Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-19T00:01:54.191Z Has data issue: false hasContentIssue false
  • English
  • Français

The effect of divergent selection for intramuscular fat on the domestic rabbit genome

Published online by Cambridge University Press:  19 June 2020

B. S. Sosa-Madrid Open the ORCID record for B. S. Sosa-Madrid [Opens in a new window] ,
L. Varona Open the ORCID record for L. Varona [Opens in a new window] ,
A. Blasco Open the ORCID record for A. Blasco [Opens in a new window] ,
P. Hernández Open the ORCID record for P. Hernández [Opens in a new window] ,
C. Casto-Rebollo Open the ORCID record for C. Casto-Rebollo [Opens in a new window]  and
N. Ibáñez-Escriche Open the ORCID record for N. Ibáñez-Escriche [Opens in a new window]
B. S. Sosa-Madrid
Affiliation:
Institute for Animal Science and Technology, Universitat Politècnica de València, 46022Valencia, Spain
L. Varona
Affiliation:
Unidad de Genética Cuantitativa y Mejora Animal, Universidad de Zaragoza, Instituto Agroalimentario de Aragón (IA2), 50013Zaragoza, Spain
A. Blasco
Affiliation:
Institute for Animal Science and Technology, Universitat Politècnica de València, 46022Valencia, Spain
P. Hernández
Affiliation:
Institute for Animal Science and Technology, Universitat Politècnica de València, 46022Valencia, Spain
C. Casto-Rebollo
Affiliation:
Institute for Animal Science and Technology, Universitat Politècnica de València, 46022Valencia, Spain
N. Ibáñez-Escriche*
Affiliation:
Institute for Animal Science and Technology, Universitat Politècnica de València, 46022Valencia, Spain
*
†E-mail: noeibes@dca.upv.es
Get access

Abstract

An experiment of divergent selection for intramuscular fat was carried out at Universitat Politècnica de València. The high response of selection in intramuscular fat content, after nine generations of selection, and a multidimensional scaling analysis showed a high degree of genomic differentiation between the two divergent populations. Therefore, local genomic differences could link genomic regions, encompassing selective sweeps, to the trait used as selection criterion. In this sense, the aim of this study was to identify genomic regions related to intramuscular fat through three methods for detection of selection signatures and to generate a list of candidate genes. The methods implemented in this study were Wright’s fixation index, cross population composite likelihood ratio and cross population – extended haplotype homozygosity. Genomic data came from the 9th generation of the two populations divergently selected, 237 from Low line and 240 from High line. A high single nucleotide polymorphism (SNP) density array, Affymetrix Axiom OrcunSNP Array (around 200k SNPs), was used for genotyping samples. Several genomic regions distributed along rabbit chromosomes (OCU) were identified as signatures of selection (SNPs having a value above cut-off of 1%) within each method. In contrast, 8 genomic regions, harbouring 80 SNPs (OCU1, OCU3, OCU6, OCU7, OCU16 and OCU17), were identified by at least 2 methods and none by the 3 methods. In general, our results suggest that intramuscular fat selection influenced multiple genomic regions which can be a consequence of either only selection effect or the combined effect of selection and genetic drift. In addition, 73 genes were retrieved from the 8 selection signatures. After functional and enrichment analyses, the main genes into the selection signatures linked to energy, fatty acids, carbohydrates and lipid metabolic processes were ACER2, PLIN2, DENND4C, RPS6, RRAGA (OCU1), ST8SIA6, VIM (OCU16), RORA, GANC and PLA2G4B (OCU17). This genomic scan is the first study using rabbits from a divergent selection experiment. Our results pointed out a large polygenic component of the intramuscular fat content. Besides, promising positional candidate genes would be analysed in further studies in order to bear out their contributions to this trait and their feasible implications for rabbit breeding programmes.

Keywords

genome scangenomic divergencelagomorphmeat qualityselection signatures
Type
Research Article
Information
animal , Volume 14 , Issue 11 , November 2020 , pp. 2225 - 2235
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Animal Consortium

