Advanced SearchSearch Tips
Estimation of Interaction Effects among Nucleotide Sequence Variants in Animal Genomes
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Estimation of Interaction Effects among Nucleotide Sequence Variants in Animal Genomes
Lee, Chaeyoung; Kim, Younyoung;
  PDF(new window)
Estimating genetic interaction effects in animal genomics would be one of the most challenging studies because the phenotypic variation for economically important traits might be largely explained by interaction effects among multiple nucleotide sequence variants under various environmental exposures. Genetic improvement of economic animals would be expected by understanding multi-locus genetic interaction effects associated with economic traits. Most analyses in animal breeding and genetics, however, have excluded the possibility of genetic interaction effects in their analytical models. This review discusses a historical estimation of the genetic interaction and difficulties in analyzing the interaction effects. Furthermore, two recently developed methods for assessing genetic interactions are introduced to animal genomics. One is the restricted partition method, as a nonparametric grouping-based approach, that iteratively utilizes grouping of genotypes with the smallest difference into a new group, and the other is the Bayesian method that draws inferences about the genetic interaction effects based on their marginal posterior distributions and attains the marginalization of the joint posterior distribution through Gibbs sampling as a Markov chain Monte Carlo. Further developing appropriate and efficient methods for assessing genetic interactions would be urgent to achieve accurate understanding of genetic architecture for complex traits of economic animals.
Animal Genomics;Bayesian Inference;Epistasis;Gibbs Sampling;Single Nucleotide Polymorphism;
 Cited by
Benjamini, Y. and Y. Hochberg. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57:289-300 crossref(new window)

Carlborg, O. and C. S. Haley. 2004. Epistasis: too often neglected in complex trait studies? Nat. Rev. Genet. 5:618-625 crossref(new window)

Charlier, C., W. Coppieters, F. Rollin, D. Desmecht, J. S. Agerholm, N. Cambisano, E. Carta, S. Dardano, M. Dive, C. Fasquelle, J. C. Frennet, R. Hanset, X. Hubin, C. Jorgensen, L. Karim, M. Kent, K. Harvey, B. R. Pearce, P. Simon, N. Tama, H. Nie, S. Vandeputte, S. Lien, M. Longeri, M. Fredholm, R. J. Harvey and M. Georges. 2008. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nat. Genet. 40:449-454 crossref(new window)

Chen, J. F., L. H. Dai, J. Peng, J. L. Li, R. Zheng, B. Zuo, F. E. Li, M. Liu, K. Yue, M G. Lei, Y. Z. Xiong, C. Y. Deng and S. W. Jiang. 2008. New evidence of alleles (V199I and G52S) at the PRKAG3 (RN) locus affecting pork meat quality. Asian-Aust. J. Anim. Sci. 21:471-477

Cheong, H. S., D. H. Yoon, L. H. Kim, B. L. Park, H. W. Lee, S. Namgoong, E. M. Kim, E. R. Chung, I. C. Cheong and H. D. Shin. 2008. Association analysis between insulin-like growth factor binding protein 3 (IGFBP3) polymorphisms and carcass traits in cattle. Asian-Aust. J. Anim. Sci. 21:309-313

Cockerham, C. C. 1954. An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39:859-882 crossref(new window)

Comtet, L. 1974. Advanced combinatorics: the art of infinite expansions. Boston:Reidel

Culverhouse, R., T. Klein and W. Shannon. 2004. Detecting epistatic interactions contributing to quantitative traits. Genet. Epidemiol. 27:141-152 crossref(new window)

Dario, C., D. Carnicella, F. Ciotola, V. Peretti and G. Bufano. 2008. Polymorphism of growth hormone GH1-Alul in Jersey cows and its effect on milk yield and composition. Asian-Aust. J. Anim. Sci. 21:1-5

Efron, B. and R. Tibshirani. 2002. Empirical bayes methods and false discovery rates for microarrays. Genet. Epidemiol. 23:70-86 crossref(new window)

Falconer, D. S. and T. F. C. Mackay. 1996. Introduction to Quantitative Genetics (4th ed). Longmans Green, Harlow, Essex, UK

