Breeding for Grain Amino Acid Composition in Maize

Darrigues, Audrey; Lamkey, Kendall R.; Scott, M. Paul

doi:10.1002/9780470752708.ch24

Cited by 5 publications

(8 citation statements)

References 31 publications

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Section: Crop Sciencementioning

confidence: 99%

“…Heritability is typically quite high for NIRS-based estimates of protein content (e.g., 0.83 in inbred lines; Cook et al, 2012). In contrast, accurate measurement of amino acid composition of maize grain generally requires time-consuming and expensive laboratory analysis (Darrigues et al, 2008). It is important to note that amino acid concentrations are reported in two primary ways in the literature: for example, phenylalanine content as a proportion of grain weight (g phenylalanine kg −1 seed weight) and phenylalanine composition as a proportion of total protein (g phenylalanine kg −1 of seed protein).…”

Section: Core Ideasmentioning

confidence: 99%

“…Bioassays and NIRS calibrations have been developed to increase the measurement throughput for a few specific amino acids, in particular lysine, tryptophan, and methionine (Darrigues et al, 2005(Darrigues et al, , 2008Rosales et al, 2011). These amino acids have been of particular interest to maize breeders because of their importance to human and animal nutrition and their limited availability in most maize varieties (Atlin et al, 2011;Darrigues et al, 2008). As a consequence, we are aware of studies reporting heritabilities of amino acid contents only for a handful of amino acids, but not including phenylalanine, and these reports have focused exclusively on quality protein maize germplasm (Dudley et al, 1974;Mahan et al, 2014).…”

Section: Crop Sciencementioning

confidence: 99%

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The potential to breed a low‐protein maize for protein‐restricted diets

Woore¹,

Flint-García

Holland

2021

Crop Science

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Inborn errors of amino acid metabolism, such as phenylketonuria (PKU), are human genetic conditions that result in an inability to metabolize certain amino acids, requiring lifelong maintenance of a very restrictive low-protein diet. In the case of PKU, this diet does not permit consumption of most maize (Zea mays L.) products, yet maize is a major part of the human diet and is especially significant in some cultures. Kernel protein content, and to a lesser extent amino acid composition, is variable in maize. Breeding maize for low kernel protein content or low amounts of specific amino acids such as phenylalanine might expand the restrictive diets and improve quality of life for people with amino acid metabolic disorders. To test the potential of breeding low-protein-content maize, diverse samples of hybrid or open-pollinated cultivars and inbreds were evaluated across two locations in two years for grain protein content and amino acid composition. Consistent with previous reports, kernel protein content varied widely across maize varieties and is highly heritable in the broad sense. We found that phenylalanine content (as a proportion of grain weight) and phenylalanine composition (as a proportion of total protein) are also highly heritable in the broad sense. As previously reported, reasonably accurate estimation of protein content is possible with near-infrared spectrophotometry, whereas measuring phenylalanine content requires a destructive, costly, and time-consuming laboratory analysis. Because phenylalanine content was strongly correlated with total kernel protein, breeding primarily aimed at reducing grain protein content should permit efficient selection of maize cultivars sufficiently low in phenylalanine to permit broader inclusion in the PKU diet.

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Section: Crop Sciencementioning

confidence: 99%

Section: Core Ideasmentioning

confidence: 99%

Section: Crop Sciencementioning

confidence: 99%

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The potential to breed a low‐protein maize for protein‐restricted diets

Woore¹,

Flint-García

Holland

2021

Crop Science

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“…However, non-essential amino acids are amino acids that can be synthesized in the body; hence, they do not necessarily need to be part of the diet, but they are required for protein synthesis in the body. Examples include alanine, aspartic acid, and tyrosine [ 2 ]. Twenty amino acids are fundamental building blocks of proteins, out of which nine are essential (must be acquired from the diet).…”

Section: Introductionmentioning

confidence: 99%

“…Biofortification has been used in recent times not just to improve the micronutrients level in crops but to enhance the overall nutritional benefits of crops. QPM (a biofortified maize variety) improves the nutritional status of the population that depends on maize as a staple crop [ 4 ], because diets with imbalanced amino acid levels contribute to the malnutrition conditions of Kwashiorkor in humans, while tryptophan’s deficiency can produce eye cataracts [ 2 ]. Quality protein maize (QPM), a biofortified opaque-2 mutant maize variety, has special characteristic features such as low and uniform ear placement, resistance to ear rot, and root lodging.…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Reflectance Spectrophotometry (NIRS) Application in the Amino Acid Profiling of Quality Protein Maize (QPM)

