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Identification of functional mutations associated with environmental variance of litter size in rabbits

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Identification of functional mutations associated with environmental variance of litter size in rabbits

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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 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 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 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 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 info:eu-repo/grantAgreement/MINECO//RYC-2016-19764 es_ES
dc.relation AGENCIA ESTATAL DE INVESTIGACION/RYC-2016-19764 es_ES
dc.relation /FPU17/01196 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.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 INVESTIGACION es_ES
dc.contributor.funder MINISTERIO DE ECONOMIA Y EMPRESA 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 CIENCIA INNOVACION Y UNIVERSIDADES 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


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