Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-19T12:34:08.568Z Has data issue: false hasContentIssue false
  • English
  • Français

A genome-wide association study for feed efficiency-related traits in a crossbred pig population

Published online by Cambridge University Press:  28 May 2019

É. F. Silva ,
M. S. Lopes ,
P. S. Lopes  and
E. Gasparino
É. F. Silva*
Affiliation:
Departamento de Zootecnia, UEM – Universidade Estadual de Maringá, Av. Colombo, 5790, 87.020-900, Maringá, PR, Brazil Topigs Norsvin, Rua Visconde do Rio Branco, 1310 – Sala 52, 80.420-210, Curitiba, PR, Brazil
M. S. Lopes
Affiliation:
Topigs Norsvin, Rua Visconde do Rio Branco, 1310 – Sala 52, 80.420-210, Curitiba, PR, Brazil Topigs Norsvin Research Center, Schoenaker 6, 6641 SZ, Beuningen, the Netherlands
P. S. Lopes
Affiliation:
Departamento de Zootecnia, UFV – Universidade Federal de Viçosa, Campus Universitário, 36.570-000, Viçosa, MG, Brazil
E. Gasparino
Affiliation:
Departamento de Zootecnia, UEM – Universidade Estadual de Maringá, Av. Colombo, 5790, 87.020-900, Maringá, PR, Brazil
*
†E-mail: everton.silva@topigsnorsvin.com.br
Get access

Abstract

Feed efficiency (FE) is one of the most important traits in pig production. However, it is difficult and costly to measure it, limiting the collection of large amount of data for an accurate selection for better FE. Therefore, the identification of single-nucleotide polymorphisms (SNPs) associated with FE-related traits to be used in the genetic evaluation is of great interest of pig breeding programs for increasing the prediction accuracy and the genetic progress of these traits. The objective of this study was to identify SNPs significantly associated with FE-related traits: average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR). We also aimed to identify potential candidate genes for these traits. Phenotypic information recorded on a population of 2386 three-way crossbreed pigs that were genotyped for 51 468 SNPs was used. We identified three loci of quantitative trait (QTL) regions associated with ADG and three QTL regions associated with ADFI; however, no significant association was found for FCR. A false discovery rate (FDR) ≤ 0.005 was used as the threshold for declaring an association as significant. The QTL regions associated with ADG on Sus scrofa chromosome (SSC) 1 were located between 177.01 and 185.47 Mb, which overlaps with the QTL regions for ADFI on SSC1 (173.26 and 185.47 Mb). The other QTL region for ADG was located on SSC12 (2.87 and 3.22 Mb). The most significant SNPs in these QTL regions explained up to 3.26% of the phenotypic variance of these traits. The non-identification of genomic regions associated with FCR can be explained by the complexity of this trait, which is a ratio between ADG and ADFI. Finally, the genes CDH19, CDH7, RNF152, MC4R, PMAIP1, FEM1B and GAA were the candidate genes found in the 1 Mb window around the QTL regions identified in this study. Among them, the MC4R gene (SSC1) has a well-known function related to ADG and ADFI. In this study, we identified three QTL regions for ADG (SSC1 and SSC12) and three for ADFI (SSC1). These regions were previously described in purebred pig populations; however, to our knowledge, this is the first study to confirm the relevance of these QTL regions in a crossbred pig population. The potential use of the SNPs and genes identified in this study in prediction models that combine genomic selection and marker-assisted selection should be evaluated for increasing the prediction accuracy of these traits in this population.

Keywords

Sus scrofamarkerfeed conversiondaily gainfeed intake
Type
Research Article
Information
animal , Volume 13 , Issue 11 , November 2019 , pp. 2447 - 2456
Copyright
© The Animal Consortium 2019 

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.)

Footnotes

a

Present address: Topigs Norsvin, Rua Visconde do Rio Branco, 1310 – Sala 52, 80.420-210, Curitiba, PR, Brazil.

