Biometrical Techniques In Plant Breeding By Phundan Singh
Elia Khensamphanh
PSO1: Impart knowledge and understanding of fundamental concepts and techniques of maintaining and enhancing soil fertility, crop production, crop management, crop improvement, biodiversity, and sustainability of agriculture worldwide.
Introduction and historical background of quantitative genetics; Multiple factor hypothesis, Qualitative and quantitative characters; Analysis of continuous variation mean, range, SD, CV; Components of variation- Phenotypic, Genotypic; Nature of gene action- additive, dominance and epistatic, linkage effect; Principles of analysis of variance and linear model, Expected variance components, Random and fixed effect model, Comparison of means and variances for significance; Designs for plant breeding experiments- principles and applications; Variability parameters, the concept of selection; simultaneous selection modes and selection of parents, MANOVA; Association analysis- Genotypic and phenotypic correlation; Path analysis; Discriminate function and principal component analysis, Genetic divergence analysis- Metroglyph and D2; Generation mean analysis; Parent progeny regression analysis; Mating designs- classification, Diallel, partial diallel; L T; NCDs; and TTC; Concept of combining ability and gene action; G E interaction-Adaptability and stability; Methods and models for stability analysis; Basic models- principles and interpretation, Bi-plot analysis; QTL mapping, Strategies for QTL mapping- Desired population and statistical methods, QTL mapping in genetic analysis; Markers, Marker-assisted selection and factors influencing the MAS; Simultaneous selection based on marker and phenotype.
biometrical techniques in plant breeding by phundan singh
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Analysis and interpretation of variability parameters; Analysis and interpretation of Index score and Metroglyph; Clustering and interpretation of D2 analysis; Genotypic and phenotypic correlation analysis and interpretation; Path coefficient analysis and interpretation; Estimation of different types of heterosis, inbreeding depression and interpretation; A, B, and C Scaling test; L T analysis and interpretation; QTL analysis; Use of computer packages; Diallel analysis; G E interaction and stability analysis.
In any hybridization program, recognition of the best combination of two (or more) parental genotypes to maximize variance within related breeding populations, and as a result the chance of recognizing superior transgressive segregants in the segregating populations, are the most critical challenge to plant breeders. Since the combining ability was introduced in 1942, it has been widely adopted in plant breeding to compare performances of lines in hybrid combinations. In addition, the ability to predict optimal genotype combinations for different traits based on molecular-based genetic data would greatly enhance the efficiency of plant breeding programmes. This article reviews our current understanding of combining ability in plant breeding as well as recent advances in research in this field. It brings an introduction to combining ability and the concept of general and specific combining ability, methods for estimating combining ability, and QTL mapping of related traits.
Identification of the best performing lines (for commercial release) and lines which can be used as parents in future crosses are two principal objects considering in most crop breeding programs.1 The best performing lines for required characteristics are selected based on conducting multi-environment trials following statistical analysis. A well-designed trial accompanied by statistical analysis distinguishes genetic and environmental influences. The parental lines selection can be performed by particular mating designs such as line tester, North Carolina (NC) designs I, II and III, and diallel. Through conducting such designs, the genetic influences of a line can be partitioned into additive and non-additive components.1,2
Crossing a line to several others provides the mean performance of the line in all its crosses. Combining ability or productivity in crosses is defined as the cultivars or parents ability to combine among each other during hybridization process such that desirable genes or characters are transmitted to their progenies. In another definition, combining ability is an estimation of the value of genotypes on the basis of their offspring performance in some definite mating design.3 It can seldom be envisaged only based on parental phenotype and thus it is measured by progeny testing. When parental plants produce potent offspring, they are said to have good combining ability.4
At first, combining ability was a general concept used collectively for classifying an inbred line respective to its cross performance but was later amended. Two concepts of general combining ability (GCA) and specific combining ability (SCA) have had important influence on inbred line evaluation and population development in crop breeding.5 Sprague and Tatum5 defined GCA as the average performance of a genotype in a series of hybrid combinations. They defined SCA as those cases in which certain hybrid combinations perform better or poorer than would be expected on the basis of the average performance of the parental inbred lines. Parents showing a high average combining ability in crosses are considered to have good GCA while if their potential to combine well is bounded to a particular cross, they are considered to have good SCA.
