Crop Biotechnology - American Chemical Society

Chapter 17

Prediction of Parental Genetic Compatibility to Enhance Flavor Attributes of Peanuts 1

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H. E. Pattee , T. G. Isleib , F. G. Giesbrecht , and Z. Cui

Downloaded by UNIV OF ARIZONA on May 14, 2013 | http://pubs.acs.org Publication Date: August 7, 2002 | doi: 10.1021/bk-2002-0829.ch017

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Market Quality and Handling Research Unit, Agricultural Research Service, U.S. Department of Agriculture, Campus Box 7625, North Carolina State University, Raleigh, NC 27695 Department of Crop Science, Campus Box 7620, North Carolina State University, Raleigh, NC 27695 Department of Statistics, Campus Box 8203, North Carolina State University, Raleigh, NC 27695

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As future advances in transformation technology allow insertion of useful genes into a broader array of target genotypes, the choice of targets will become more important. Targets should be genotypes that will pass to their progeny other useful characteristics, such as sensory quality characteristics, while improving agronomic performance or pest resistance. This is particularly important if flavor quality is to be maintained or improved as the transgene is moved into breeding populations via sexual transfer. Selection of genotypes with superior breeding values through the use of Best Linear Unbiased Prediction procedures (BLUPs) is discussed and using a database of sensory attributes on 250 peanut cultivars and breeding lines, the application of BLUP procedures to the selection of parents for improvement of roasted peanut and sweet attributes in breeding of peanut cultivars is illustrated.

© 2002 American Chemical Society

In Crop Biotechnology; Rajasekaran, K., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2002.

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Currently, transformation can be used to insert useful genes into specific regenerable genotypes of many crop species. The transgenes are then moved into commercial cultivars by backcrossing. For example, in peanut (Arachis hypogaea L.), transformation mediated by Agrobacterium tumefasciens has been reproducible only in the obsolete cultivar New Mexico Valencia A (1). Transformation via microprojectile bombardment of somatic embryos is less genotype-specific, but the efficiency of regeneration of plants is highly dependent on genotype (2). Future advances in transformation technology will permit insertion of useful genes into a broader array of target genotypes. With these advances, selection of target genotypes with superior quality traits and superior capacity to transmit those qualities to new cultivars will be more critical. Estimation of this capacity, termed "breeding value" in animal improvement and "combining ability" in plant improvement, is not a new concept. However, traditional methods of estimating breeding value require complex mating designs and extensive progeny testing. Therefore, selection of parents in conventional plant breeding is usually based on the individual's phenotype rather than on its breeding value. This short-cut method of parent selection can produce some inferior breeding populations. Best Linear Unbiased Prediction (BLUP) is a procedure described by Henderson (3) to estimate the breeding values of dairy cattle based on data collected on all types of relatives rather than on progeny alone, obviating the need for complex mating designs and extensive progeny testing. Data on progeny of specific animals can be included in the analysis but are not required. The method is based on a mixed linear model with known variance-covariance structure among fixed and random effects. In general, the genetic effects in the model are considered to be random while the environmental effects are considered to be fixed. The variance-covariance matrix of additive genetic effects is calculated using standard quantitative genetic theory and is based upon the matrix of coancestries among related lines (4). BLUP is widely used in animal breeding and tree improvement (5) and is beginning to be used in annual crop species. Bernardo (6, 7, 8, 9) found it useful for identifying superior single crosses in maize (Zea mays L.) prior to field testing. Panter and Allen (70,11) found BLUP to be superior to midparent value in selecting cross combinations in soybean (Glycine max L.). Enhancement of roasted flavor of peanuts has been a long-standing objective of the peanut industry. Roasted peanut flavor has several attributes: roasted peanut, sweet, bitter, astringent,fruity,etc. and is the primary trait that induces consumers to buy peanuts. Highly significant correlations have been found among means for the attributes, particularly among roasted peanut, sweet and bitter (12, 13). The chemical basis of roasted peanut flavor is not well known, but is thought to be pyrizines derived from sugars and amino acids under heating. The specific genes or gene products involved in flavor precursor



