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Visualization of Results from Genomic Predictions. Introduction. Genomic evaluation produces lots of numbers Holsteins : 38K SNP × 31 traits Data are difficult to compare Levels of detail are obscured Results cannot easily be tabulated. Visualization.

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Presentation Transcript
introduction
Introduction
  • Genomic evaluation produces lots of numbers
    • Holsteins: 38K SNP × 31 traits
  • Data are difficult to compare
  • Levels of detail are obscured
  • Results cannot easily be tabulated
visualization
Visualization
  • “Graphical methods can retain the information in the data.” ― Deming
  • Complements numerical techniques
    • Tukey (1977), Tufte (1983, 1990, 1997, 2006) , Cleveland (1985, 1993)
  • Some work by animal breeders
    • Huang and Shanks (1995), Kachman (2004), VanRaden and Tooker (2007), Schlesser et al. (2009)
net merit by chromosome
Net Merit by Chromosome

Freddie (1HO08784) - highest Net Merit bull

text with chromosomal ebv sparklines
Text with Chromosomal EBV Sparklines

“For example, cows with positive CEBV for chromosomes 13, 14, 16, 17, 19, or 20 might be selected for breeding to the bull O-Bee Manfred Justice-ET (7HO6417; Figure 5).”

(Cole et al. 2009. J. Dairy Sci. 92(6):2931―2946.)

text with genetic trend figure
Text with Genetic Trend Figure

“The incidence of SB increased from 11.2% in 1980 to 12.0% in 2004 for heifers, and decreased from 5.8% in 1980 to 5.6% in 2005 for multiparous cows.”

(Cole et al. 2007. J. Dairy Sci. 90(5):2480―2488.)

text with genetic trend sparklines
Text with Genetic Trend Sparklines

“The incidence of SB increased from 11.16% in 1980 to 11.08% in 2005 for heifers, and decreased from 5.29% in 1980 to 4.83% in 2005 for multiparous cows.”

(Cole et al. 2007. J. Dairy Sci. 90(5):2480―2488.)

expected relationship matrix 1
Expected Relationship Matrix1

1HO9167 O-Style

1Calculated assuming that all grandparents are unrelated

closing thoughts
Closing Thoughts
  • Substance over style
    • The goal is greater insight
  • Better tools are needed
    • Scripting is essential
  • Color is still difficult
    • Print versus electronic publication
  • We need to share ideas
    • Not all ideas will be good ones
acknowledgments
Acknowledgments
  • Genotyping and DNA extraction:
    • BFGL, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina
  • Computing:
    • AIPL staff (Leigh Walton, Jay Megonigal)
  • Funding:
    • NRI grants 2006-35205-16888 and 2006-35205-16701
    • Agriculture Research Service
    • Holstein, Jersey and Brown Swiss breed associations
    • Cooperative Dairy DNA Repository (CDDR)
cddr contributors
CDDR Contributors
  • National Association of Animal Breeders (Columbia, MO)
    • ABS Global (DeForest, WI)
    • Accelerated Genetics (Baraboo, WI)
    • Alta (Balzac, AB, Canada)
    • Genex (Shawano, WI)
    • New Generation Genetics (Fort Atkinson, WI)
    • Select Sires (Plain City, OH)
    • Semex Alliance (Guelph, ON, Canada)
    • Taurus-Service (Mehoopany, PA)
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