From a statistical point of view, the GCA is a main effect and the SCA is an interaction effect.6 Based on Sprague and Tatum,5 GCA is owing to the activity of genes which are largely additive in their effects as well as additive additive interactions.7 Specific combining ability is regarded as an indication of loci with dominance variance (non-additive effects) and all the three types of epistatic interaction components if epistasis were present. They include additive dominance and dominance dominance interactions.
It is obvious from the foregoing definitions that the combining ability of lines for main characteristics is estimated by examining a set of designed progeny in good trial design accompanied by statistical analysis. Furthermore, parent selection for combining ability is conducted through growing and evaluating the progenies.8
Combining ability studies have been conducted in many crops ranging from cereals, roots to legumes, indicating that it is a crucial tool in plant breeding. As shown in Table 1, GCA effects for parents and SCA effects for crosses were estimated in different crops, such as wheat,1,10 sunflower,11 rice,12 sorghum,13 maize,8,14 cotton,15 and chickpea.16 Interesting combining ability analyses were recently performed in watermelon17 and oil palm.18
Importance of combining ability in applied genetics including plant and animal breeding cannot be overemphasized. The GCA concept has been effectively used in crop and livestock breeding for more than 70 years.5,53-55 GCA is an effective tool used in selection of parents based on performance of their progenies, usually the F1 but it has also been used in F2 and later generations (Fn). A low GCA value, positive or negative, shows that the mean of a parent in crossing with the other does not vary largely from the general mean of the crosses. In contrast, a high GCA value shows that the parental mean is superior or inferior to the general mean. This indicates a potent evidence of desirable gene flow from parents to offspring at high intensity and represents information regarding the concentration of predominantly additive genes.56 A high GCA estimate indicates higher heritability and less environmental effects. It may also result in less gene interactions and higher achievement in selection.2,30 One of the main features of the elite parent with high GCA effect is its large adaptability. A parent good in per se performance may not necessarily produce better hybrids when used in hybridization.3,38,57 Concurrently, it also indicated that one parent of the worst combination could make the best combination if the other parent was selected properly.9
In GCA determination, SCA usually acts as a masking effect. By using genetically broad testers or increasing number of testers, SCA impact can be decreased.58 Parental choice only on the basis of SCA effect has limited value in breeding programs. Therefore, SCA effect should be used in combination with a high performance per se hybrid, favourable SCA estimates, and involving at least one parent with high GCA13,41,44,56
Observations of performance of different cross patterns on the basis of SCA have been used to make inferences on gene action at play. High SCA effects resulting from crosses where both parents are good general combiners (i.e., good GCA good GCA) may be ascribed to additive additive gene action.29,40 The high SCA effects derived from crosses including good poor general combiner parents29,34,40 may be attributed to favourable additive effects of the good general combiner parent and epistatic effects of poor general combiner, which fulfils the favourable plant attribute. High SCA effects manifested by low low crosses29,30 may be due to dominance dominance type of non-allelic gene interaction producing over dominance thus being non-fixable.59 Predominance of non-additive effects has been reported for inheritance of pod yield and related traits in groundnut under salinity stress in which there were cross combinations with high SCA effects arising from parents with high and low GCA, and another set of crosses with high SCA effects arising from both parents with good GCA effects.60
Different methods have been used to evaluate relative importance of GCA and SCA in plant breeding. The first step is to check whether or not both GCA and SCA are significant at P=0.05 or at higher probability levels (0.01 or 0.001 etc.). If both the GCAand SCA values are not significant, epistatic gene effects may play a remarkable role in determining these characters.61
The ratio of combining ability variance components (predictability ratio) determines the type of gene action involved in the expression of traits and allows inferences about optimum allocation of resources in hybrid breeding:
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