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dc.contributor.author | Casto-Rebollo, Cristina | es_ES |
dc.contributor.author | Argente, María José | es_ES |
dc.contributor.author | García , María Luz | es_ES |
dc.contributor.author | Pena, Romi | es_ES |
dc.contributor.author | Ibáñez-Escriche, Noelia | es_ES |
dc.date.accessioned | 2021-06-30T03:30:39Z | |
dc.date.available | 2021-06-30T03:30:39Z | |
dc.date.issued | 2020-05-06 | es_ES |
dc.identifier.issn | 0999-193X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/168534 | |
dc.description.abstract | [EN] Background Environmental variance (V-E) is partly under genetic control and has recently been proposed as a measure of resilience. Unravelling the genetic background of the V-E of complex traits could help to improve resilience of livestock and stabilize their production across farming systems. The objective of this study was to identify genes and functional mutations associated with variation in V-E of litter size (LS) in rabbits. To achieve this, we combined the results of a genome-wide association study (GWAS) and a whole-genome sequencing (WGS) analysis using data from two divergently selected rabbit lines for high and low V-E of LS. These lines differ in terms of biomarkers of immune response and mortality. Moreover, rabbits with a lower V-E of LS were found to be more resilient to infections than animals with a higher V-E of LS. Results By using two GWAS approaches (single-marker regression and Bayesian multiple-marker regression), we identified four genomic regions associated with V-E of LS, on chromosomes 3, 7, 10, and 14. We detected 38 genes in the associated genomic regions and, using WGS, we identified 129 variants in the splicing, UTR, and coding (missense and frameshift effects) regions of 16 of these 38 genes. These genes were related to the immune system, the development of sensory structures, and stress responses. All of these variants (except one) segregated in one of the rabbit lines and were absent (n = 91) or fixed in the other one (n = 37). The fixed variants were in the HDAC9, ITGB8, MIS18A, ENSOCUG00000021276 and URB1 genes. We also identified a 1-bp deletion in the 3 ' UTR region of the HUNK gene that was fixed in the low V-E line and absent in the high V-E line. Conclusions This is the first study that combines GWAS and WGS analyses to study the genetic basis of V-E. The new candidate genes and functional mutations identified in this study suggest that the V-E of LS is under the control of functions related to the immune system, stress response, and the nervous system. These findings could also explain differences in resilience between rabbits with homogeneous and heterogeneous V-E of litter size. | es_ES |
dc.description.sponsorship | This study was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) with the Projects AGL2014-55921, C2-1-P and C2-2-P, and AGL2017-86083, C2-1-P and C2-2-P and the Grant RYC-2016-19764. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Springer (Biomed Central Ltd.) | es_ES |
dc.relation.ispartof | Genetics Selection Evolution | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject.classification | PRODUCCION ANIMAL | es_ES |
dc.title | Identification of functional mutations associated with environmental variance of litter size in rabbits | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1186/s12711-020-00542-w | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//AGL2014-55921-C2-2-P/ES/ANALISIS GENOMICO DE LA VARIANZA RESIDUAL DEL TAMAÑO DE CAMADA Y SU RELACION CON EL BIENESTAR ANIMAL/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-86083-C2-2-P/ES/ESTUDIO MULTIOMICO DE LA MICROBIOTA DIGESTIVA Y SU RELACION CON LA SENSIBILIDAD AL AMBIENTE EN LINEAS DE CONEJO SELECCIONADAS POR VARIABILIDAD AMBIENTAL/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/AGL2017-86083-C2-1-P/ES/ESTUDIO MULTIOMICO SOBRE SENSIBILIDAD AMBIENTAL, LONGEVIDAD Y DEPOSICION GRASA EN LINEAS SELECCIONADAS DE CONEJO/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//AGL2014-55921-C2-1-P/ES/ESTUDIO GENOMICO Y METABOLOMICO DE VARIAS LINEAS DE SELECCION DIVERGENTE EN CONEJO: EL CONEJO COMO MODELO EXPERIMENTAL/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RYC-2016-19764/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MECD//FPU17%2F01196/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ciencia Animal - Departament de Ciència Animal | es_ES |
dc.description.bibliographicCitation | Casto-Rebollo, C.; Argente, MJ.; García, ML.; Pena, R.; Ibáñez-Escriche, N. (2020). Identification of functional mutations associated with environmental variance of litter size in rabbits. Genetics Selection Evolution. 52(1):1-9. https://doi.org/10.1186/s12711-020-00542-w | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1186/s12711-020-00542-w | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 9 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 52 | es_ES |
