon
- Laboratoire de Génétique Cellulaire, INRA de Toulouse-Auzeville, Castanet Tolosan, France.
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Beaumont C, Lecerf F, Protais J, Calenge F, Prevost K, Lalmanach AC, Chapuis H, Pitel F, Burlot T, Sellier N, Fravalo P, Vignal A, Velge P. An integrated approach of genetic resistance to Salmonella carrier state in fowls: from genetics to genomics and modelling. Dev Biol (Basel) 2008; 132:353-357. [PMID: 18817326 DOI: 10.1159/000317185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Increasing resistance to acute Salmonellosis (that is, contamination level shortly after infection) is not sufficient to reduce the risk for consumers to be contaminated by Salmonella. Indeed, animals may remain contaminated at a low level for weeks or months. Increased resistance to the Salmonella carrier state, i.e., animals' ability to clear bacteria, is needed; it involves measuring bacterial contamination several weeks after inoculation with a low dose. To study such resistance traits, three convergent approaches were used. A quantitative trait loci (QTL) study was performed, taking advantage of inbred lines differing in resistance. Several QTLs controlling resistance at a younger age were identified and are currently being confirmed in a new cross before finer mapping, using advanced intercross lines. These inbred lines are also presently being compared using functional genomics. In parallel, a selection experiment for increased or decreased resistance at a younger and a later age was undertaken. Besides providing genetic models differing in their levels of resistance, it underlined the importance of the choice of selection criterion, whether marker assisted or not. Indeed, genes controlling resistance are strongly dependant on age; selecting for resistance at a younger age might result in increased susceptibility at an older age. Finally, the results of this experiment were used in a model of the intra-flock propagation of Salmonella. It showed that introducing a proportion of resistant animals within a flock of susceptible hens could dramatically change the evolution of contamination. Moreover, it demonstrated the magnitude of synergy between selection and vaccination, which should enhance the interest of increased resistance. The results show that selection for increased resistance to the Salmonella carrier state may be efficient, providing that the appropriate criteria of selection are used.
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Morisson M, Denis M, Milan D, Klopp C, Leroux S, Bardes S, Pitel F, Vignoles F, Gérus M, Fillon V, Douaud M, Vignal A. The chicken RH map: current state of progress and microchromosome mapping. Cytogenet Genome Res 2007; 117:14-21. [PMID: 17675840 DOI: 10.1159/000103160] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2006] [Accepted: 07/26/2006] [Indexed: 11/19/2022] Open
Abstract
The ChickRH6 radiation hybrid panel has been used to construct consensus chromosome radiation hybrid (RH) maps of the chicken genome. Markers genotyped were either from throughout the genome or targeted to specific chromosomes and a large proportion (one third) of data was the result of collaborative efforts. Altogether, 2,531 markers were genotyped, allowing the construction of RH reference maps for 20 chromosomes and linkage groups for four other chromosomes. Amongst the markers, 581 belong to the framework maps, while 1,721 are on the comprehensive maps. Around 800 markers still have to be assigned to linkage groups. Our attempt to assign the supercontigs from the chrun (virtual chromosome containing all the genome sequence that could not be attributed to a chromosome) as well as EST (Expressed Sequence Tag) contigs that do not have a BLAST hit in the genome assembly led to the construction of new maps for microchromosomes either absent or for which very little data is present in the genome assembly. RH data is presented through our ChickRH webserver (http://chickrh.toulouse.inra.fr/), which is a mapping tool as well as the official repository RH database for genotypes. It also displays the RH reference maps and comparison charts with the sequence thus highlighting the possible discrepancies. Future improvements of the RH maps include complete coverage of the sequence assigned to chromosomes, further mapping of the chrun and mapping of EST contigs absent from the assembly. This will help finish the mapping of the smallest gene-rich microchromosomes.
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Affiliation(s)
- M Morisson
- INRA, UR444 Laboratoire de Génétique Cellulaire, Castanet-Tolosan, France.
