LINEAR REGRESSION MODEL TO PREDICT THE AGRONOMIC PERFORMANCE OF MAIZE PLANTS

Martín García-Flores1* & Axel Tiessen1

1CINVESTAV Unidad Irapuato. México *corresponding author: masterfoodscience@live.com

INTRODUCTION

Starch

REFERENCES.1.Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress. 2011. Witt, S. et al. 2.Drought tolerance in Maize. 2009. Ribaut, et al. 3.Breeding strategies to adapt crops to a changing climate. 2010. Trethowan, et al. 4. Effects of environmental factors on cereal starch biosynthesis and composition. 2012. Thitisaksakul et al. 5. Regression methods in biostatistics. 2012. Vittinghoff, et al.

MATERIALS AND METHODS

VIS-UV spectrophotometer

Spectrophotometer UV-VIS equipment

CONCLUSIONS

ACKNOWLEGMENTS We thank the “Consejo Nacional de Ciencia y Tecnología” (CONACYT) in México for funding and supporting this Project. We also thank MASAGRO and CINVESTAV for funding. We thank the maize meeting organizers for the scholarship given to MGF to attend the 55th Annual Maize Genetics Conference.

RESULTS

BACKGROUND

The characteristics of UV-VIS spectrophotometry allowed handling a large number of samples, which is a great advantage for breeding projects. Biochemical data is correlated to physiological state and yield. This can be used for genotypic selection. In order to adjust the predictors, we run Step Wiese, Best Subsets, Fitted line plots and General Regression tests using Minitab 16.0 2010, obtaining an R-square of 86.45% delivered by the last option.

Glucose Fructose

Flow diagram for processing samples of Maize grain extracts.

Protocols. Tiessen, 2010.

Figure 2. Schematic illustration of the cold response network in Arabidopsis. Cold sensing and signaling leads to the activation of multiple transcriptional cascades, one of which involves ICE1 and CBFs. The ubiquitin E3 ligase HOS1 negatively regulates ICE1. Metabolism, and RNA processing and export, affect cold tolerance via cold signaling and/or cold-responsive gene expression. The constitutive HOS9 and HOS10 regulons have a role in the negative regulation of CBF-target genes. MYBRS, MYB; MYCRS, MYC recognition sequences (Zhu, 2007).

Biological material: Left-right: W-Puma,

W-Leopardo, W-Oso

Y-2B150Y-2A120

Figure 1. Simplified model of the starch biosynthetic pathway in a cereal endosperm cell. The legend is as follows: INV-invertase; SuSy-Sucrose synthase; PGI Phosphoglucoisomerase; PGM-Phosphoglucomutase; UGPase-UDP-glucose pyrophosphorylase; SPS-Sucrose Phosphate Synthase; AGPase-S-Small subunit of ADP glucose pyrophosphorylase; AGPase-Large subunit of AGPase; ADPGT-ADPglucose transporter (Brittle-1or Bt); AATP-lulase; ISA-Isoamylase; PHO-Starch phosphorylase (Thitisaksakul, et al. 2012).

Starch biosynthetic pathway

Figure2. An example of a conventional breeding scheme using either a modified bulk or selected bulk strategy. The time from cross to homozygous line identification is 4-7 years and a further 4-5 years of yield and quality evaluation and seed multiplication are required before the selected genotype is released to farmers (Trethowan, 2010).

Figure 1 Structure of maize kernel

(www.fao.org)

Multiple strategies are being employed in breeding world wide in an attempt to improve the nutritional value of new germplasm that can tolerate extremes of environment. The correlation between parental inbreds and hybrids, to predict hybrid performance from that of its inbred parents, depends on the trait and the environment. In general, the correlation is relatively high for some traits (e.g., plant morphology, ear traits, maturity) but is relatively low for grain yield which is consistently positive and significant but not high enough to predict hybrid performance (Jean-Marcel Ribaut, Javier Betran , Philippe Monneveux, and Tim Setter, 2009). An ideal secondary trait would be genetically correlated with grain yield in the target environment, genetically variable, have a high level of heritability, be simple, cheap, non-destructive and fast to assay (Edmeades et al., 1997a ; Lafitte et al., 2003).