219 control are unknown. Through the research of Pattee and coworkers certain roasted peanut quality sensory attributes have been shown to be heritable (13, 14, 15, 16, 17). They have also shown that the choice of parents to create a new variety can influence flavor quality. There are four market-types of peanuts, each with a different primary usage. The runner market-type is used to make peanut butter. Large-seeded Virginia market-type are sold in-shell at ball parks, in grocery stores, and as boiled peanuts. They are also sold shelled as cocktail peanuts. Spanish market-type are used in confectionery products and mixed-nut products. Valencia market-type are sold in-shell in grocery stores. These market-types are genetically diverse in parentage and these differences can be important in selecting for breeding value. The runner and Virginia market-types have an alternate branching pattern typical of subspecies hypogaea and pod characteristics typical of botanical variety hypogaea. Their genetic base is predominantly the hypogaea botanical variety, but current cultivars and breeding lines have at least some ancestryfromsubspecies fastigata. The Spanish and Valencia market-types are entirely from the subspecies fastigata Waldron, the Spanish lines from botanical variety vulgaris Harz and the Valencia lines from botanical variety fastigata. Because the Virginia and runner market-types come from a distinctly different genetic background than the fastigate types, it is conceivable and perhaps likely that these differences can be important in sensory attribute relationships. Our objectives are to (a) introduce Best Linear Unbiased Prediction procedures (BLUPs), which can help select the genotypes with superior breeding values, and (b) present the concept that parent selection becomes more critical as the capacity to insert transgenes into target genotypes improves because of the wider availability of genotypes.

Materials and Methods Genotype Resources. The data used for this study were gathered over an 11-year span and include four peanut market-types, 250 different genotypes and 53 environments (year-by-location combinations). In the data set there are 1822 observations on roasted peanut attribute, 1779 on sweet and bitter attributes, and 1460 on the astringent attribute. All samples were obtained from plants grown and harvested under standard recommended procedures for the specific location. The market-types Spanish and Valencia have been combined in the data set because of an insufficient number of Valencia entries to properly represent the group.



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Sample Handling. Across years samples were shipped to Raleigh, NC in February following harvest and placed in controlled storage at 5 °C and 60% RH until processed. Sample Roasting and Preparation. The peanut samples were roasted between May and June using a Blue M "Power-O-Matic 60" laboratory oven, ground into a paste, and stored in glass jars at -10 °C until evaluated. The roasting, grinding, and color measurement protocols were as described by Pattee and Giesbrecht (18). Sensory Evaluation. A long-standing six to eight-member highly-trained roasted peanut profile panel at the Food Science Department, North Carolina State University, Raleigh, NC, evaluated all peanut-paste samples using a 14-point intensity scale. Panel orientation and reference control were as described by Pattee and Giesbrecht (18) and Pattee et al. (14). Two sessions were conducted each week on nonconsecutive days. Statistical Analysis. PROC MIXED in SAS (19) was used for analysis of the unbalanced data set to estimate the sensory attribute least square means for genotypes. Covariates fruity and roast color were used, as needed, based upon the findings of Pattee et al. (12, 20, 21). The fixed effects were genotype, region, genotype-by-region, and covariates fruity and roast color. Each genotype effect was partitioned to reflect the effects of market type and genotype within market-types. Classification of lines into market-types was based upon branching pattern, pod type, and seed size. PROC IML in SAS was used to perform the calculations to compute BLUP estimates given in Harville (22). The mixed model (Formula 1) includes a parameter for the population mean (|i,), a set of fixed effects (p) with a corresponding incidence matrix (X) that assoviates specific effects with individual observations, a set of random additive genetic effects (a) with its incidence matrix (Z), and a vector of error terms (e): Y = | i + Xp + Za + e

(1)

The variance-covariance matrix for the random effects and error terms is

(2) where a is the error variance and A = Var([a]) = G 0 is the additive genetic variance-covariance matrix for the lines. G is therefore 2Ch /(l-h ) where C is the coancestry matrix and h is the narrow-sense heritability of the trait. Pedigree information on the lines was obtained from published records and from the individual breeders. Coancestries among lines were calculated using 2

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standard computational techniques incorporated into the computer program of Delannay et al. (23). Modifications described by Cockerham (24) were required to calculate coancestries among lines derived from the same cross. Lines tracing to different F plants had the same coancestry as full sibs, while pairs tracing to the same F or later generation selection were more closely related than full sibs. When no information was available on the commonality of two lines derived from the same cross, it was assumed that the lines traced to different F selections. The standard BLUP solutions (Formula 3) can be obtained only when the genetic variance-covariance matrix is nonsingular. 2

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J

"X'R X

a

Z'R X

-l

X'R Z

_1

_ 1

Z'R Z + G

_1

- 1

"X'R Y" _1

Z'R Y_

- 1

(3)

_1

Because of the inclusion of multiline cultivars and their component pure lines in the study, there were collinearities in the coancestry matrix, the G matrix was singular, thus the variance-covariance matrix cannot be estimated for BLUPs calculated in this way (21). The BLUP solutions for a singular G matrix were obtained using Formula 4.

"p

X'R-'X

X'R Z

V

Z'R X

Z'R ZG+I

_ 1

_ 1

_1

-l Z'R Y _ 1

PROC CORR, PROC GLM and PROC GPLOT in SAS (19) were used to perform other statistical analyses in this chapter.