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Granevitze Z, Blum S, Cheng H, Vignal A, Morisson M, Ben-Ari G, David L, Feldman MW, Weigend S, Hillel J. Female-specific DNA sequences in the chicken genome. J Hered 2007; 98:238-42. [PMID: 17395599 DOI: 10.1093/jhered/esm010] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Eight in silico W-specific sequences from the WASHUC1 chicken genome assembly gave female-specific PCR products using chicken DNA. Some of these fragments gave female-specific products with turkey and peacock DNA. Sequence analysis of these 8 fragments (3077 bp total) failed to detect any polymorphisms among 10 divergent chickens. In contrast, comparison of the DNA sequences of chicken with those of turkey and peacock revealed a nucleotide difference every 25 and 28 bp, respectively. Radiation hybrid mapping verified that these amplicons exist only on chromosome W. The homology of 6 W-specific fragments with chromo-helicase-DNA-binding gene and expressed sequenced tags from chicken and other species indicate that these fragments may have or have had a biological function. These fragments may be used for early sexing in commercial chicken and turkey flocks.
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Affiliation(s)
- Zur Granevitze
- Robert H. Smith Institute of Plant Sciences and Genetics, The Hebrew University of Jerusalem, Rehovot 76100, Israel
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Gautron J, Murayama E, Vignal A, Morisson M, McKee MD, Réhault S, Labas V, Belghazi M, Vidal ML, Nys Y, Hincke MT. Cloning of Ovocalyxin-36, a Novel Chicken Eggshell Protein Related to Lipopolysaccharide-binding Proteins, Bactericidal Permeability-increasing Proteins, and Plunc Family Proteins. J Biol Chem 2007; 282:5273-86. [PMID: 17179153 DOI: 10.1074/jbc.m610294200] [Citation(s) in RCA: 93] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
The avian eggshell is a composite biomaterial composed of noncalcifying eggshell membranes and the overlying calcified shell matrix. The shell is deposited in a uterine fluid where the concentration of different protein species varies at different stages of its formation. The role of avian eggshell proteins during shell formation remains poorly understood, and we have sought to identify and characterize the individual components in order to gain insight into their function during elaboration of the eggshell. In this study, we have used direct sequencing, immunochemistry, expression screening, and EST data base mining to clone and characterize a 1995-bp full-length cDNA sequence corresponding to a novel chicken eggshell protein that we have named Ovocalyxin-36 (OCX-36). Ovocalyxin-36 protein was only detected in the regions of the oviduct where egg-shell formation takes place; uterine OCX-36 message was strongly up-regulated during eggshell calcification. OCX-36 localized to the calcified eggshell predominantly in the inner part of the shell, and to the shell membranes. BlastN data base searching indicates that there is no mammalian version of OCX-36; however, the protein sequence is 20-25% homologous to proteins associated with the innate immune response as follows: lipopolysaccharide-binding proteins, bactericidal permeability-increasing proteins, and Plunc family proteins. Moreover, the genomic organization of these proteins and OCX-36 appears to be highly conserved. These observations suggest that OCX-36 is a novel and specific chicken eggshell protein related to the superfamily of lipopolysaccharide-binding proteins/bactericidal permeability-increasing proteins and Plunc proteins. OCX-36 may therefore participate in natural defense mechanisms that keep the egg free of pathogens.
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Affiliation(s)
- Joël Gautron
- Institut National de la Recherche Agronomique, UR83 Unité de Recherches Avicoles, F-37380 Nouzilly, France.
