Genetic Evaluation for Late Maturity A

Genetic Evaluation for Late-Maturity

α

-Amylase Activity in Bread

Wheat using GGE Model and a Mixed Linear Model Approach

*Golam Rasul, Karl D. Glover, Padmanaban G. Krishnan

1

, Jixiang Wu, and William A. Berzonsky

2

Department of Plant Science, South Dakota State University; *Presenting Author; 1_{Department of Health and Nutritional Sciences, South Dakota State University;} 2 _{Bayer CropScience LP, Lincoln, Nebraska.}

Pre-harvest sprouting (PHS) in wheat is generally associated with high levels of late-maturity -amylase activity (LMAA), one of the four modes of -amylase enzyme accumulation. -Amylase is one of the enzymes that occurs naturally in all wheat and is found mainly in the aleurone layer. This enzyme is activated during the germination or sprouting process and its activity can quickly break down starch and convert it into free sugar molecules, resulting in a loss of flour viscosity. Elevated levels of -amylase are a concern in bread making because this enzyme breaks down starch granules in wheat flour when mixed with water causing the dough to become wet, sticky and difficult to handle.

Late-maturity -amylase activity appears to be a genetic defect as it is limited to certain genotypes that are more subject to influences of environmental factors. Therefore, an international collection of 18 spring wheat genotypes and another set of 15 spring wheat cultivars adapted to South Dakota (SD) were assessed for genetic evaluation for LMAA over 5 and 9 environments, respectively. The datasets were analysed using a GGE (genotype and genotype-by-environment interaction) model with a mixed linear model approach MINQUE (minimum norm quadratic unbiased estimation), and stability analysis was presented using AMMI (additive main and multiplicative interaction) bi-plot on R software.

Figure 2: Phenotypic mean and genotypic effect of 15

adapted cultivars from South Dakota evaluated for LMAA over 9 environments

Table 1: Estimated variance components for 18 genotypes from international collections and for 15 regionally adapted cultivars from South Dakota evaluated for LMAA

Jixiang Wu, Johnie N. Jenkins and Jack C. McCarty, 2013. Package gtools_1.0 , An R

Package for Quantitative Genetics Data Analyses, Version 1.0. Plant Science Department, South Dakota State University, Brookings, SD 57007, USA.

This research was a contribution of the Department of Plant Science, South Dakota State University. The author was financially supported by Monsanto through the Monsanto Fellowships in Plant Breeding program. We would like to show our gratitude to Jonathan Kleinjan, Spring Wheat Breeding Crews, and Fellow Graduate Students for their assistance in our field trials and in screening materials.

RESULTS AND DISCUSSIONS

C D

Email: Golam.Rasul@sdstate.edu; rasulg27@gmail.com

 To estimate genetic variance components and broad-sense heritability for LMAA in an international collections and in a set of adapted cultivars from South Dakota

 To predict genetic effects, genotype-by-environment interaction effects and stability analysis for G x E

A

MATERIALS AND METHODS

Table 2: Estimated variance components expressed as proportions to the phenotypic variance for LMAA evaluated for 18 genotypes from international collections and for 15 regionally adapted cultivars from South Dakota

Figure 1: Phenotypic mean and genotypic effect of 18 genotypes from international collections evaluated for LMAA over 5 environments

B

INTRODUCTION

OBJECTIVES

CONCLUSIONS

The observation for ith genotype grown in jth block in hth environment can be expressed as the following linear model:

y_hij = µ + E_h + G_i + GE_hi + B_j(h) + e_hij

where, µ = population mean

E_h = environment effect

G_i = genotypic effect

GE_hi = genotype-by-environment interaction effect

B_j(h) = block effect

e_hij = random error

Figure 3: Stability analysis using AMMI Bi-plot of 18 genotypes from international collections evaluated for LMAA over 5 environments

Figure 4: Stability analysis using AMMI Bi-plot of 15 South Dakota adapted cultivars evaluated for LMAA over 9 environments

 All estimated variance components and their proportion to the phenotypic variance were highly significant for South Dakota adapted cultivars and for international collections.

 Broad-sense heritability for LMAA (70%) was higher in regionally adapted cultivars compared to that (49%) in international collections.

 Significant genetic effects showed some genotypes, e.g. Lancer, Chester and LoSprout from international collections, and Alsen, Traverse and Forefront from SD adapted cultivars can be used as parents with low levels of LMAA.

 Stability analysis using AMMI bi-plot revealed that Chester and LoSprout from international collections, and Traverse from SD adapted cultivars showing negative PC1 and PC2, therefore, can be used as parents to reduce LMAA in order to develop PHS resistant cultivars.

ACKNOWLEDGEMENTS

Edgar S. McFadden

The Fathe of

Wheat-‘ye Hyb ids

REFERENCES

a _{evaluated over 5 environments;} b _{evaluated over 9 environments; **** significant at p < 0.0001.}

¶_V

G, VG x E, Ve, and VP are genotypic-, genotype x environment-, error-, and phenotypic variance

components, respectively.

Variance in red was the broad-sense heritability estimation. _{Since genotypes with low LMAA were identified, and their genetic}

effects and G x E effects were predicted, application of GGE model using ‘ packages gtools_1.0 (Wu et. al. 2013) with a mixed linear model approach (MINQUE) were justified to analyze our datasets for genetic evaluation of LMAA in bread wheat.

Plant materials:

18 spring wheat genotypes from Canada, USA, Australia, UK and CIMMYT, Mexico

15 spring wheat cultivars adapted to South Dakota

Design and Statistical Analysis:

RCBD with 3 reps; GGE model with mixed linear model approach using R software package gtools_1.0 (Wu et al., 2013), and AMMI bi-plot for stability analysis on R platform

Environments:

International collection: Aurora 2011 (AUR11), Brookings

2012 (BRK12), Volga 2012 (VOL12), Aurora 2013 (AUR13), and Brookings 2013 (BRK13)

South Dakota cultivars: Aurora 2011 (AUR11), Brookings

2011 (BRK11), Selby 2011 (SEL11), Groton 2011 (GRO11), Aurora 2012 (AUR12), Brookings 2012 (BRK12), Selby 2012 (SEL12), Groton 2012 (GRO12), and Watertown 2012 (WAT12)

Variance¶ _{Estimate SE. Pvalue Sign.}

International collections a

V_G 0.00257 0.000222 0.000000 **** V_GxE 0.00201 0.000352 0.000098 **** V_e 0.00069 0.000066 0.000000 **** V_P 0.00527 0.000626 0.000002 **** South Dakota cultivars b

V_G 0.00043 0.000010 0.000000 **** V_GxE 0.00005 0.000010 0.000022 **** V_e 0.00013 0.000010 0.000000 **** V_P 0.00061 0.000010 0.000000 ****

a _{evaluated over 5 environments;} b _{evaluated over 9 environments; ****: significant at p < 0.0001.} ¶_V

G, VG x E, Ve, and VP are genotypic-, genotype x environment-, error-, and phenotypic variance

components, respectively.

Variance¶ _{Estimate SE. Pvalue Sign.}

International collections a

V_G/V_P 0.49 0.028 0.000000 ****

V_GxE/V_P 0.38 0.035 0.000002 **** V_e/V_P 0.13 0.008 0.000000 **** South Dakota cultivars b

V_G/V_P 0.70 0.008 0.000000 ****

V_GxE/V_P 0.08 0.011 0.000001 **** V_e/V_P 0.22 0.010 0.000000 ****

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