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aloulou, A, Ali, Y Ben, Bezzine, S, Gargouri, Y and Gelb, MH 2012. Phospholipases: an overview. In Lipases and phospholipases: methods and protocols (ed. Sandoval, G.), pp. 63–85. Humana Press, Totowa, NJ, USA.CrossRefGoogle Scholar
Beissinger, TM, Rosa, GJ, Kaeppler, SM, Gianola, D and de Leon, N 2015. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genetics Selection Evolution 47, 30.CrossRefGoogle ScholarPubMed
Carneiro, M, Albert, FW, Afonso, S, Pereira, RJ, Burbano, H, Campos, R, Melo-Ferreira, J, Blanco-Aguiar, JA, Villafuerte, R, Nachman, MW, Good, JM and Ferrand, N 2014a. The Genomic Architecture of Population Divergence between Subspecies of the European Rabbit. PLoS Genetics 10, e1003519.CrossRefGoogle ScholarPubMed
Carneiro, M, Rubin, CJ, Di Palma, F, Albert, FW, Alföldi, J, Barrio, AM, Pielberg, G, Rafati, N, Sayyab, S, Turner-Maier, J, Younis, S, Afonso, S, Aken, B, Alves, JM, Barrell, D, Bolet, G, Boucher, S, Burbano, HA, Campos, R, Chang, JL, Duranthon, V, Fontanesi, L, Garreau, H, Heiman, D, Johnson, J, Mage, RG, Peng, Z, Queney, G, Rogel-Gaillard, C, Ruffier, M, Searle, S, Villafuerte, R, Xiong, A, Young, S, Forsberg-Nilsson, K, Good, JM, Lander, ES, Ferrand, N, Lindblad-Toh, K and Andersson, L 2014b. Rabbit genome analysis reveals a polygenic basis for phenotypic change during domestication. Science 345, 1074–1079.CrossRefGoogle ScholarPubMed
Cesar, AS, Regitano, LC, Tullio, RR, Lanna, DP, Nassu, RT, Mudado, MA, Oliveira, PS, do Nascimento, ML, Chaves, AS, Alencar, MM, Sonstegard, TS, Garrick, DJ, Reecy, JM and Coutinho, LL 2014. Genome-wide association study for intramuscular fat deposition and composition in Nellore cattle. BMC Genetics 15, 39.Google ScholarPubMed
Chen, H, Patterson, N and Reich, D 2010. Population differentiation as a test for selective sweeps. Genome Research 20, 393–402.CrossRefGoogle ScholarPubMed
Damon, M, Wyszynska-Koko, J, Vincent, A, Hérault, F and Lebret, B 2012. Comparison of muscle transcriptome between pigs with divergent meat quality phenotypes identifies genes related to muscle metabolism and structure. PLoS ONE 7, e33763.Google ScholarPubMed
Gandolfi, G, Mazzoni, M, Zambonelli, P, Lalatta-Costerbosa, G, Tronca, A, Russo, V and Davoli, R 2011. Perilipin 1 and perilipin 2 protein localization and gene expression study in skeletal muscles of European cross-breed pigs with different intramuscular fat contents. Meat Science 88, 631–637.Google ScholarPubMed
Gol, S, Ros-Freixedes, R, Zambonelli, P, Tor, M, Pena, RN, Braglia, S, Zappaterra, M, Estany, J and Davoli, R 2016. Relationship between perilipin genes polymorphisms and growth, carcass and meat quality traits in pigs. Journal of Animal Breeding and Genetics 133, 24–30.Google ScholarPubMed
González-Rodríguez, A, Munilla, S, Mouresan, EF, Cañas-Álvarez, JJ, Díaz, C, Piedrafita, J, Altarriba, J, Baro, JÁ, Molina, A and Varona, L 2016. On the performance of tests for the detection of signatures of selection: a case study with the Spanish autochthonous beef cattle populations. Genetics Selection Evolution 48, 81.CrossRefGoogle ScholarPubMed
Grams, V, Wellmann, R, Preuß, S, Grashorn, MA, Kjaer, JB, Bessei, W and Bennewitz, J 2015. Genetic parameters and signatures of selection in two divergent laying hen lines selected for feather pecking behaviour. Genetics Selection Evolution 47, 77.CrossRefGoogle ScholarPubMed
Gurgul, A, Jasielczuk, I, Ropka-Molik, K, Semik-Gurgul, E, Pawlina-Tyszko, K, Szmatoła, T, Szyndler-Nędza, M, Bugno-Poniewierska, M, Blicharski, T, Szulc, K, Skrzypczak, E and Krupiński, J 2018. A genome-wide detection of selection signatures in conserved and commercial pig breeds maintained in Poland. BMC Genetics 19, 95.CrossRefGoogle ScholarPubMed
Johansson, AM, Pettersson, ME, Siegel, PB and Carlborg, Ö 2010. Genome-wide effects of long-term divergent selection. PLoS Genetics 6, e1001188.Google ScholarPubMed
Kim, E-S, Ros-Freixedes, R, Pena, RN, Baas, TJ, Estany, J and Rothschild, MF 2015. Identification of signatures of selection for intramuscular fat and backfat thickness in two Duroc populations. Journal of Animal Science 93, 3292–3302.CrossRefGoogle ScholarPubMed
Kuleshov, MV, Jones, MR, Rouillard, AD, Fernandez, NF, Duan, Q, Wang, Z, Koplev, S, Jenkins, SL, Jagodnik, KM, Lachmann, A, McDermott, MG, Monteiro, CD, Gundersen, GW and Ma’ayan, A 2016. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research 44, W90–W97.Google ScholarPubMed
Li, X, Lee, C-K, Choi, B-H, Kim, T-H, Kim, J-J and Kim, K-S 2010. Quantitative gene expression analysis on chromosome 6 between Korean native pigs and Yorkshire breeds for fat deposition. Genes & Genomics 32, 385–393.CrossRefGoogle Scholar