dc.description.issue | 1 | es_ES |
dc.identifier.pmid | 32375645 | es_ES |
dc.identifier.pmcid | PMC7203823 | es_ES |
dc.relation.pasarela | S\414415 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |
dc.contributor.funder | Ministerio de Educación, Cultura y Deporte | es_ES |
dc.description.references | Ibáñez-Escriche N, Varona L, Sorensen D, Noguera JL. A study of heterogeneity of environmental variance for slaughter weight in pigs. Animal. 2008;2:19–26. | es_ES |
dc.description.references | Ibáñez-Escriche N, Moreno A, Nieto B, Piqueras P, Salgado C, Gutiérrez JP. Genetic parameters related to environmental variability of weight traits in a selection experiment for weight gain in mice; signs of correlated canalised response. Genet Sel Evol. 2008;40:279–93. | es_ES |
dc.description.references | Mulder H, Hill W, Vereijken A, Veerkamp R. Estimation of genetic variation in residual variance in female and male broiler chickens. Animal. 2009;3:1673–80. | es_ES |
dc.description.references | Ros M, Sorensen D, Waagepetersen R, Dupont-Nivet M, Sancristobal M, Bonnet J, et al. Evidence for genetic control of adult weight plasticity in the snail Helix aspersa. Genetics. 2004;168:2089–97. | es_ES |
dc.description.references | Rönnegård L, Felleki M, Fikse WF, Mulder HA, Strandberg E. Variance component and breeding value estimation for genetic heterogeneity of residual variance in Swedish Holstein dairy cattle. J Dairy Sci. 2013;96:2627–36. | es_ES |
dc.description.references | Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. Harlow: Prentice Hall; 1996. | es_ES |
dc.description.references | Garreau H, Bolet G, Larzul C, Robert-Granié C, Saleil G, SanCristobal M, et al. Results of four generations of a canalising selection for rabbit birth weight. Livest Sci. 2008;119:55–62. | es_ES |
dc.description.references | Formoso-Rafferty N, Cervantes I, Ibáñez-Escriche N, Gutiérrez JP. Genetic control of the environmental variance for birth weight in seven generations of a divergent selection experiment in mice. J Anim Breed Genet. 2016;133:227–37. | es_ES |
dc.description.references | Blasco A, Martínez-Álvaro M, García ML, Ibáñez-Escriche N, Argente MJ. Selection for environmental variance of litter size in rabbit. Genet Sel Evol. 2017;49:48. | es_ES |
dc.description.references | Berghof TVL, Poppe M, Mulder HA. Opportunities to improve resilience in animal breeding programs. Front Genet. 2019;9:692. | es_ES |
dc.description.references | Mulder HA. Genomic selection improves response to selection in resilience by exploiting genotype by environment interactions. Front Genet. 2016;7:178. | es_ES |
dc.description.references | Colditz IG, Hine BC. Resilience in farm animals: biology, management, breeding and implications for animal welfare. Anim Prod Sci. 2016;56:1961–83. | es_ES |
dc.description.references | Sell-Kubiak E, Duijvesteijn N, Lopes MS, Janss LLG, Knol EF, Bijma P, et al. Genome-wide association study reveals novel loci for litter size and its variability in a Large White pig population. BMC Genomics. 2015;16:1049. | es_ES |
dc.description.references | Morgante F, Sørensen P, Sorensen DA, Maltecca C, Mackay TFC. Genetic architecture of micro-environmental plasticity in Drosophila melanogaster. Sci Rep. 2015;5:9785. | es_ES |
dc.description.references | Wijga S, Bastiaansen JWM, Wall E, Strandberg E, de Haas Y, Giblin L, et al. Genomic associations with somatic cell score in first-lactation Holstein cows. J Dairy Sci. 2012;95:899–908. | es_ES |
dc.description.references | Owen JA, Punt J, Stranford SA, Jones PP, Kuby J. Immunology. 7th ed. New York: Freeman; 2013. | es_ES |
dc.description.references | Yang J, Loos RJF, Powel JE, Medland SE, Elizabeth K, Chasman DI, et al. FTO genotype is associated with phenotypic variability of body mass index. Nature. 2012;490:267–72. | es_ES |
dc.description.references | Feng Y, Wang F, Pan H, Qiu S, Lu J, Wu L, et al. Obesity-associated gene FTO rs9939609 polymorphism in relation to the risk of tuberculosis. BMC Infect Dis. 2014;14:592. | es_ES |
dc.description.references | Hermesch S, Dominik S, editors. Breeding focus 2014—improving resilience. Armidale: University of New England; 2014. | es_ES |
dc.description.references | Argente MJ, García ML, Zbyňovská K, Petruška P, Capcarová M, Blasco A. Correlated response to selection for litter size environmental variability in rabbits’ resilience. Animal. 2019;13:2348–55. | es_ES |
dc.description.references | Piles M, Garcia ML, Rafel O, Ramon J, Baselga M. Genetics of litter size in three maternal lines of rabbits: repeatability versus multiple-trait models. J Anim Sci. 2006;84:2309–15. | es_ES |