Alamu

Menkir

Adesokan

et al. 2022

Foods

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The accurate quantification of amino acids in maize breeding programs is challenging due to the high cost of analysis using High-Performance Liquid Chromatography (HPLC) and other conventional methods. Using the Near-Infrared Spectroscopic (NIRS) method in breeding to screen many genotypes has proven to be a fast, cost-effective, and non-destructive method. Thus, this study aimed to develop and apply the NIRS prediction models for quantifying amino acids in biofortified quality protein maize (QPM). Sixty-three (63) QPM maize genotypes were used as the calibration set, and another twenty (20) genotypes were used as the validation set. The microwave hydrolysis system coupled with post-column derivatization with 6-amino-quinoline-succinimidyl-carbamate as the derivatization reagent and the HPLC method were used to generate the reference data set used for the calibration development. The calibration models were developed for essential and non-essential amino acids using WINSI Foss software. Good coefficients of determination in calibration (R2cal) of 0.91, 0.93, 0.93, and 0.91 and low standard errors in calibrations (SEC) of 0.62, 0.71, 0.26, and 1.75 were obtained for glutamic acids, alanine, proline, and leucine, respectively, while aspartic acids, serine, glycine, arginine, tyrosine, valines, and phenylalanine had fairly good R2Cal values of 0.86, 0.71, 0.81, 0.78, 0.68, 0.79, and 0.75. In contrast, poor (R2cal) was obtained for histidine (0.07), cystine (0.09), methionine (0.09), lysine (0.20), threonine (0.51), and isoleucine (0.09), respectively. The models’ prediction performances (R2pred) and standard error of prediction (SEP) were reasonably good for certain amino acids such as aspartic acid (0.90), glycine (0.80), arginine (0.94), alanine (0.90), proline (0.80), tyrosine (0.83), valine (0.82), leucine (0.90), and phenylalanine (0.88) with SEP values of 0.24, 0.39,0.24, 0.93, 0.47,0.34, 0.78, 2.20, and 0.77, respectively. However, certain amino acids had their R2pred below 0.50, which could be improved to become useful for screening purposes for those amino acids. Further work is recommended by including a training set representing the sample population’s variance to improve the model’s performance.

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In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor

Aykas

Ball

Sia

et al. 2020

Sensors

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This study evaluates a novel handheld sensor technology coupled with pattern recognition to provide real-time screening of several soybean traits for breeders and farmers, namely protein and fat quality. We developed predictive regression models that can quantify soybean quality traits based on near-infrared (NIR) spectra acquired by a handheld instrument. This system has been utilized to measure crude protein, essential amino acids (lysine, threonine, methionine, tryptophan, and cysteine) composition, total fat, the profile of major fatty acids, and moisture content in soybeans (n = 107), and soy products including soy isolates, soy concentrates, and soy supplement drink powders (n = 15). Reference quantification of crude protein content used the Dumas combustion method (AOAC 992.23), and individual amino acids were determined using traditional protein hydrolysis (AOAC 982.30). Fat and moisture content were determined by Soxhlet (AOAC 945.16) and Karl Fischer methods, respectively, and fatty acid composition via gas chromatography-fatty acid methyl esterification. Predictive models were built and validated using ground soybean and soy products. Robust partial least square regression (PLSR) models predicted all measured quality parameters with high integrity of fit (RPre ≥ 0.92), low root mean square error of prediction (0.02–3.07%), and high predictive performance (RPD range 2.4–8.8, RER range 7.5–29.2). Our study demonstrated that a handheld NIR sensor can supplant expensive laboratory testing that can take weeks to produce results and provide soybean breeders and growers with a rapid, accurate, and non-destructive tool that can be used in the field for real-time analysis of soybeans to facilitate faster decision-making.

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Breeding for Grain Amino Acid Composition in Maize

Cited by 5 publications

References 31 publications

The potential to breed a low‐protein maize for protein‐restricted diets

The potential to breed a low‐protein maize for protein‐restricted diets

Near-Infrared Reflectance Spectrophotometry (NIRS) Application in the Amino Acid Profiling of Quality Protein Maize (QPM)

In-Situ Screening of Soybean Quality with a Novel Handheld Near-Infrared Sensor

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