- Analysis of the CAIGE yield trials 2015 (US00073)-0 · 2017-03-13 · -0.8-0.5-0.2 0.1 0.4 0.7...

Analysis of the CAIGE yield trials 2015 (US00073)

Chong You1, Daniel Tolhurst1 & Bev Gogel2

1University of Wollongong [email protected], [email protected]

2University of Adelaide [email protected]

CAIGE annual meeting, Waite Campus, Adelaide, 7 March, 2016

Chong You, Daniel Tolhurst & Bev Gogel

Acknowledgments

Richard Trethowan

Chong You, Daniel Tolhurst

Alison Smith & Brian Cullis

Grains Research and Development Corporation


CAIGE yield trials 2015: summary of trials

Table: Summary of trials for the 2015 yield trials

Site Location Organisation State Ranges Rows Plots p (%)

Site 1 Narrabri University of Sydney NSW(North) 16 25 400 30.6Site 2 Northstar AGT NSW(North) 12 34 408 33.3Site 3 Junee LPB NSW(South) 12 34 408 33.3Site 4 Roseworthy AGT SA 12 34 408 32.4Site 5 Longerenong Bayer VIC 12 34 408 32.9Site 6 Toodyay EdStar Genetics WA 12 34 408 32.5Site 7 Mukinbudin Intergrain WA 12 34 408 32.2

• the Longerenong site (Horsham) was badly affected by the drought and data was notrecorded

• −→ 6 trials for analysis


CAIGE yield trials 2015: trial locations


p-rep trials

in p-rep trials

• a proportion of the lines replicated with thereplicates separated into 2 replicate blocks(green plots)

• the remaining lines unreplicated (white plots)

• Cullis etal. (2006)

• little loss of efficiency for p-rep in terms ofresponse to selection

• p-rep designs for spring and durum wheattrials for CIM00018 arm of CAIGE project Range

Row

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

1 2 3 4 5 6 7 8 9 10 11 12

58

262

301

30

45

9

19

175

7

2

81

230

20

21

303

137

24

197

57

74

16

161

102

202

233

246

247

112

190

131

173

298

166

237

171

138

52

152

83

186

210

201

261

278

22

119

10

44

147

243

253

194

222

162

290

195

251

270

14

258

235

117

289

159

75

242

164

178

85

184

38

120

297

271

295

66

15

203

200

300

70

305

146

88

80

115

206

106

109

128

101

249

224

264

37

73

151

145

12

292

239

29

269

56

135

302

97

48

291

267

191

17

193

244

39

257

47

40

99

71

209

155

281

296

82

94

265

153

196

307

275

308

211

32

294

86

87

3

118

46

148

185

266

174

23

221

205

189

229

95

5

169

214

154

34

98

43

100

181

133

136

26

42

182

50

76

228

176

180

78

254

268

125

160

208

156

130

122

163

126

89

63

177

69

60

241

92

59

212

11

108

204

279

216

284

49

142

129

103

304

168

306

299

4

19

250

302

254

134

53

48

213

13

189

91

60

199

88

25

194

276

142

207

154

121

101

35

191

84

235

151

123

231

96

242

90

218

304

36

148

139

67

295

124

143

45

65

1

230

240

227

89

157

305

169

236

74

273

195

8

284

77

246

79

28

281

85

286

50

173

51

243

215

106

160

138

223

179

174

234

265

111

289

31

95

280

177

55

303

113

107

43

217

279

270

6

225

150

237

144

285

12

248

220

306

239

56

86

114

288

156

291

141

38

187

201

41

81

167

300

27

54

115

92

252

105

282

209

188

224

198

133

64

24

307

158

131

259

238

228

118

18

58

232

152

287

7

127

203

245

61

39

163

263

21

47

255

57

98

102

165

277

233

149

37

68

258

226

178

219

192

78

256

32

297

269

72

3

283

301

274

186

293

15

221

172

193

10

272

132

212

170

99

104

296

290

116

26

110

112

140

308

62

298

260

33

29

299


MET analysis

have combined the data from the 6 individuals trials into a single analysis and analysed ittogether in a multi-environment trial (MET) analysis

why?

• we want to get the best predictions of the genetic effects for each trial andmore trials = more information = more reliable estimates of the genetic effects

• we want to estimate the genetic correlation between trials ... this will tell us

– in which trials the genotypes behave similarly

... high estimated genetic correlation −→ similar ranking of genotypes

– in which trials the genotypes behave differently

... lower or negative estimated genetic correlation −→ some crossover in ranking of genotypes

the MET analysis will give us statistically valid (the best) information about g × e


Statistical analysis: single site analyses

smoothtrend

randomrow

randomblock

extraneousfixed

genotype

overallmean

response

smoothtrend

extraneous

random

randomrow

randomblock

overallmean

response

smoothtrend

extraneous

random

randomrow

randomblock

extraneousfixed

genotype

overallmean

response

smoothtrend

randomblock

extraneousfixed

genotype

overallmean

response

smoothtrend

extraneous

random

randomrow

extraneousfixed

genotype

overallmean

response

smoothtrend

extraneous

random

randomrow

randomblock

extraneousfixed

genotype

overallmean

response

the first MET analysis ≡ to fitting single-site analyses

yield = mean + genetic+ non-genetic+ error

• non-genetic terms

– random blocking terms to reflect the randomisation process for each trial ...reps, main-plots,...

– extraneous terms as necessary for a better fit of the data for each trial ...linear row, covariates,...