References

Aulchenko, YS, Ripke, S, Isaacs, A and van Duijn, CM 2007. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296.10.1093/bioinformatics/btm108CrossRefGoogle Scholar
Bacanu, S-A, Devlin, B and Roeder, K 2000. The power of genomic control. The American Journal of Human Genetics 66, 1933–1944.CrossRefGoogle ScholarPubMed
Barrett, JC, Fry, B, Maller, J and Daly, MJ 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, 263–265.10.1093/bioinformatics/bth457CrossRefGoogle ScholarPubMed
Beavis, WD 1998. QTL analyses: power, precision, and accuracy. In Molecular dissection of complex traits (ed. Paterson, AH), pp. 145–162. CRC Press LLC, Boca Raton, FL, USA.Google Scholar
Chao, Z, Wang, F, Deng, C-Y, Wei, L-M, Sun, R-P, Liu, H-L, Liu, Q-W and Zheng, X-L 2012. Distribution and linkage disequilibrium analysis of polymorphisms of MC4R, LEP, H-FABP genes in the different populations of pigs, associated with economic traits in DIV 2 line. Molecular Biology Reports 39, 6329–6335.10.1007/s11033-012-1454-xCrossRefGoogle Scholar
Dabney, A, Storey, JD and Warnes, GR 2011. qvalue: Q-value estimation for false discovery rate control. R package version 1.26. 0. Retrieved on 28 June 2018, from http://www.idg.pl/mirrors/CRAN/web/packages/qvalue/qvalue.pdf.Google Scholar
Davoli, R, Braglia, S, Valastro, V, Annarratone, C, Comella, M, Zambonelli, P, Nisi, I, Gallo, M, Buttazzoni, L and Russo, V 2012. Analysis of MC4R polymorphism in Italian Large White and Italian Duroc pigs: association with carcass traits. Meat Science 90, 887–892.CrossRefGoogle ScholarPubMed
Do, DN, Janss, LL, Jensen, J and Kadarmideen, HN 2015. SNP annotation-based whole genomic prediction and selection: an application to feed efficiency and its component traits in pigs. Journal of Animal Science 93, 2056–2063.10.2527/jas.2014-8640CrossRefGoogle ScholarPubMed
Fan, B, Onteru, SK, Du, Z-Q, Garrick, DJ, Stalder, KJ and Rothschild, MF 2011. Genome-wide association study identifies loci for body composition and structural soundness traits in pigs. PLoS One 6, e14726.10.1371/journal.pone.0014726CrossRefGoogle ScholarPubMed
Gilmour, AR, Gogel, BJ, Cullis, BR and Thompson, R 2009. ASReml User Guide Release 3.0. VSN International Ltd., Hemel Hempstead, UK.Google Scholar
Hayes, B, Bowman, P, Chamberlain, A and Goddard, M 2009. Invited review: genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.10.3168/jds.2008-1646CrossRefGoogle ScholarPubMed
Hayes, BJ, Lewin, HA and Goddard, ME 2013. The future of livestock breeding: genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genetics 29, 206–214.CrossRefGoogle ScholarPubMed
Hickey, JM, Kinghorn, BP, Tier, B, Wilson, JF, Dunstan, N and van der Werf, JH 2011. A combined long-range phasing and long haplotype imputation method to impute phase for SNP genotypes. Genetics Selection Evolution 43, 1.10.1186/1297-9686-43-12CrossRefGoogle ScholarPubMed
Hidalgo, AM, Bastiaansen, JW, Lopes, MS, Harlizius, B, Groenen, MA and de Koning, D-J 2015. Accuracy of predicted genomic breeding values in purebred and crossbred pigs. G3: Genes Genomes| Genetics, g3. 115.018119.CrossRefGoogle Scholar
Hinrichs, AL, Larkin, EK and Suarez, BK 2009. Population stratification and patterns of linkage disequilibrium. Genetic Epidemiology 33, S88–S92.CrossRefGoogle ScholarPubMed
Houston, R, Cameron, N and Rance, K 2004. A melanocortin-4 receptor (MC4R) polymorphism is associated with performance traits in divergently selected Large White pig populations. Animal Genetics 35, 386–390.CrossRefGoogle ScholarPubMed
Howard, JT, Jiao, S, Tiezzi, F, Huang, Y, Gray, KA and Maltecca, C 2015. Genome-wide association study on legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars. BMC Genetics 16, 59.CrossRefGoogle ScholarPubMed
Jonas, E and Koning, D-Jd 2015. Genomic selection needs to be carefully assessed to meet specific requirements in livestock breeding programs. Frontiers in Genetics 6, 49.10.3389/fgene.2015.00049CrossRefGoogle ScholarPubMed
