Advanced Plant Breeding PBG 650
Take-Home Final Exam, Fall 2015
Due 9:30 am on Friday, December 11, 2015
Name
Part 1 – AMMI analysis of Genotype by Environment interaction using R
The Barley Project at OSU strives to develop barley cultivars that are both high yielding and that
provide outstanding flavor in beer. A trial of 34 promising new experimental varieties and 3
standard checks (Golden Promise, BCD47, and Copeland) was conducted at three locations in
Oregon in 2015. Yield trials were conducted in Corvallis, Lebanon, and Madras, OR. Each
experiment was arranged as a Randomized Complete Block Design with two replications. While
quality testing remains to be done, they need your help to determine how well these varieties
are adapted to diverse environments in the target production regions of Oregon.
The data set is available in the file OregonPromise2015.csv.
As an initial step, they calculated the mean and CV% for each entry (i.e., variety) across all of
the blocks and testing sites. The horizontal axis is bisected by the mean yield across all varieties,
and the vertical axis is bisected by the mean CV% for all varieties.
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1) Briefly discuss the concept of stability and the various ways that it can be defined and
achieved. What does the graph above tell us (if anything) about the stability of these
varieties? Use examples from the graph to illustrate your points. Which varieties are the
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best (in your opinion)?
2) A stability analysis (similar to the Eberhart and Russel model) was conducted to look at the
regression of each variety across an environmental index, and the following results were
obtained:
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Source
DF
Location
Block(Location)
Entry
Location*Entry
Heterogeneity of slopes
Dev from Linear Reg
Residual
2
3
36
72
(36)
(36)
108
Type I SS
426578200
10621586.5
35572607.5
53530235.6
19349229.9
34181005.7
27651886
Mean Square
213289100
3540528.8
988128
743475.5
537478.6
949472.4
256036
F Value
60.24
13.83
3.86
2.9
2.1
3.71
Pr > F
0.0038
<.0001
<.0001
<.0001
0.0018
<.0001
Is there evidence for genotype by environment interactions? How can you tell? What do
these results tell us about the nature of those interactions?
2
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3) Refer to Table 1 to identify several varieties that you think show a good level of stability.
Justify your choice of varieties.
Table 1. Stability analysis of barley genotypes
Entry
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Genotype
DH120058
DH120145
DH120166
DH120020
DH120031
DH120089
DH120090
DH120156
DH120285
120314
120329
120330
120331
120341
120363
120365
120366
120374
120381
120384
120510
120516
120520
120521
120529
120536
120543
120657
120661
120671
120691
120709
120715
120731
Golden Promise
BCD47
Copeland
Mean
Yield
lb/acre
linear regression
coefficient
5910
6178
5879
5477
5338
5273
5595
6064
5778
6082
6030
5244
5823
6733
6146
6048
6064
6293
6459
6489
5970
6059
6086
5597
5673
6142
5323
6428
6796
5887
5514
5729
5666
6275
6380
6633
6294
1.25
1.01
0.98
1.07
1.13
1.10
1.16
0.85
1.34
1.60
0.62
0.84
1.14
1.20
0.61
1.03
1.28
0.96
1.11
0.66
0.89
0.88
0.87
0.88
1.08
0.93
0.67
1.27
0.85
1.06
1.14
0.85
0.85
1.09
0.66
1.04
1.05
3
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4) What are some of the limitations of the Eberhart and Russel stability analysis?
5) Describe the components of the model for an AMMI analysis. What are some of the
benefits of AMMI for investigating GXE interactions?
6) Use the R program below to run an AMMI analysis. Interpret the output using guidelines
from lecture and from the article by Malosetti et al. (2013). Delete the program, and cut
and paste a copy of the PC1*PC2 biplot with your discussion (no other output needs to be
submitted). What recommendations would you make to the Barley Project about the
adaptation of their experimental varieties to diverse environments in the target production
regions of Oregon? Use examples to illustrate the principles involved (one or two
paragraphs or about 200 words is sufficient.)
#Analysis of barley MET trial
#Read in the table from a csv file
promise<-read.table("C:/Users/klingj/Desktop/OregonPromise2015.csv",
header=TRUE, sep=",")
#several options for reviewing data structure before the analysis
head(promise) #headings and first six lines of the data
#convert integers to factors
promise$genof <- as.factor(promise$entry)
promise$envf <- as.factor(promise$Location)
promise$blockf <- as.factor(promise$Block)
promise <- subset(promise, select = c(envf, genof, blockf, Yield))
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str(promise)
#compactly displays structure of the dataset
#plot the data
library(ggplot2)
qplot(genof, Yield, data=promise, geom="boxplot")
library(agricolae)
attach(promise)
model<- AMMI(envf, genof, blockf, Yield, console=TRUE, PC=TRUE)
detach(promise)
#print out scaled PCA scores (note alphanumeric sorting)
model$biplot
# biplot PC1 vs average Yield
# number=false turns off renumbering of entries in sorted order
plot(model, first=0,second=1, number=FALSE)
# biplot
plot(model)
# get AMMI predicted GXE effects
model$genXenv
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