• use spatial methods to allow for smooth trend across the field at the error level

• genetic effects are assumed to be independent/uncorrelated between trials


Statistical analysis: MET

smoothtrend

randomrow

randomblock

extraneousfixed

genotype

correlation

overallmean

response

smoothtrend

extraneous

random

randomrow

randomblock

genotype

correlation

overallmean

response

smoothtrend

extraneous

random

randomrow

randomblock

extraneousfixed

genotype

correlation

overallmean

response

smoothtrend

randomblock

extraneousfixed

genotype

correlation

overallmean

response

smoothtrend

extraneous

random

randomrow

extraneousfixed

genotype

correlation

overallmean

response

smoothtrend

extraneous

random

randomrow

randomblock

extraneousfixed

genotype

overallmean

response

transition to a MET analysis

• non-genetic terms– a different mean level for each trial– random blocking terms to reflect the randomisation process for each trial ...reps, main-plots,...

– extraneous terms as necessary for a better fit of the data for each trial ...linear row, covariates,...

• use spatial methods to allow for smooth trend across the field at the error level

• we fit a factor analytic (FA) model to the g × e interaction effects– introduces genetic correlation between trials


FA facts

the FA approach of Smith et al. (2005)

– used routinely for the analysis of MET data from plant breeding programs in Australia– it fits

– a different genetic variance for each trial– a different genetic covariance (hence correlation) for each pair of trials

– can be likened to embedding a principal component analysis (PCA) for the g × einteraction effects within the wider model ... means we can get at

– the principal components/factors driving the variation in the effects

– how the individual varieties respond across each of these factors

– has neat properties that facilitate some really informative graphics


Key outputs

the 2 key outputs for an FA analysis are

• the full set of predicted genetic effects for each trial: the g × e effects

• the estimated genetic correlation matrix

we also get

• key components associated with the PCA-style modelling of the g × e interaction effects


MET analysis: output files

• estimated genetic correlation matrix • full set of predicted genetic effects


MET analysis: heatmap of estimated genetic correlation matrix

Site

Site

Junee

Roseworthy

Narribri

Mukinbudin

Toodyay

Northstar

June

e

Ros

ewor

thy

Nar

ribri

Muk

inbu

din

Tood

yay

Nor

thst

ar

−1.0

−0.5

0.0

0.5

1.0

• sites re-ordered on the basis of a dendrogram

• Toodyay (WA) and Mukinbudin (WA) highly correlated

• Junee (Southern NSW) and Roseworthy (SA) highly correlated

• little correlation between (Roseworthy, Junee) and Mukinbudin


MET analysis: plot of predicted genetic effects

−0.8

−0.5

−0.2

0.1

0.4

0.7

1.0

Nor(4.39)

Nar(4.66)

Jun(3.02)

Ros(2.8)

Muk(2.38)

Too(2.95)

Site and site mean yield (t/ha)

Pro

duct

ion

Val

ue (

t/ha)

Variety

Axe

Borlaug 100

Crusader

EGA Gregory

Emu Rock

Gladius

Livingston

Mace

Magenta

Scout

Suntop

Wyalkatchem

Yitpi

Australian checks

• full set of predicted genetic effects

• called production values in the NVT

• NVT PV-PLUS style graphic

• 13 Australian checks



for a given trial

• the dashed line represents theexpected average yield (over allvarieties included in the dataset) forthat trial

• a positive genetic effect (> 0)indicates that a variety is expected toyield higher than average

• a negative genetic effect (< 0)indicates that a variety is expected toyield lower than average

• a genetic effect that is ≈ 0 indicatesthat a variety is expected to yieldabout average

−0.8

−0.5

−0.2

0.1

0.4

0.7

1.0

Nor(4.39)

Nar(4.66)

Jun(3.02)

Ros(2.8)

Muk(2.38)

Too(2.95)


Pro

duct

ion

Val

ue (

t/ha)

Variety

Axe

Borlaug 100

Crusader

EGA Gregory

Emu Rock

Gladius

Livingston

Mace

Magenta

Scout

Suntop

Wyalkatchem

Yitpi

Australian checks



Site

Site

Junee

Roseworthy

Narribri

Mukinbudin

Toodyay

Northstar

June

e

Ros

ewor

thy

Nar

ribri

Muk

inbu

din

Tood

yay

Nor

thst

ar

−1.0

−0.5

0.0

0.5

1.0

−0.8

−0.5

−0.2

0.1

0.4

0.7

1.0

Nor(4.39)

Nar(4.66)

Jun(3.02)

Ros(2.8)

Muk(2.38)

Too(2.95)


Pro

duct

ion

Val

ue (

t/ha)

Variety

Axe

Borlaug 100

Crusader

EGA Gregory

Emu Rock

Gladius

Livingston

Mace

Magenta

Scout

Suntop

Wyalkatchem

Yitpi

Australian checks


PV-plus plot for the top 10 lines

0.1

0.4

0.7

1.0

Nor(4.39)

Nar(4.66)

Jun(3.02)

Ros(2.8)

Muk(2.38)

Too(2.95)


Pro

duct

ion

Val

ue (

t/ha)

Variety

103.ZWB14

142.ZWB14

189.ZWB14

27.ZWB14

2.ZWB14

35.ZWB14

43.ZWB14

51.ZWB14

73.ZWB14

Mace

• variety 2.ZWB14 was strong in terms of its consistently high yield and relative stabilityacross trials


An APP


Reference

Smith, A., Cullis, B. & Thompson, R. (2001). Analyzing variety by environment data usingmultiplicative mixed models and adjustments for spatial field trend. Biometrics 57,11381147.

Cullis, B. R., Smith, A. B. & Coombes, N. E. (2001). On the design of early generationvariety trials with correlated data. Journal of Agricultural, Biological, and EnvironmentalStatistics 11(4), 381393.


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