Fisher, R. A. 1918. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinburgh 3:399-433

Frankel, W. N. and N. J. Schork. 1996. Who's afraid of epistasis? Nat. Genet. 14:371-373 crossref(new window)

Hansen, T. F. and G. P. Wagner. 2001. Modeling genetic architecture: A multilinear theory of gene interaction. Theor. Popul. Biol. 59:61-86 crossref(new window)

Hirschhorn, J. N. and M. J. Daly. 2005. Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6:95-108 crossref(new window)

Hobert, J. P. and G. Casella. 1996. The effect of improper priors on Gibbs sampling in hierarchical linear mixed models. J. Am. Stat. Assoc. 91:1461-1473 crossref(new window)

Kim, J. B., Z. X. Zeng, Y. J. Nam, Y. Kim, S. L. Yang, X. Wu and C. Lee. 2005. Association of mahogany/attractin gene (ATRN) with porcine growth and fat. Asian-Aust. J. Anim. Sci. 18:1383-1386

Kruglyak, L. and D. A. Nickerson. 2001. Variation is the spice of life. Nat. Genet. 27:234-236 crossref(new window)

Lee, C. 2000. Methods and techniques for variance component estimation in animal breeding. Asian-Aust. J. Anim. Sci. 13:413-422

Lee, C. and J. Park. 2007. Estimation of epistasis among finite polygenic loci for complex traits with a mixed model using Gibbs sampling. J. Biomed. Inform. 40:500-506 crossref(new window)

Lee, C. and Y. Kim. 2008. Optimal designs for estimating and testing interaction among multiple loci in complex traits by a Gibbs sampler. Genomics 92:446-451 crossref(new window)

Li, X. L., W. L. He, C. Y. Deng and Y. Z. Xiong. 2007. Associations of polymorphisms in the Mx1 gene with immunity traits in Large White X Meishan F2 offspring. Asian-Aust. J. Anim. Sci. 20:1651-1654

McCarthy, M. I., G. R. Abecasis, L. R. Cardon, D. B. Goldstein, J. Little, J. P. A. Ioannidis and J. N. Hirschhorn. 2008. Genomewide association studies for complex traits: consensus, uncertainty and challenges. Nat. Rev. Genet. 9:356-369 crossref(new window)

Moore, J. H. and S. M. Williams. 2005. Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. BioEssays 27:637-646 crossref(new window)

Nelson, M. R., S. L. Kardia, R. E. Ferrell and C. F. Sing. 2001. A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. Genome Res. 11:458-470 crossref(new window)

Omelka, R., M. Martiniakova, D. Peskovicova and M. Bauerova. 2008. Associations between Alu I polymorphisms in the prolactin receptor gene and reproductive traits of Slovak Large White, White Meaty and Landrace pigs. Asian-Aust. J. Anim. Sci. 21:484-488

Tanner, M. A. 1993. Tools for stastistical inference: methods for the expoloration of posterior distributions and likelihood functions. Springer Series in Statistics, New York, NY, USA

Walbot, W. and N. M. S. Evans. 2003. Unique features of the plant life cycle and their consequences. Nat. Rev. Genet. 4:369-379 crossref(new window)

Wang, Y., H. Li, Y. D. Zhang, Z. L. Gu, Z. H. Li and Q. G. Wang. 2006. Analysis on association of a SNP in the chicken OBR gene with growth and body composition traits. Asian-Aust. J. Anim. Sci. 19:1706-1710

Wang, Y., D. Shu, L. Li, H. Qu, C. Yang and Q. Zhu. 2007. Identification of single nucleotide polymorphism of H-FABP gene and its association with fatness traits in chickens. Asian-Aust. J. Anim. Sci. 20:1812-1819

Wolf, J. B. 2000. Gene interactions from maternal effects. Evolution 54:1882-1898 crossref(new window)

Zhang, N. B., H. Tang, L. Kang, Y. H. Ma, D. G. Cao, Y. Lu, M. Hou and Y. L. Jiang. 2008. Associations of single nucleotide polymorphisms in BMPR-IB gene with egg production in a synthetic broiler line. Asian-Aust. J. Anim. Sci. 21:628-632