Lillie, M, Sheng, Z, Honaker, CF, Dorshorst, BJ, Ashwell, CM, Siegel, PB and Carlborg, Ö 2017. Genome-wide standing variation facilitates long-term response to bidirectional selection for antibody response in chickens. BMC Genomics 18, 99.CrossRefGoogle ScholarPubMed
Ma, H, Zhang, S, Zhang, K, Zhan, H, Peng, X, Xie, S, Li, X, Zhao, S and Ma, Y 2019. Identifying selection signatures for backfat thickness in Yorkshire pigs highlights new regions affecting fat metabolism. Genes 10, 254.CrossRefGoogle ScholarPubMed
Mallick, S, Gnerre, S, Muller, P and Reich, D 2009. The difficulty of avoiding false positives in genome scans for natural selection. Genome Research 19, 922–933.CrossRefGoogle ScholarPubMed
Martínez-Álvaro, M, Hernández, P and Blasco, A 2016. Divergent selection on intramuscular fat in rabbits: responses to selection and genetic parameters. Journal of Animal Science 94, 4993–5003.CrossRefGoogle ScholarPubMed
Mauch, E, Servin, B, Gilbert, H and Dekkers, J 2018. Signatures of selection in two independent populations of pigs divergently selected for feed efficiency. Animal Industry Report AS 664, ASL R3274.Google Scholar
Oleksyk, TK, Smith, MW and O’Brien, SJ 2010. Genome-wide scans for footprints of natural selection. Philosophical Transactions of the Royal Society B: Biological Sciences 365, 185–205.CrossRefGoogle ScholarPubMed
Purcell, S, Neale, B, Todd-Brown, K, Thomas, L, Ferreira, MAR, Bender, D, Maller, J, Sklar, P, de Bakker, PIW, Daly, MJ and Sham, PC 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics 81, 559–575.Google ScholarPubMed
Qanbari, S and Simianer, H 2014. Mapping signatures of positive selection in the genome of livestock. Livestock Science 166, 133–143.CrossRefGoogle Scholar
Rui, L 2014. Energy metabolism in the liver. In Comprehensive physiology, pp. 177–197. John Wiley & Sons, Inc., Hoboken, NJ, USA.Google Scholar
Sabeti, PC, Varilly, P, Fry, B, Lohmueller, J, Hostetter, E, Cotsapas, C, Xie, X, Byrne, EH, McCarroll, SA, Gaudet, R, Schaffner, SF and Lander, ES 2007. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918.Google ScholarPubMed
Sargolzaei, M, Chesnais, JP and Schenkel, FS 2014. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478.CrossRefGoogle ScholarPubMed
Sosa-Madrid, BS, Hernández, P, Blasco, A, Haley, CS, Fontanesi, L, Santacreu, MA, Pena, RN, Navarro, P and Ibáñez-Escriche, N 2020. Genomic regions influencing intramuscular fat in divergently selected rabbit lines. Animal Genetics 51, 58–69.Google Scholar
Sosa-Madrid, BS, Ibañez-Escriche, N, Santacreu, MA, Varona, L and Blasco, A 2017. Huellas de selección en un experimento de seleccion divergente para capacidad uterina en conejo. In Proceedings of the XVII Jornadas sobre Producción Animal, 30–31 May 2017, Zaragoza, Spain, pp. 558–560.Google Scholar
Szpiech, ZA and Hernandez, RD 2014. selscan: an efficient multithreaded program to perform EHH-based scans for positive selection. Molecular Biology and Evolution 31, 2824–2827.Google Scholar
Utsunomiya, YT, Pérez O’Brien, AM, Sonstegard, TS, Van Tassell, CP, do Carmo, AS, Mészáros, G, Sölkner, J and Garcia, JF 2013. Detecting loci under recent positive selection in dairy and beef cattle by combining different genome-wide scan methods. PLoS ONE 8, e64280.Google ScholarPubMed
Walter, M, Chen, FW, Tamari, F, Wang, R and Ioannou, YA 2009. Endosomal lipid accumulation in NPC1 leads to inhibition of PKC, hypophosphorylation of vimentin and Rab9 entrapment. Biology of the Cell 101, 141–153.Google ScholarPubMed
Wang, Z, Ma, H, Xu, L, Zhu, B, Liu, Y, Bordbar, F, Chen, Y, Zhang, L, Gao, X, Gao, H, Zhang, S, Xu, L and Li, J 2019. Genome-wide scan identifies selection signatures in Chinese Wagyu cattle using a high-density SNP array. Animals 9, 296.Google ScholarPubMed
Wipperman, MF, Montrose, DC, Gotto, AM and Hajjar, DP 2019. Mammalian target of rapamycin. The American Journal of Pathology 189, 492–501.CrossRefGoogle ScholarPubMed
Zomeño, C, Hernandez, P and Blasco, A 2013. Divergent selection for intramuscular fat content in rabbits. I. Direct response to selection. Journal of Animal Science 91, 4526–4531.CrossRefGoogle ScholarPubMed
Supplementary material: File

Sosa-Madrid et al. supplementary material

Sosa-Madrid et al. supplementary material 1

Download Sosa-Madrid et al. supplementary material(File)
File 24.3 KB
Supplementary material: File

Sosa-Madrid et al. supplementary material

Sosa-Madrid et al. supplementary material 2

Download Sosa-Madrid et al. supplementary material(File)
File 22.9 KB