Kim, KS, Larsen, N, Short, T, Plastow, G and Rothschild, MF 2000. A missense variant of the porcine melanocortin-4 receptor (MC4R) gene is associated with fatness, growth, and feed intake traits. Mammalian Genome 11, 131–135.10.1007/s003350010025CrossRefGoogle ScholarPubMed
Knap, PW and Wang, L 2012. Pig breeding for improved feed efficiency. In Feed efficiency in swine (ed. Patience, JP), pp. 167–179. Wageningen Academic Publishers, Wageningen, the Netherlands.CrossRefGoogle Scholar
Kongsro, J 2014. Genetic gain on body composition in pigs by computed tomography (CT): return on investment. In FAIM III: Farm animal imaging Copenhagen (ed. Maltin, C, Craigie, C and Bünger, L), pp. 28–30. Scotland’s Rural College, Edinburgh, Scotland, UK.Google Scholar
Kools, P, Van Imschoot, G and Van Roy, F 2000. Characterization of three novel human cadherin genes (CDH7, CDH19, and CDH20) clustered on chromosome 18q22–q23 and with high homology to chicken cadherin-7. Genomics 68, 283–295.CrossRefGoogle ScholarPubMed
Kušec, G, Scholz, AM, Baulain, U, Kušec, ID and Bernau, M 2016. Non-invasive techniques for exact phenotypic assessment of carcass composition and tissue growth in domestic animals. Acta Agriculturae Slovenica 5, 12–17.Google Scholar
Lin, J, Wang, C and Redies, C 2014. Restricted expression of classic cadherins in the spinal cord of the chicken embryo. Frontiers in Neuroanatomy 8, 18.10.3389/fnana.2014.00018CrossRefGoogle ScholarPubMed
Lopes, MS, Bovenhuis, H, van Son, M, Nordbø, Ø, Grindflek, E, Knol, E and Bastiaansen, J 2017. Using markers with large effect in genetic and genomic predictions. Journal of Animal Science 95, 59–71.Google ScholarPubMed
Lopes, MS, Silva, FF, Harlizius, B, Duijvesteijn, N, Lopes, PS, Guimarães, SEF and Knol, EF 2013. Improved estimation of inbreeding and kinship in pigs using optimized SNP panels. BMC Genetics 14, 92.10.1186/1471-2156-14-92CrossRefGoogle ScholarPubMed
Lu, D, Ventura-Holman, T, Li, J, McMurray, RW, Subauste, JS and Maher, JF 2005. Abnormal glucose homeostasis and pancreatic islet function in mice with inactivation of the Fem1b gene. Molecular and Cellular Biology 25, 6570–6577.10.1128/MCB.25.15.6570-6577.2005CrossRefGoogle ScholarPubMed
Onteru, SK, Gorbach, DM, Young, JM, Garrick, DJ, Dekkers, JC and Rothschild, MF 2013. Whole genome association studies of residual feed intake and related traits in the pig. PLoS One 8, e61756.10.1371/journal.pone.0061756CrossRefGoogle ScholarPubMed
Patience, JF, Rossoni-Serão, MC and Gutiérrez, NA 2015. A review of feed efficiency in swine: biology and application. Journal of Animal Science and Biotechnology 6, 33.10.1186/s40104-015-0031-2CrossRefGoogle ScholarPubMed
R Development Core Team 2013. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.Google Scholar
Reuser, AJ, Kroos, M, Elferink, RPO and Tager, JM 1985. Defects in synthesis, phosphorylation, and maturation of acid alpha-glucosidase in glycogenosis type II. Journal of Biological Chemistry 260, 8336–8341.Google ScholarPubMed
Rohrer, GA 2000. Identification of quantitative trait loci affecting birth characters and accumulation of backfat and weight in a Meishan-White Composite resource population. Journal of Animal Science 78, 2547–2553.10.2527/2000.78102547xCrossRefGoogle Scholar
Rothschild, MF, Kim, ES and Plastow, GS 2014. Novel tools provide new opportunities for genetic improvement of swine. In 10th World Congress on Genetics Applied to Livestock Production, 17–22 August 2014, Vancouver, BC, Canada.Google Scholar
Veroneze, R, Bastiaansen, JW, Knol, EF, Guimarães, SE, Silva, FF, Harlizius, B, Lopes, MS and Lopes, PS 2014. Linkage disequilibrium patterns and persistence of phase in purebred and crossbred pig (Sus scrofa) populations. BMC Genetics 15, 1.CrossRefGoogle ScholarPubMed
Yang, J, Lee, SH, Goddard, ME and Visscher, PM 2011. GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics 88, 76–82.CrossRefGoogle ScholarPubMed
Supplementary material: File

Silva et al. supplementary material

Tables S1-S2 and Figure S1

Download Silva et al. supplementary material(File)
File 395.6 KB