Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-27T10:43:32.280Z Has data issue: false hasContentIssue false
  • English
  • Français

Genomic dissection and prediction of feed intake and residual feed intake traits using a longitudinal model in F2 chickens

Published online by Cambridge University Press:  22 December 2017

H. Emamgholi Begli ,
R. Vaez Torshizi ,
A. A. Masoudi ,
A. Ehsani  and
J. Jensen
H. Emamgholi Begli
Affiliation:
Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, PO Box 14115–336, Pajoohesh Blvd, 149771311 Tehran, Iran
R. Vaez Torshizi*
Affiliation:
Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, PO Box 14115–336, Pajoohesh Blvd, 149771311 Tehran, Iran
A. A. Masoudi
Affiliation:
Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, PO Box 14115–336, Pajoohesh Blvd, 149771311 Tehran, Iran
A. Ehsani
Affiliation:
Department of Animal Science, Faculty of Agriculture, Tarbiat Modares University, PO Box 14115–336, Pajoohesh Blvd, 149771311 Tehran, Iran
J. Jensen
Affiliation:
Department of Molecular Biology and Genetics, Aarhus University, Blichers Alle 20, 8830 Tjele, Denmark
*
†E-mail: rasoult@modares.ac.ir
Get access

Abstract

Feed efficiency traits (FETs) are important economic indicators in poultry production. Because feed intake (FI) is a time-dependent variable, longitudinal models can provide insights into the genetic basis of FET variation over time. It is expected that the application of longitudinal models as part of genome-wide association (GWA) and genomic selection (i.e. genome-wide selection (GS)) studies will lead to an increase in accuracy of selection. Thus, the objectives of this study were to evaluate the accuracy of estimated breeding values (EBVs) based on pedigree as well as high-density single nucleotide polymorphism (SNP) genotypes, and to conduct a GWA study on longitudinal FI and residual feed intake (RFI) in a total of 312 chickens with phenotype and genotype in the F2 population. The GWA and GS studies reported in this paper were conducted using β-spline random regression models for FI and RFI traits in a chicken F2 population, with FI and BW recorded for each bird weekly between 2 and 10 weeks of age. A single SNP regression approach was used on spline coefficients for weekly FI and RFI traits, with results showing that two significant SNPs for FI occur in the synuclein (SNCAIP) gene. Results also show that these regions are significantly associated with the spline coefficients (q2) for 5- and 6-week-old birds, while GWA study results showed no SNP association with RFI in F2 chickens. Estimated breeding value predictions obtained using a pedigree-based best linear unbiased prediction (ABLUP) model were then compared with predictions based on genomic best linear unbiased prediction (GBLUP). The accuracy was measured as correlation between genomic EBV and EBV with the phenotypic value corrected for fixed effects divided by the square root of heritability. The regression of observed on predicted values was used to estimate bias of methods. Results show that prediction accuracies using GBLUP and ABLUP for the FI measured from 2nd to 10th week were between 0.06 and 0.46 and 0.03 and 0.37, respectively. These results demonstrate that genomic methods are able to increase the accuracy of predicted breeding values at later ages on the basis of both traits, and indicate that use of a longitudinal model can improve selection accuracy for the trajectory of traits in F2 chickens when compared with conventional methods.

Keywords

chickengenome-wide associationgenomic selectionfeed intakeresidual feed intake
Type
Research Article
Information
animal , Volume 12 , Issue 9 , September 2018 , pp. 1792 - 1798
Copyright
© The Animal Consortium 2017 

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

Benjamini, Y and Hochberg, Y 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57, 289–300.Google Scholar
Browning, BL and Browning, SR 2009. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. The American Journal of Human Genetics 84, 210–223.Google Scholar
Button, KS, Ioannidis, JPA, Mokrysz, C, Nosek, BA, Flint, J, Robinson, ESJ and Munafò, MR 2013. Power failure: why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience 14, 365–376.Google Scholar
Calus, MP, Meuwissen, TH, de Roos, AP and Veerkamp, RF 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics 178, 553–561.Google Scholar
Daetwyler, HD, Swan, AA, van der Werf, JHJ and Hayes, BJ 2012. Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation. Genetics Selection Evolution 44, 33.Google Scholar
De Koning, DJ, Haley, CS, Windsor, D, Hocking, PM, Griffin, H, Morris, A, Vincent, J and Burt, DW 2004. Segregation of QTL for production traits in commercial meat-type chickens. Genetical Research 83, 211–220.Google Scholar
Diggle, PJ, Heagery, PJ, Liang, KY and Zeger, SL 2002. Analysis of Longitudinal Data. Oxford University Press, Oxford, UK.Google Scholar
Emamgholi Begli, H, Vaez Torshizi, R, Masoudi, AA and Ehsani, A 2016. Longitudinal analysis of body weight, feed intake and residual feed intake in F2 chickens. Livestock Science 184, 28–34.Google Scholar
Engelender, S, Kaminsky, Z, Guo, X, Sharp, AH, Amaravi, RK, Kleiderlein, JJ, Margolis, RL, Troncoso, JC, Lanahan, AA, Worley, PF, Dawson, VL, Dawson, TM and Ross, CA 1999. Synphilin-1 associates with alpha-synuclein and promotes the formation of cytosolic inclusions. Nature Genetics 22, 110–114.Google Scholar
Fu, W, Dekkers, JCM, Lee, WR and Abasht, B 2015. Linkage disequilibrium in crossbred and pure line chickens. Genetics Selection Evolution 47, 11.Google Scholar
George, JM 2001. The synucleins. Genome Biology 3, reviews3002.1–3002.6.Google Scholar
Guo, J, Jorjani, H and Carlborg, O 2012. A genome-wide association study using international breeding-evaluation data identifies major loci affecting production traits and stature in the Brown Swiss cattle breed. BMC Genetics 13, 82.Google Scholar
Habier, D, Fernando, RL and Dekkers, JCM 2007. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397.Google Scholar
Hayes, BJ, Visscher, PM and Goddard, ME 2009. Increased accuracy of selection by using the realized relationship matrix. Genetics Research 91, 47–60.Google Scholar
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.Google Scholar
Hume, DA, Whitelaw, CBA and Archibald, AL 2011. The future of animal production: improving productivity and sustainability. Journal of Agricultural Science 149, 9–16.Google Scholar
Kang, H, Zhou, L, Mrode, R, Zhang, Q and Liu, JF 2016. Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait. Heredity 1–9.Google Scholar
Kennedy, BW, Van de Werf, JHJ and Meuwissen, TH 1993. Genetics and statistical properties of residual feed intake. Journal of Animal Science 71, 3239–3250.Google Scholar
Kerner, B, North, KE and Fallin, MD 2009. Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of group 14. Genetic Epidemiology 33 (Suppl 1), S93–S98.Google Scholar
Koivula, M, Stranden, I, Poso, J, Aamand, GP and Mantysaari, EA 2015. Single-step genomic evaluation using multitrait random regression model and test-day data. Journal of Dairy Science 98, 2775–2784.Google Scholar
Li, X, Tamashiro, KL, Liu, Z, Bello, NT, Wang, X, Aja, S, Bi, S, Ladenheim, EE, Ross, CA, Moran, TH and Smith, WW 2012. A novel obesity model: synphilin-1-induced hyperphagia and obesity in mice. International Journal of Obesity 36, 1215–1221.Google Scholar
Lillehammer, M, Meuwissen, TH and Sonesson, AK 2011. Genomic selection for maternal traits in pigs. Journal of Animal Science 89, 3908–3916.Google Scholar
Liu, J, Li, T, Yang, D, Ma, R, Moran, TH and Smith, WW 2012. Synphilin-1 alters metabolic homeostasis in a novel Drosophila obesity model. International Journal of Obesity 36, 1529–1536.Google Scholar
Liu, T, Qu, H, Luo, C, Shu, D, Wang, J, Lund, MS and Su, G 2014. Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens. BMC Genetics 15, 110.Google Scholar
Lourenco, DAL, Fragomeni, BO, Tsuruta, S, Aguilar, I, Zumbach, B, Hawken, RJ, Legarra, A and Misztal, I 2015. Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken. Genetic Selection Evolution 47, 56.Google Scholar
Luan, T, Woolliams, JA, Lien, S, Kent, M, Svendsen, M and Meuwissen, TH 2009. The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183, 1119–1126.Google Scholar
Lund, MS, de Roos, APW, de Vries, AG, Druet, T, Ducrocq, V, Fritz, S, Guillaume, F, Guldbrandtsen, B, Liu, Z, Reents, R, Schrooten, C, Seefried, F and Su, G 2011. A common reference population from four European Holstein populations increases reliability of genomic predictions. Genetic Selection Evolution 43, 43.Google Scholar
Madsen, P and Jensen, J 2013. A user’s guide to DMU – a package for analysing multivariate mixed models. Version 6, Release 5.2. Department of Molecular Biology and Genetics, Center of Quantitative Genetics and Genomics, University of Aarhus, Tjele, Denmark. http://dmu.agrsci.dk/DMU/Doc/Current/dmuv6_guide.5.2.pdf Google Scholar
Meuwissen, THE, Hayes, BJ and Goddard, ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.Google Scholar
Onteru, SK, Gorbach, DM, Young, JM, Garrick, DJ, Dekkers, JCM and Rothschild, MF 2013. Whole genome association studies of residual feed intake and related traits in the pig. PLoS ONE 8, e61756.Google Scholar
Saatchi, M, McClure, MC, McKay, SD, Rolf, MM, Kim, J, Decker, JE, Taxis, TM, Chapple, RH, Ramey, HR, Northcutt, SL, Bauck, S, Woodward, B, Dekkers, JCM, Fernando, RL, Schnabel, RD, Garrick, DJ and Taylor, JF 2011. Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genetics Selection Evolution 43, 40.Google Scholar
Santana, MHA, Utsunomiya, YT, Neves, HHR, Gomes, RC, Garcia, JF, Fukumasu, H, Silva, SL, Oliveira Junior, GA, Alexandre, PA, Leme, PR, Brassaloti, RA, Coutinho, LL, Lopes, TG, Meirelles, FV, Eler, JP and Ferraz, JBS 2014. Genome-wide association analysis of feed intake and residual feed intake in Nellore cattle. BMC Genetics 15, 21.Google Scholar
Sitlani, CM, Rice, KM, Lumley, T, McKnight, B, Cupples, LA, Avery, CL, Noordam, R, Stricker, BHC, Whitsel, EA and Psaty, BM 2015. Generalized estimating equations for genome-wide association studies using longitudinal phenotype data. Statistics in Medicine 34, 118–130.Google Scholar
Smith, EN, Chen, W, Kähönen, M, Kettunen, J, Lehtimäki, T, Peltonen, L, Raitakari, OT, Salem, RM, Schork, NJ, Shaw, M, Srinivasan, SR, Topol, EJ, Viikari, JS, Berenson, GS and Murray, SS 2010. Longitudinal genome-wide association of cardiovascular disease risk factors in the Bogalusa heart study. PLoS Genetics 6, e1001094.Google Scholar
Su, G and Madsen, P 2012. User’s guide for Gmatrix – a program for computing genomic relationship matrix. Department of Genetics and Biotechnology, Aarhus University, Tjele, Denmark. http://dmu.agrsci.dk/Gmatrix/Doc/Previous/Gmatrix-User-Guide.pdf Google Scholar
Van Kaam, JB, Groenen, MA, Bovenhuis, H, Veenendaal, A, Vereijken, AL and Van Arendonk, JA 1999. Whole genome scan in chickens for quantitative trait loci affecting carcass traits. Poultry Science 78, 1091–1099.Google Scholar
Wolc, A, Arango, J, Settar, P, Fulton, JE, O’Sullivan, NP, Preisinger, R, Fernando, R, Garrick, DJ and Dekkers, JCM 2013. Analysis of egg production in layer chickens using a random regression model with genomic relationships. Poultry Science 92, 1486–1491.Google Scholar
Zhang, Z, Zhang, Q and Ding, XD 2011. Advances in genomic selection in domestic animals. Chinese Science Bulletin 56, 2655–2663.Google Scholar