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LINEAR REGRESSION MODEL TO PREDICT THE AGRONOMIC PERFORMANCE OF MAIZE PLANTS Martín García-Flores 1* & Axel Tiessen 1 1 CINVESTAV Unidad Irapuato. México.

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Presentation on theme: "LINEAR REGRESSION MODEL TO PREDICT THE AGRONOMIC PERFORMANCE OF MAIZE PLANTS Martín García-Flores 1* & Axel Tiessen 1 1 CINVESTAV Unidad Irapuato. México."— Presentation transcript:

1 LINEAR REGRESSION MODEL TO PREDICT THE AGRONOMIC PERFORMANCE OF MAIZE PLANTS Martín García-Flores 1* & Axel Tiessen 1 1 CINVESTAV Unidad Irapuato. México * corresponding author: INTRODUCTION Starch REFERENCES. 1.Metabolic and phenotypic responses of greenhouse-grown maize hybrids to experimentally controlled drought stress Witt, S. et al. 2.Drought tolerance in Maize Ribaut, et al. 3.Breeding strategies to adapt crops to a changing climate Trethowan, et al. 4. Effects of environmental factors on cereal starch biosynthesis and composition Thitisaksakul et al. 5. Regression methods in biostatistics 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 , obtaining an R-square of 86.45% delivered by the last option. GlucoseFructose Flow diagram for processing samples of Maize grain extracts. Protocols. Tiessen, 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)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 Variance Source GL SC CM F P Regression ,3 2660,8 14,2 0,000 Residual error ,9 187,3 Total ,2 Lack of fitness test Probably 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 P Genotype ,41 0,246 Error Total Tukey test Genotype N Average Groups Oso 10 56,30 A Puma 12 47,00 A Dow 2A 10 45,30 A Dow 2B 10 35,40 A Leopardo 10 31,40 A Minitab 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) Carotenoids W_200grains = 54, ,94332 W_tassel - 0, 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, Starch - 0, W_corncob*W_corncob -0, W_grains*W_grains S = 11,4488 R-sq. = 86,45% R-sq.(adjusted) = 81,82%


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