- -

Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Casto-RebolloArge ...
Tamaño: 1.409Mb
Formato: PDF
Descripción: Versión editorial
Abrir
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 Blasco Mateu, Agustín es_ES
dc.contributor.author Ibáñez-Escriche, Noelia es_ES
dc.date.accessioned 2022-07-22T18:06:32Z
dc.date.available 2022-07-22T18:06:32Z
dc.date.issued 2021-07-13 es_ES
dc.identifier.issn 0999-193X es_ES
dc.identifier.uri http://hdl.handle.net/10251/184701
dc.description.abstract [EN] Background Environmental variance (V-E) is partially under genetic control, which means that the V-E of individuals that share the same environment can differ because they have different genotypes. Previously, a divergent selection experiment for V-E of litter size (LS) during 13 generations in rabbit yielded a successful response and revealed differences in resilience between the divergent lines. The aim of the current study was to identify signatures of selection in these divergent lines to better understand the molecular mechanisms and pathways that control V-E of LS and animal resilience. Three methods (F-ST, ROH and varLD) were used to identify signatures of selection in a set of 473 genotypes from these rabbit lines (377) and a base population (96). A whole-genome sequencing (WGS) analysis was performed on 54 animals to detect genes with functional mutations. Results By combining signatures of selection and WGS data, we detected 373 genes with functional mutations in their transcription units, among which 111 had functions related to the immune system, stress response, reproduction and embryo development, and/or carbohydrate and lipid metabolism. The genes TTC23L, FBXL20, GHDC, ENSOCUG00000031631, SLC18A1, CD300LG, MC2R, and ENSOCUG00000006264 were particularly relevant, since each one carried a functional mutation that was fixed in one of the rabbit lines and absent in the other line. In the 3MODIFIER LETTER PRIMEUTR region of the MC2R and ENSOCUG00000006264 genes, we detected a novel insertion/deletion (INDEL) variant. Conclusions Our findings provide further evidence in favour of V-E as a measure of animal resilience. Signatures of selection were identified for V-E of LS in genes that have a functional mutation in their transcription units and are mostly implicated in the immune response and stress response pathways. However, the real implications of these genes for V-E and animal resilience will need to be assessed through functional analyses. es_ES
dc.description.sponsorship We are grateful to CEGEN-PRB3-ISCIII for their genotyping service, supported by Grant No PT17/0019 of the PE I+D+i 2013-2016, funded by ISCIII and ERDF. Cristina Casto-Rebollo acknowledges a FPU17/01196 scholarship from the Spanish Ministry of Science, Innovation and Universities. This study was supported by Projects AGL2014-5592, C2-1-P and C2-2-P, and AGL2017-86083, C2-1-P and C2-2-P, funded by the Spanish Ministerio de Ciencia e Innovacion (MIC)-Agencia Estatal de Investigacion (AEI) and the European Regional Development Fund (FEDER). 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 Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1186/s12711-021-00653-y 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/ISCIII//PT17%2F0019/ 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/MIU//FPU17%2F01196//AYUDA CONTRATO PREDOCTORAL FPU-CASTO REBOLLO/ 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//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.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.; Blasco Mateu, A.; Ibáñez-Escriche, N. (2021). Selection for environmental variance of litter size in rabbits involves genes in pathways controlling animal resilience. Genetics Selection Evolution. 53(1). https://doi.org/10.1186/s12711-021-00653-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1186/s12711-021-00653-y es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 53 es_ES
dc.description.issue 1 es_ES
dc.identifier.pmid 34256696 es_ES
dc.identifier.pmcid PMC8276493 es_ES
dc.relation.pasarela S\443567 es_ES
dc.contributor.funder Instituto de Salud Carlos III es_ES
dc.contributor.funder MINISTERIO DE ECONOMIA Y EMPRESA es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund 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 Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. Harlow: Prentice Hall; 1996. es_ES
dc.description.references Walsh B, Lynch M. Evolution and selection of quantitative traits. Oxford: Oxford University Press; 2018. 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 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 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 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 Casto-Rebollo C, Argente MJ, García ML, Pena R, Ibáñez-Escriche N. Identification of functional mutations associated with environmental variance of litter size in rabbits. Genet Sel Evol. 2020;52:22. es_ES
dc.description.references Hill WG, Mulder HA. Genetic analysis of environmental variation. Genet Res (Camb). 2010;92:381–95. es_ES
dc.description.references Crouch DJM, Bodmer WF. Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants. Proc Natl Acad Sci USA. 2020;117:18924–33. es_ES
dc.description.references Qanbari S, Simianer H. Mapping signatures of positive selection in the genome of livestock. Livest Sci. 2014;166:133–43. es_ES