Results and Discussion To better understand the impact of genetic variability on the sensory aspects of crop quality characteristics of a species it is essential to also understand the various environmental sources of variability. As previously stated the three primary sensory attributes that are heritable are roasted peanut, sweet, and bitter. Some aspects of the variation in these flavor components have been investigated, such as the effects of roast color and the attribute fruity (20, 21), genotype-by-environment (GxE) interaction on roasted peanut, sweet, and bitter (i2, 75), ancestral effects on roasted peanut attribute (17), and high oleic acid content (25). These results have not previously been brought together in single review.


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Sources of Variation Within Heritable Roasted Peanut Sensory Data. Environmental factors are the predominate source of variability in roasted peanut and bitter attributes while genotype is the single most important factor in the sweet attribute (Figure 1). Of the environmental effects, year stands out as a source of variation. Differences between years generally reflect differences in temperature and rainfall, although the three peanut-producing regions of the US (Virginia-Carolina, Georgia-Florida-Alabama, and Texas-Oklahoma) are separated by sufficient distance that one would not expect consistent climatic effects across all three. However, year-by-region interaction was small for all three attributes. Genotype-by-environment interaction (GxE) effects were small in comparison with genotypic variation for the sweet and bitter attributes, but relatively large for roasted peanut, especially the interaction of genotypes with specific locations within years and production regions. Each attribute has a substantial amount of error variation, i.e., variation that was not attributable to any of the factors included in the statistical model, suggesting that additional factors influencing flavor could be identified in the future.

Figure 1. Magnitudes of variance components reflecting predominant sources of variation in flavor attributes of roasted peanuts.


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The range of genotypic variation is different for the different market-types of peanut (Figure 2). The runner market-type has the greatest mean and the greatest maximum value for the roasted peanut attribute, followed by the fastigiate market-types and then by the Virginia market-type. However, the distributions of the three groups overlap. There is room to improve the roasted peanut scores of Virginia and fastigiate market-types, but there is also a risk of releasing runner cultivars with roasted peanut intensity inferior to Florunner, the long-time industry standard.

Figure 2. Means for roasted peanut attribute intensity across 122 peanut cultivars and breeding lines.

Substitution of Broad-sense for Narrow-sense Heritability in the G Matrix. Because only broad-sense heritability (H) estimates are available for the sensory attributes (13, 14, 16, 18), BLUPs were computed for each sensory attribute using a range of estimates of narrow-sense heritability (h ) (Table I, II, III). The estimates of h bracketed the published estimates of H (0.06 to 0.11 for roasted peanut, 0.26 to 0.37 for sweet, and 0.02 to 0.06 for bitter). Because it reflects only the fraction of phenotypic variance caused by additive genetic effects, narrow-sense heritability must be less than or equal to broad-sense heritability which reflects all genetic variation. Correlations among BLUPs obtained using the heritability values were examined as indicators of the sensitivity of the technique to variation in the heritability estimate. In all cases, the correlations and rank correlations among the BLUPs were very high, 2

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Table I. Correlations among BLUPs of breeding value for the roasted peanut attribute estimated at selected heritabilities. Heritability Rank correlation Correlation estimate h =0.10 h =0.15 h =0.10 h =0.15 h =0.05 0.9720 0.9454 0.9879 0.9690 h =0.10 0.9933 0.9954 2

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Table II. Correlations among BLUPs of breeding value for the sweet attribute estimated at selected heritabilities. Rank correlation Heritability Correlation h =0.20 h =0.25 estimate h =0.20 h =0.25 2

h=0.15 h=0.20

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0.9972

0.9911 0.9983

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0.9980 0.9980

Table III. Correlations among BLUPs of breeding value for the bitter attribute at selected heritabilities. Rank correlation Heritability Correlation estimate h =0.10 h =0.10 h =0.05 0.9908 0.9918 2

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indicating that the method is relatively insensitive to imprecision in the heritability estimate used in the calculations.

BLUPs of Breeding Value for Roasted Peanut and Sweet Attributes Using a database of sensory attributes on 250 peanut cultivars and breeding lines, BLUP procedures were used to predict breeding values of parents for the roasted peanut and sweet attributes of peanut flavor (Figure 3). The range of predicted breeding values for roasted peanut attribute was -0.51 to +0.45 flavor intensity units (fiu), approximately twice the range of flavor intensity needed to establish a statistically significant difference. The range for sweet attribute was -0.65 to +0.68 fiu. The range for bitter was -0.41 to +0.40fiu(data not shown). These values indicate that there is genetic potential to improve flavor quality through breeding. In collecting the sensory data, panelists assigned whole number scores to each sample, so these ranges are sufficiently large to be detectable by the human palate. The correlation observed between BLUPs for roasted peanut and sweet (r=0.71, P

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