Phenotyping

1
Phenotyping
The significance level and reproducibility of the
QTL are only as good as the assay you use for
phenotyping
Examples
Diseases - measure by eye (e.g. diseased) or
ELISA, etc. Fruit color - spectrophotometer,
visualization Yield - number of fruit, weight of
whole harvest, etc. Fruit or grain size -
measure, number per unit weight
2
Kinds of data
Categorical Nominal unordered
Ex. Fruit shape 1round 2blocky 3long
Test of Independence (e.g. chi-square)
3
Categorical Ordinal - some natural order
Ex. Fruit size 1 smallest, 5 biggest
Association test (e.g. Kendalls tau)
4
Continuous data
Non-discrete data Poisson distribution
Ex. Yield, sugars, height
Regressions, etc.
5
Special issues in Marker Assisted Selection
Dont guess! Missing data is better than wrong
data Missing data is different than a score of
0 ex. Yield a score of 0 grams is much worse
than an unknown yield