Sucrose

Analysis of Regression: W_200grains vs. Predictors Linear Regression Equation

W_200grains = 72,4 + 3,19 W_tassel - 1,00 H_tassel - 0,100 H_plant - 0,95 L_plant - 5,43 W_corncob + 6,00 W_grains + 5,95 W_cob - 1,76 Glucose + 1,72 Fructose - 0,261 Sucrose + 0,143 Starch

S = 13,6875 R-sq. = 79,6% R-sq. .(adjusted) = 74,0%

Analysis of VarianceSource GL SC CM F PRegression 11 29269,3 2660,8 14,2 0,000Residual error 40 7493,9 187,3Total 51 36763,2

Lack of fitness testProbably curvature of W_CornCob variable (Value P = 0,017); Probably curvature of W_Grains variable(Valor P = 0,009); The lack of fitness general test is significative at P = 0,009

Unidirectional ANOVA: W_200grains vs. Genotype Source GL SC CM F PGenotype 4 3934 984 1,41 0,246Error 47 32829 698Total 51 36763

Tukey test Genotype N Average GroupsOso 10 56,30 APuma 12 47,00 ADow 2A 10 45,30 ADow 2B 10 35,40 ALeopardo 10 31,40 A

Minitab 16 1.0 2010

EXPERIMENTAL STRATEGY

Sugar and starch ethanolic extraction of maize grain samples, 20 mg.

Experimental field trial.

Maize Grain sampling from experimental fields.

Physiological data recording.

Incubate in thermomixer at 80°C and centrifuge for 5 min, 6000 rpm, 0°C.

Use supernatant for sugar assay, repeat procedure.

Denaturation and hydrolysis of starch

Statistical Analysis

Reading of sugar and starch for enzymatic assays (UV-VIS)

Glucose in maize grain

0369

1215182124

Puma Oso Leopardo 2A120 2B150

Sample

mm

ol/L

Fructose in maize grain

0369

121518212427

Puma Oso Leopardo 2A120 2B150

Sample

mm

ol/L

Sucrose in maize grain

0

20

40

60

80

100

120

140

Puma Oso Leopardo 2A120 2B150

Sample

mm

ol/L

Starch in maize grain

0

10

20

30

40

50

60

70

80

90

100

110

Pum

a

Oso

Leop

ardo

2A12

0

2B15

0

Sample

mm

ol G

lc /

L

Carotenoids

Carotenoids

8.4

8.6

8.8

9.0

9.2

9.4

9.6

9.8

Puma Oso Leopardo 2A120 2B150

Hybrids

Co

ncen

trati

on

UA

/gr.

Dry

weig

ht

Carotenoids

PumaOsoLeopardoDow 2BDow 2A

80

70

60

50

40

30

20

10

0

Genotype

W_200 g

rain

s

Box Plot Individual values: W_200 grains vs. Genotype

160140120100806040200

100

80

60

40

20

0

-20

-40

W_corncob

W_200gra

ins

S 13,1319R-sq. 77,0%R-sq.(adjusted) 76,1%

RegressionIC of 95%IP of 95%

Adjusted polynomial lineW_200grains = 2,663 + 0,9624 W_corncob

- 0,003611 W_corncob**2

140120100806040200

100

80

60

40

20

0

-20

W_grains

W_200gra

ins

S 13,5702R-sq. 75,5%R-sq.(adjusted) 74,5%

RegressionIC of 95%IP of 95%

Adjusted polynomial lineW_200grains = 8,288 + 1,111 W_grains

- 0,005305 W_grains**2

W_200grains = 54,3911 + 4,94332 W_tassel - 0,030091 H_plant - 1,51615 L_plant - 1,20884 H_tassel - 6,32619 W_corncob + 7,50039 W_grains +6,87808 W_cob - 1,1331 Glucose + 1,11734Fructose - 0,069625Sucrose - 0,00276978 Starch - 0,00113775 W_corncob*W_corncob -0,0042445 W_grains*W_grains

S = 11,4488 R-sq. = 86,45% R-sq.(adjusted) = 81,82%