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Galkina S, Deryusheva S, Fillon V, Vignal A, Crooijmans R, Groenen M, Rodionov A, Gaginskaya E. FISH on avian lampbrush chromosomes produces higher resolution gene mapping. Genetica 2006; 128:241-51. [PMID: 17028954 DOI: 10.1007/s10709-005-5776-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2005] [Accepted: 12/07/2005] [Indexed: 10/24/2022]
Abstract
Giant lampbrush chromosomes, which are characteristic of the diplotene stage of prophase I during avian oogenesis, represent a very promising system for precise physical gene mapping. We applied 35 chicken BAC and 4 PAC clones to both mitotic metaphase chromosomes and meiotic lampbrush chromosomes of chicken (Gallus gallus domesticus) and Japanese quail (Coturnix coturnix japonica). Fluorescence in situ hybridization (FISH) mapping on lampbrush chromosomes allowed us to distinguish closely located probes and revealed gene order more precisely. Our data extended the data earlier obtained using FISH to chicken and quail metaphase chromosomes 1-6 and Z. Extremely low levels of inter- and intra-chromosomal rearrangements in the chicken and Japanese quail were demonstrated again. Moreover, we did not confirm the presence of a pericentric inversion in Japanese quail chromosome 4 as compared to chicken chromosome 4. Twelve BAC clones specific for chicken chromosome 4p and 4q showed the same order in quail as in chicken when FISH was performed on lampbrush chromosomes. The centromeres of chicken and quail chromosomes 4 seem to have formed independently after centric fusion of ancestral chromosome 4 and a microchromosome.
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Affiliation(s)
- Svetlana Galkina
- Biological Research Institute, Saint-Petersburg State University, Oranienbaumskoie shosse 2, Stary Peterhof, 198504, Saint-Petersburg, Russia
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Minvielle F, Kayang BB, Inoue-Murayama M, Miwa M, Vignal A, Gourichon D, Neau A, Monvoisin JL, Ito SI. Search for QTL affecting the shape of the egg laying curve of the Japanese quail. BMC Genet 2006; 7:26. [PMID: 16677378 PMCID: PMC1473198 DOI: 10.1186/1471-2156-7-26] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2006] [Accepted: 05/05/2006] [Indexed: 11/21/2022] Open
Abstract
Background Egg production is of critical importance in birds not only for their reproduction but also for human consumption as the egg is a highly nutritive and balanced food. Consequently, laying in poultry has been improved through selection to increase the total number of eggs laid per hen. This number is the cumulative result of the oviposition, a cyclic and repeated process which leads to a pattern over time (the egg laying curve) which can be modelled and described individually. Unlike the total egg number which compounds all variations, the shape of the curve gives information on the different phases of egg laying, and its genetic analysis using molecular markers might contribute to understand better the underlying mechanisms. The purpose of this study was to perform the first QTL search for traits involved in shaping the egg laying curve, in an F2 experiment with 359 female Japanese quail. Results Eight QTL were found on five autosomes, and six of them could be directly associated with egg production traits, although none was significant at the genome-wide level. One of them (on CJA13) had an effect on the first part of the laying curve, before the production peak. Another one (on CJA06) was related to the central part of the curve when laying is maintained at a high level, and the four others (on CJA05, CJA10 and CJA14) acted on the last part of the curve where persistency is determinant. The QTL for the central part of the curve was mapped at the same position on CJA06 than a genome-wide significant QTL for total egg number detected previously in the same F2. Conclusion Despite its limited scope (number of microsatellites, size of the phenotypic data set), this work has shown that it was possible to use the individual egg laying data collected daily to find new QTL which affect the shape of the egg laying curve. Beyond the present results, this new approach could also be applied to longitudinal traits in other species, like growth and lactation in ruminants, for which good marker coverage of the genome and theoretical models with a biological significance are available.
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Affiliation(s)
- Francis Minvielle
- Génétique et Diversité Animales, Institut National de la Recherche Agronomique, Centre de Jouy, 78352 Jouy-en-Josas, France.