dc.description.references | Chang CC, Chow CC, Tellier LCAM, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. | es_ES |
dc.description.references | Browning BL, Browning SR. Genotype imputation with millions of reference samples. Am J Hum Genet. 2016;98:116–26. | es_ES |
dc.description.references | Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet. 2011;88:76–82. | es_ES |
dc.description.references | Yang J, Weedon MN, Purcell S, Lettre G, Estrada K, Willer CJ, et al. Genomic inflation factors under polygenic inheritance. Eur J Hum Genet. 2011;19:807–12. | es_ES |
dc.description.references | Lander E, Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat Genet. 1995;11:241–7. | es_ES |
dc.description.references | Garrick DJ, Fernando RL. Genome-wide association studies and genomic prediction. Methods Mol Biol. 2013;1019:275–98. | es_ES |
dc.description.references | Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90:773–95. | es_ES |
dc.description.references | VanLiere JM, Rosenberg NA. Mathematical properties of the r2 measure of linkage disequilibrium. Theor Pop Biol. 2008;74:130–7. | es_ES |
dc.description.references | Elston RC. Preprocessing and quality control for whole-genome sequences from the Illumina HiSeq X platform. In: Wright MN, Gola D, Ziegler A, editors. Statistical human genetics methods in molecular biology, vol. 1666. New York: Humana Press; 2017. p. 629–47. | es_ES |
dc.description.references | Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler Transform. Bioinformatics. 2009;25:1754–60. | es_ES |
dc.description.references | Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20. | es_ES |
dc.description.references | Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. 1000 Genome project data processing subgroup. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9. | es_ES |
dc.description.references | Broad Institute. Picard tools. version 2.17.8; Broad Institute, GitHub repository. http://broadinstitute.github.io/picard/. Accessed 21 Feb 2018. | es_ES |
dc.description.references | McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303. | es_ES |
dc.description.references | Cingolani P, Platts A, le Wang L, Coon M, Nguyen T, Wang L, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80–92. | es_ES |
dc.description.references | Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, et al. Ensembl 2018. Nucleic Acids Res. 2018;46:D754–61. | es_ES |
dc.description.references | Stelzer G, Rosen R, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards suite: from gene data mining to disease genome sequence analysis. Curr Protoc Bioinformatics. 2016;54:1–30. | es_ES |
dc.description.references | López de Maturana E, Ibáñez-Escriche N, González-Recio Ó, Marenne G, Mehrban H, Chanock SJ, et al. Next generation modelling in GWAS: comparing different genetic architectures. Hum Genet. 2014;133:1235–53. | es_ES |
dc.description.references | Chen Y, Meng F, Wang B, He L, Liu Y, Liu Z. Dock2 in the development of inflammation and cancer. Eur J Immunol. 2018;48:915–22. | es_ES |
dc.description.references | Kelly A, Gunaltay S, McEntee CP, Shuttleworth EE, Smedley C, Houston SA, et al. Human monocytes and macrophages regulate immune tolerance via integrin αvβ8–mediated TGFβ activation. J Exp Med. 2018;215:2725–36. | es_ES |
dc.description.references | de Zoeten EF, Wang L, Sai H, Dillmann WH, Hancock WW. Inhibition of HDAC9 increases T regulatory cell function and prevents colitis in mice. Gastroenterology. 2010;138:583–94. | es_ES |
dc.description.references | Zhang X, Mosser D. Macrophage activation by endogenous danger signals. J Pathol. 2009;214:161–78. | es_ES |
dc.description.references | Enerbäck S, Nilsson D, Edwards N, Heglind M, Alkanderi S, Ashton E, et al. Acidosis and deafness in patients with recessive mutations in FOXI1. J Am Soc Nephrol. 2018;29:1041–8. | es_ES |
dc.description.references | James G, Foster SR, Key B, Beverdam A. The expression pattern of EVA1C, a novel slit receptor, is consistent with an axon guidance role in the mouse nervous system. PLoS One. 2013;28:e74115. | es_ES |
dc.description.references | Liu A, Li JYH, Bromleigh C, Lao Z, Niswander LA, Joyner AL. FGF17b and FGF18 have different midbrain regulatory properties from FGF8b or activated FGF receptors. Development. 2003;130:6175–85. | es_ES |
dc.description.references | Williams CB, Phelps-Polirer K, Dingle IP, Williams CJ, Rhett MJ, Eblen ST, et al. HUNK phosphorylates EGFR to regulate breast cancer metastasis. Oncogene. 2020;39:1112–24. | es_ES |
dc.description.references | Costa AM, Leite M, Seruca R, Figueiredo C. Adherens junctions as targets of microorganisms: a focus on Helicobacter pylori. FEBS Lett. 2013;587:259–65. | es_ES |