Journal of Dairy Science
Volume 93, Issue 6 , Pages 2727-2740 , June 2010

Visualization of results from genomic evaluations

Received 22 September 2009 ,Accepted 3 March 2010.

References 

  1. Abdel-Azim G. An approach to predict and manage Mendelian sampling variance based on dense SNP data. J. Dairy Sci. 2009;92(E Suppl. 1):125;(Abstr.)
  2. Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–265
  3. Carey VJ, Gentry J, Whalen E, Gentleman R. Network structures and algorithms in Bioconductor. Bioinformatics. 2005;21:135–136
  4. Cleveland WS. Visualizing Data. Hobart, NJ: Summit Press; 1993;
  5. Cole JB. PyPedal: A computer program for pedigree analysis. Comput. Electron. Agric. 2007;57:107–113
  6. Cole JB, VanRaden PM, O’Connell JR, Van Tassell CP, Sonstegard TS, Schnabel RD, et al. Distribution and location of genetic effects for dairy traits. J. Dairy Sci. 2009;92:2931–2946
  7. Cole JB, Wiggans GR, VanRaden PM. Genetic evaluation of stillbirth in United States Holsteins using a sire-maternal grandsire threshold model. J. Dairy Sci. 2007;90:2480–2488
  8. Daetwyler HD, Villanueva B, Bijma P, Woolliams JA. Inbreeding in genome-wide selection. J. Anim. Breed. Genet. 2007;124:369–376
  9. Garbe JR, Da Y. A software tool for the graphical visualization of large and complex populations. Acta Genet. Sin. 2003;30:2293–2295
  10. Georges M, Massey JM. Velogenetics, or the synergistic use of marker assisted selection and germ line manipulation. Theriogenology. 1991;35:151–159
  11. Gheorghiu, G. 2006. Sparkplot: Creating sparklines in Python with matplotlib. http://sparkplot.org/wiki/WikiStart Accessed September 9, 2009.
  12. Huang YC, Shanks RD. Visualization of inheritance patterns from graphic representation of additive and dominance relationships between animals. J. Dairy Sci. 1995;78:2877–2883
  13. Hunter JD. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 2007;9:90–95
  14. Kachman SD. Relationship between the choice of a random regression model and the possible shapes of the resulting variance functions. J. Dairy Sci. 2004;87(Suppl. 1):243;(Abstr.)
  15. Langtangen HP. Python Scripting for Computational Science. 3rd ed.. Berlin, Germany: Springer-Verlag; 2008;
  16. Legates JE, Warwick EJ. Breeding and Improvement of Farm Animals. 8th ed.. New York, NY: McGraw-Hill Inc.; 1990;
  17. Lush JL. Animal Breeding Plans. Ames, IA: Iowa State College Press; 1949;
  18. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, et al. Development and characterization of a high density SNP genotyping assay for cattle. PLoS ONE. 2009;4:e5350
  19. National Center for Biotechnology Information. 2009. National Center for Biotechnology Information Website. http://www.ncbi.nlm.nih.gov/ Accessed August 25, 2009.
  20. Odegård J, Sonesson AK, Yazdi MH, Meuwissen THE. Introgression of a major QTL from an inferior into a superior population using genomic selection. Genet. Sel. Evol. 2009;41:38–47
  21. Oliehoek PA, Bijma P. Effects of pedigree errors on the efficiency of conservation decisions. Genet. Sel. Evol. 2009;41:9–19
  22. Oliphant TE. Guide to NumPy. Provo, UT: Brigham Young University; 2006;
  23. Schlesser HN, Shanks RD, Berger PJ, Healey MH. Graphical approach to evaluate genetic estimates of calf survival. J. Dairy Sci. 2009;92:2166–2173
  24. Stein W, Joyner D. SAGE: System for Algebra and Geometry Experimentation. Commun. Comput. Algebra. 2005;39:61–64
  25. Takada T, Mita A, Maeno A, Sakai T, Shitara H, Kikkawa Y, et al. Mouse inter-subspecific consomic strains for genetic dissection of quantitative complex traits. Genome Res. 2008;18:500–508
  26. Tufte ER. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press; 1983;
  27. Tufte ER. Beautiful Evidence. Cheshire, CT: Graphics Press; 2006;
  28. Tukey JW. Exploratory Data Analysis. Reading, MA: Addison-Wesley Publishing Company; 1977;
  29. VanRaden PM. Efficient methods to compute genomic predictions. J. Dairy Sci. 2008;91:4414–4423
  30. VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, et al. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. Dairy Sci. 2009;92:16–24
  31. VanRaden, P. M., and Multi-State Project S-1008. 2006. Net merit as a measure of lifetime profit: 2006 revision. http://gov/reference/nmcalc-2006.htm Accessed Sep.10, 2009.
  32. Wickham, H., A. Cromie, and D. Cook. 2006. Dynamic and interactive graphical methods for animal breeding. Commun. No. 03–15 in Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Brazil.
  33. Wiggans GR, Misztal I, Van Vleck LD. Implementation of an animal model for genetic evaluation of dairy cattle in the United States. J. Dairy Sci. 1988;71(Suppl. 2):54–69
  34. Wright S. The method of path coefficients. Ann. Math. Stat. 1934;5:161–215

PII: S0022-0302(10)00277-8

doi: 10.3168/jds.2009-2763

Journal of Dairy Science
Volume 93, Issue 6 , Pages 2727-2740 , June 2010

Article Tools

* Image Usage & Resolution