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Kayang BB, Fillon V, Inoue-Murayama M, Miwa M, Leroux S, Fève K, Monvoisin JL, Pitel F, Vignoles M, Mouilhayrat C, Beaumont C, Ito S, Minvielle F, Vignal A. Integrated maps in quail (Coturnix japonica) confirm the high degree of synteny conservation with chicken (Gallus gallus) despite 35 million years of divergence. BMC Genomics 2006; 7:101. [PMID: 16669996 PMCID: PMC1534036 DOI: 10.1186/1471-2164-7-101] [Citation(s) in RCA: 63] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2005] [Accepted: 05/02/2006] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND By comparing the quail genome with that of chicken, chromosome rearrangements that have occurred in these two galliform species over 35 million years of evolution can be detected. From a more practical point of view, the definition of conserved syntenies helps to predict the position of genes in quail, based on information taken from the chicken sequence, thus enhancing the utility of this species in biological studies through a better knowledge of its genome structure. A microsatellite and an Amplified Fragment Length Polymorphism (AFLP) genetic map were previously published for quail, as well as comparative cytogenetic data with chicken for macrochromosomes. Quail genomics will benefit from the extension and the integration of these maps. RESULTS The integrated linkage map presented here is based on segregation analysis of both anonymous markers and functional gene loci in 1,050 quail from three independent F2 populations. Ninety-two loci are resolved into 14 autosomal linkage groups and a Z chromosome-specific linkage group, aligned with the quail AFLP map. The size of linkage groups ranges from 7.8 cM to 274.8 cM. The total map distance covers 904.3 cM with an average spacing of 9.7 cM between loci. The coverage is not complete, as macrochromosome CJA08, the gonosome CJAW and 23 microchromosomes have no marker assigned yet. Significant sequence identities of quail markers with chicken enabled the alignment of the quail linkage groups on the chicken genome sequence assembly. This, together with interspecific Fluorescence In Situ Hybridization (FISH), revealed very high similarities in marker order between the two species for the eight macrochromosomes and the 14 microchromosomes studied. CONCLUSION Integrating the two microsatellite and the AFLP quail genetic maps greatly enhances the quality of the resulting information and will thus facilitate the identification of Quantitative Trait Loci (QTL). The alignment with the chicken chromosomes confirms the high conservation of gene order that was expected between the two species for macrochromosomes. By extending the comparative study to the microchromosomes, we suggest that a wealth of information can be mined in chicken, to be used for genome analyses in quail.
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Affiliation(s)
- Boniface B Kayang
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
- Department of Animal Science, University of Ghana, Legon, Accra, Ghana
| | - Valérie Fillon
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
| | - Miho Inoue-Murayama
- Faculty of Applied Biological Sciences, Gifu University, Gifu 501-1193, Japan
| | - Mitsuru Miwa
- Faculty of Applied Biological Sciences, Gifu University, Gifu 501-1193, Japan
| | - Sophie Leroux
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
| | - Katia Fève
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
| | - Jean-Louis Monvoisin
- UMR Génétique et Diversité Animales, INRA bât 211, 78352 Jouy-en-Josas Cedex, France
| | - Frédérique Pitel
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
| | - Matthieu Vignoles
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
| | - Céline Mouilhayrat
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
| | | | - Shin'ichi Ito
- Faculty of Applied Biological Sciences, Gifu University, Gifu 501-1193, Japan
| | - Francis Minvielle
- UMR Génétique et Diversité Animales, INRA bât 211, 78352 Jouy-en-Josas Cedex, France
| | - Alain Vignal
- Laboratoire de Génétique Cellulaire, Centre INRA de Toulouse, BP 52627 Auzeville, 31326 Castanet Tolosan, France
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Abasht B, Pitel F, Lagarrigue S, Le Bihan-Duval E, Le Roy P, Demeure O, Vignoles F, Simon J, Cogburn L, Aggrey S, Vignal A, Douaire M. Fatness QTL on chicken chromosome 5 and interaction with sex. Genet Sel Evol 2006; 38:297-311. [PMID: 16635451 PMCID: PMC2733900 DOI: 10.1186/1297-9686-38-3-297] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2005] [Accepted: 12/08/2005] [Indexed: 11/10/2022] Open
Abstract
Quantitative trait loci (QTL) affecting fatness in male chickens were previously identified on chromosome 5 (GGA5) in a three-generation design derived from two experimental chicken lines divergently selected for abdominal fat weight. A new design, established from the same pure lines, produced 407 F2 progenies (males and females) from 4 F1-sire families. Body weight and abdominal fat were measured on the F2 at 9 wk of age. In each sire family, selective genotyping was carried out for 48 extreme individuals for abdominal fat using seven microsatellite markers from GGA5. QTL analyses confirmed the presence of QTL for fatness on GGA5 and identified a QTL by sex interaction. By crossing one F1 sire heterozygous at the QTL with lean line dams, three recombinant backcross 1 (BC1) males were produced and their QTL genotypes were assessed in backcross 2 (BC2) progenies. These results confirmed the QTL by sex interaction identified in the F2 generation and they allow mapping of the female QTL to less than 8 Mb at the distal part of the GGA5. They also indicate that fat QTL alleles were segregating in both fat and lean lines.