dc.description.references González-Rodríguez A, Munilla S, Mouresan EF, Cañas-Álvarez JJ, Diaz C, Piedrafiat J, et al. On the performance of tests for the detection of signatures of selection: a case study with the Spanish autochthonous beef cattle populations. Genet Sel Evol. 2016;48:81. es_ES
dc.description.references Sosa-Madrid BS, Varona L, Blasco A, Hernández P, Casto-Rebollo C, Ibáñez-Escriche N. The effect of divergent selection for intramuscular fat on the domestic rabbit genome. Animal. 2020;14:2225–35. es_ES
dc.description.references Chang CC, Chow CC, Tellier LC, 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 Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 2012;28:3326–8. es_ES
dc.description.references Smith JM, Haigh J. The hitch-hiking effect of a favourable gene. Genet Res (Camb). 1974;23:23–35. es_ES
dc.description.references Ceballos FC, Joshi PK, Clark DW, Ramsay M, Wilson JF. Runs of homozygosity: windows into population history and trait architecture. Nat Rev Genet. 2018;19:220–34. es_ES
dc.description.references Weir BS, Cockerham CC. Estimating F-Statistics for the analysis of population structure. Evolution. 1984;38:1358–70. es_ES
dc.description.references Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, et al. The variant call format and VCF tools. Bioinformatics. 2011;27:2156–8. es_ES
dc.description.references Ong RTH, Teo YY. varLD: a program for quantifying variation in linkage disequilibrium patterns between populations. Bioinformatics. 2010;26:1269–70. 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, vol. 1666. Methods in molecular biology. New York: Humana Press; 2017. p. 629–47. 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 Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4:1184–91. 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 Kim ES, Ros-Freixedes R, Pena RN, Baas TJ, Estany J, Rothschild MF. Identification of signatures of selection for intramuscular fat and backfat thickness in two Duroc populations. J Anim Sci. 2015;93:3292–302. es_ES
dc.description.references Lillie M, Sheng Z, Honaker CF, Dorshorst BJ, Ashwell CM, Siegel PB, et al. Genome-wide standing variation facilitates long-term response to bidirectional selection for antibody response in chickens. BMC Genomics. 2017;18:99. es_ES
dc.description.references Johansson AM, Pettersson ME, Siegel PB, Carlborg Ö. Genome-wide effects of long-term divergent selection. PLoS Genet. 2010;6:e1001188. es_ES
dc.description.references Pritchard JK, Pickrell JK, Coop G. The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation. Curr Biol. 2010;20:R208–15. es_ES
dc.description.references Fisher RA. The correlation between relatives on the supposition of mendelian inheritance. Trans R Soc Edinb. 1918;53:399–433. es_ES
dc.description.references Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell. 2017;169:1177–86. es_ES
dc.description.references Beloumi D, Blasco A, Muelas R, Santacreu MA, García ML, Argente MJ. Inflammatory correlated response in two lines of rabbit selected divergently for litter size environmental variability. Animals (Basel). 2020;10:1540. es_ES
dc.description.references Argente MJ, Calle EW, García ML, Blasco A. Correlated response in litter size components in rabbits selected for litter size variability. J Anim Breed Genet. 2017;134:505–11. es_ES
dc.description.references Calle EW, García ML, Blasco A, Argente MJ. Correlated response in early embryonic development in rabbits selected for litter size variability. World Rabbit Sci. 2017;25:323–7. es_ES
dc.description.references Yang Y, Chen M, Ventro G, Harmon CM. Amino acid residue L112 in the ACTH receptor plays a key role in ACTH or α-MSH selectivity. Mol Cell Endocrinol. 2019;482:11–7. es_ES
dc.description.references Hannibal KE, Bishop MD. Chronic stress, cortisol dysfunction, and pain: a psychoneuroendocrine rationale for stress management in pain rehabilitation. Phys Ther. 2014;94:1816–25. es_ES
dc.description.references Larange A, Cheroutre H. Retinoic acid and retinoic acid receptors as pleiotropic modulators of the immune system. Annu Rev Immunol. 2016;34:369–94. 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 Collier RJ, Collier JL, Rhoads RP, Baumgard LH. Invited review: genes involved in the bovine heat stress response. J Dairy Sci. 2008;91:445–54. es_ES
dc.description.references Celi M, Vazzana M, Sanfratello MA, Parrinello N. Elevated cortisol modulates Hsp70 and Hsp90 gene expression and protein in sea bass head kidney and isolated leukocytes. Gen Comp Endocrinol. 2012;175:424–31. es_ES
dc.description.references Neuer A, Spandorfer SD, Giraldo P, Jeremias J, Dieterle S, Korneeva I, et al. Heat shock protein expression during gametogenesis and embryogenesis. Infect Dis Obstet Gynecol. 1999;7:10–6. es_ES
dc.description.references Ravikumar S, Muthuraman P. Cortisol effect on heat shock proteins in the C2C12 and 3T3-L1 cells. In Vitro Cell Dev Biol Anim. 2014;50:581–6. es_ES
dc.description.references Pires BV, Stafuzza NB, Lima SBGPNP, Negrão JA, Paz CCP. Differential expression of heat shock protein genes associated with heat stress in Nelore and Caracu beef cattle. Livest Sci. 2019;30:103839. es_ES
upv.costeAPC 1900 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem