Advertisement

Genetica

, Volume 125, Issue 1, pp 103–113 | Cite as

Evaluation of Candidate Gene Effects for Beef Backfat via Bayesian Model Selection

  • Xiao-Lin Wu
  • Michael D. MacNeil
  • Sachinadan De
  • Qian-Jun Xiao
  • Jennifer J. Michal
  • Charles T. Gaskins
  • Jerry J. Reeves
  • Jan R. Busboom
  • Raymond W.  WrightJr.
  • Zhihua Jiang
Article

Abstract

Candidate gene approaches provide tools for exploring and localizing causative genes affecting quantitative traits and the underlying variation may be better understood by determining the relative magnitudes of effects of their polymorphisms. Diacyglycerol O-acyltransferase 1 (DGAT1), fatty acid binding protein (heart) 3 (FABP3), growth hormone 1 (GH1), leptin (LEP) and thyroglobulin (TG) have been previously identified as genes contributing to genetic control of subcutaneous fat thickness (SFT) in beef cattle. In the present research, Bayesian model selection was used to evaluate effects of these five candidate genes by comparing competing non-nested models and treating candidate gene effects as either random or fixed. The analyses were implemented in SAS to simplify the programming and computation. Phenotypic data were gathered from a F2 population of Wagyu × Limousin cattle. The five candidate genes had significant but varied effects on SFT in this population. Bayesian model selection identified the DGAT1 model as the one with the greatest model probability, whether candidate gene effects were considered random or fixed, and DGAT1 had the greatest additive effect on SFT. The SAS codes developed in the study are freely available and can be downloaded at: http://www.ansci.wsu.edu/programs/.

Keywords

backfat Bayesian analysis beef candidate genes model selection 

Abbreviations

BIC

Bayesian Information Criterion

DGAT1

diacyglycerol O-acyltransferase 1

FABP3

fatty acid binding protein (heart) 3

GH1

growth hormone 1

LEP

leptin

MLE

maximum likelihood estimates

QTL

quantitative trait loci

REML

residual maximum likelihood

SFT

subcutaneous fat thickness

TG

thyroglobulin

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barendse, W.J., 1999. Assessing lipid metabolism. Patent International Publication number: WO 99/23248. World International Property Organization.Google Scholar
  2. Bollen, K.A. 1989Structural Equations with Latent VariablesWileyNew YorkGoogle Scholar
  3. Buchanan, F.C., Fitzsimmons, C.J., Kessel, A.G., Thue, T.D., Winkelman, D.C. 2002Association of a missense mutation in the bovine leptin gene with carcass fat content and leptin mRNA levelsGenet. Sel. Evol.34105116CrossRefPubMedGoogle Scholar
  4. Cases, S., Smith, S.L., Zheng, Y.W., Myers, H.M., Lear, S.R. 1998Identification of a gene encoding an acyl CoA: diacylglycerol acyltransferase, a key enzyme in triacylglycerol synthesisProc. Natl. Acad. Sci. USA951301813023CrossRefPubMedGoogle Scholar
  5. Campbell, E.M., Nonneman, D., Rohrer, G.A. 2003Fine mapping a quantitative trait locus affecting ovulation rate␣in swine on chromosome 8J. Anim. Sci.8117061714PubMedGoogle Scholar
  6. Congdon, P. 2001Bayesian Statistical ModelingJohn Wiley & SonsNew YorkGoogle Scholar
  7. Craddock, N., Dave, S., Greening, J. 2001Association studies of bipolar disorderBipolar Disord.3284298CrossRefPubMedGoogle Scholar
  8. Fries, R. & A. Winter, 2002. Method of testing a mammal for its predisposition for fat content of milk and/or its predisposition for meat marbling. Patent, International Application Number: PCT/EP 02/07520. World International Property Organization.Google Scholar
  9. Grisart, B., Farnir, F., Karim, L., Cambisano, N., Kim, J.J. 2004Genetic and functional confirmation of the causality of the DGAT1 K232A quantitative trait nucleotide in affecting milk yield and compositionProc. Natl. Acad. Sci. USA10123982403CrossRefPubMedGoogle Scholar
  10. Henderson, C.R. 1984Application of Linear Models in Animal BreedingUniversity of GuelphGuelph, Ontario, CanadaGoogle Scholar
  11. Jannink, J.-L., Wu, X.-L. 2003Estimating allelic number and identity in state of QTLs in interconnected familiesGenet. Res.81133144CrossRefPubMedGoogle Scholar
  12. Riquet, J., Coppieters, W., Cambisano, N., Arranz, J.J., Berzi, P. 1999Fine-mapping of quantitative trait loci by identity by descent in outbred populations: application to milk production in dairy cattleProc. Natl. Acad. Sci. USA9692529257CrossRefPubMedGoogle Scholar
  13. Roberts, G.O. 1995Markov chain concepts related to sampling algorithmGilks, W.R.Richardson,  S.Spiegelhalter,  D.J. eds. Markov Chain Monte Carlo in PracticeChapman & HallLondon4548Google Scholar
  14. Roy, R., Calvo, J.H., Hayes, H., Rodellar, C., Eggen, A. 2003Fine mapping of the bovine heart fatty acid-binding protein gene (FABP3) to BTA2q45 by fluorescence in situ hybridization and radiation hybrid mappingAnim. Genet.34466467CrossRefPubMedGoogle Scholar
  15. Schwarz, G. 1978Estimating the dimension of the modelAnn. Stat.6461464Google Scholar
  16. Smith, S.J., Cases, S., Jensen, D.R., Chen, H.C., Sande, E.,  et al. 2000Obesity resistance and multiple mechanisms of triglyceride synthesis in mice lacking DGATNat. Genet.258790CrossRefPubMedGoogle Scholar
  17. Taylor, J.F., Coutinho, L.L., Herring, K.L., Gallagher, D.S., Brenneman, R.A. 1998Candidate gene analysis of GH1 for effects on growth and carcass composition of cattleAnim. Genet.29194201PubMedGoogle Scholar
  18. Thaller, G., Kuhn, C., Winter, A., Ewald, G., Bellmann, O. 2003DGAT1, a new positional and functional candidate gene for intramuscular fat deposition in cattleAnim. Genet.34354357CrossRefPubMedGoogle Scholar
  19. Tierney, L. 1994Markov chains for exploring posterior distribution (with discussion)Ann. Stat.2217011762Google Scholar
  20. Winter, A., Kramer, W., Werner, F.A., Kollers, S., Kata, S. 2002Association of a lysine-232/alanine polymorphism in a bovine gene encoding acyl-CoA:diacylglycerol acyltransferase (DGAT1) with variation at a quantitative trait locus for milk fat contentProc. Natl. Acad. Sci. USA9993009305CrossRefPubMedGoogle Scholar
  21. Wolfinger, R.D., Kass, R.E. 2000Non-conjugate Bayesian analysis of variance component modelsBiometrics56768774CrossRefPubMedGoogle Scholar
  22. Womack, J.E., Johnson, J.S., Owens, E.K., Rexroad, C.E.,III, Schlapfer, J. 1997A whole-genome radiation hybrid panel for bovine gene mappingMamm. Genome8854856CrossRefPubMedGoogle Scholar
  23. Wu, X.-L., Jannink, J.-L. 2004Optimal sampling of a population to determine QTL location, variance, and allelic numberTheor. Appl. Genet.10814341442CrossRefPubMedGoogle Scholar
  24. Xu, S. 1998Mapping quantitative trait loci using multiple families of line crossesGenetics148517524PubMedGoogle Scholar
  25. Yi, N., Xu, S. 2000Bayesian mapping of quantitative trait loci under the identity-by-descent-based variance component modelGenetics156411422PubMedGoogle Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Xiao-Lin Wu
    • 1
  • Michael D. MacNeil
    • 2
  • Sachinadan De
    • 1
    • 3
  • Qian-Jun Xiao
    • 1
  • Jennifer J. Michal
    • 1
  • Charles T. Gaskins
    • 1
  • Jerry J. Reeves
    • 1
  • Jan R. Busboom
    • 1
  • Raymond W.  WrightJr.
    • 1
  • Zhihua Jiang
    • 1
  1. 1.Department of Animal SciencesWashington State UniversityPullmanUSA
  2. 2.Fort Keogh Livestock and Range Research LaboratoryUSDA-ARSMiles CityUSA
  3. 3.Animal Biotechnology CenterNational Dairy Research InstituteKarnalIndia

Personalised recommendations