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Affiliation(s)
- Behnam Abasht
- UMR Inra-Agrocampus Génétique animale, 35042 Rennes, France
| | - Frédérique Pitel
- Laboratoire de génétique cellulaire, Inra, 31326 Auzeville, France
| | | | | | - Pascale Le Roy
- UMR Inra-Agrocampus Génétique animale, 35042 Rennes, France
- SGQA, Inra, 78352 Jouy en Josas, France
| | | | | | - Jean Simon
- Station de recherches avicoles, Inra, 37380 Nouzilly, France
| | | | - Sammy Aggrey
- Department of Animal and Food Sciences, University of Delaware, Newark DE 19717, USA
| | - Alain Vignal
- Laboratoire de génétique cellulaire, Inra, 31326 Auzeville, France
| | - Madeleine Douaire
- UMR Inra-Agrocampus Génétique animale, 35042 Rennes, France
- University of Georgia, Athens, GA 30602, USA
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Lagarrigue S, Pitel F, Carré W, Abasht B, Le Roy P, Neau A, Amigues Y, Sourdioux M, Simon J, Cogburn L, Aggrey S, Leclercq B, Vignal A, Douaire M. Mapping quantitative trait loci affecting fatness and breast muscle weight in meat-type chicken lines divergently selected on abdominal fatness. Genet Sel Evol 2006; 38:85-97. [PMID: 16451793 PMCID: PMC2689300 DOI: 10.1186/1297-9686-38-1-85] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Quantitative trait loci (QTL) for abdominal fatness and breast muscle weight were investigated in a three-generation design performed by inter-crossing two experimental meat-type chicken lines that were divergently selected on abdominal fatness. A total of 585 F2 male offspring from 5 F1 sires and 38 F1 dams were recorded at 8 weeks of age for live body, abdominal fat and breast muscle weights. One hundred-twenty nine microsatellite markers, evenly located throughout the genome and heterozygous for most of the F1 sires, were used for genotyping the F2 birds. In each sire family, those offspring exhibiting the most extreme values for each trait were genotyped. Multipoint QTL analyses using maximum likelihood methods were performed for abdominal fat and breast muscle weights, which were corrected for the effects of 8-week body weight, dam and hatching group. Isolated markers were assessed by analyses of variance. Two significant QTL were identified on chromosomes 1 and 5 with effects of about one within-family residual standard deviation. One breast muscle QTL was identified on GGA1 with an effect of 2.0 within-family residual standard deviation.
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Affiliation(s)
| | - Frédérique Pitel
- Laboratoire de génétique cellulaire, Inra, 31326 Auzeville, France
| | - Wilfrid Carré
- Department of Animal and Food Sciences, University of Delaware,Newark, DE 19717, USA
| | - Behnam Abasht
- UMR Inra-Agrocampus génétique animale, 35042 Rennes, France
| | - Pascale Le Roy
- UMR Inra-Agrocampus génétique animale, 35042 Rennes, France
- SGQA, Inra, 78352 Jouy-en-Josas, France
| | - André Neau
- Department of Animal Genetics, Inra, 78352 Jouy-en-Josas, France
| | | | - Michel Sourdioux
- UMR Inra-Agrocampus génétique animale, 35042 Rennes, France
- Gene+, 62134 Erin, France
| | - Jean Simon
- Station de recherches avicoles, Inra, 37380 Nouzilly, France
| | - Larry Cogburn
- Department of Animal and Food Sciences, University of Delaware,Newark, DE 19717, USA
| | | | | | - Alain Vignal
- Laboratoire de génétique cellulaire, Inra, 31326 Auzeville, France
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Beaumont C, Roussot O, Feve K, Vignoles F, Leroux S, Pitel F, Faure JM, Mills AD, Guémené D, Sellier N, Mignon-Grasteau S, Le Roy P, Vignal A. A genome scan with AFLP markers to detect fearfulness-related QTLs in Japanese quail. Anim Genet 2006; 36:401-7. [PMID: 16167983 DOI: 10.1111/j.1365-2052.2005.01336.x] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
A quantitative trait loci (QTL) study was undertaken to identify genome regions involved in the control of fearfulness in Japanese quail (Coturnix japonica). An F2 cross was made between two quail lines divergently selected over 29 generations on duration of tonic immobility (DTI), a catatonic-like state of reduced responsiveness to a stressful stimulation. A total of 1065 animals were measured for the logarithm of DTI (LOGTI), the number of inductions (NI) necessary to induce the immobility reaction, open-field behaviour including locomotor activity (MOVE), latency before first movement (LAT), number of jumps (JUMP), dejections (DEJ) and shouts (SHOUT), corticosterone level after a contention stress (LOGCORT) and body weight at 2 weeks of age (BW2). A total of 310 animals were included in a genome scan using selective genotyping with 248 AFLP markers. A total of 21 suggestive or genome-wide significant QTL were observed. Two highly significant QTL were identified on linkage group 1 (GL1), one for LOGTI and one for NI. In the vicinity of the QTL for LOGTI, a nearly significant QTL for SHOUT and a suggestive QTL for LAT were also identified. On GL3, genome-wide significant QTL were observed for JUMP and DEJ as well as suggestive QTL for LOGTI, MOVE, SHOUT and LAT. A significant QTL for BW2 was observed on GL2 and a nearly significant one on GL1. These results may be useful in the understanding of fearfulness in quail and related species provided that fearfulness has the same genetic basis.
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Affiliation(s)
- C Beaumont
- Laboratoire de Génétique Cellulaire, INRA, 31326 Castanet-Tolosan, France.
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50
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Morisson M, Leroux S, Jiguet-Jiglaire C, Assaf S, Pitel F, Lagarrigue S, Bardes S, Feve K, Faraut T, Milan D, Vignal A. A gene-based radiation hybrid map of chicken microchromosome 14: comparison to human and alignment to the assembled chicken sequence. Genet Sel Evol 2006; 37:229-51. [PMID: 16194526 PMCID: PMC2697232 DOI: 10.1186/1297-9686-37-3-229] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
We present a gene-based RH map of the chicken microchromosome GGA14, known to have synteny conservations with human chromosomal regions HSA16p13.3 and HSA17p11.2. Microsatellite markers from the genetic map were used to check the validity of the RH map and additional markers were developed from chicken EST data to yield comparative mapping data. A high rate of intra-chromosomal rearrangements was detected by comparison to the assembled human sequence. Finally, the alignment of the RH map to the assembled chicken sequence showed a small number of discordances, most of which involved the same region of the chromosome spanning between 40.5 and 75.9 cR(6000) on the RH map.
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Affiliation(s)
- Mireille Morisson
- Laboratoire de génétique cellulaire, Institut national de la recherche agronomique, Castanet-Tolosan, France.
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