George Acquaah - Principles of Plant Genetics and Breeding

Breeding value(or genetic merit) of an individual as a genetic parent is the sum of gene effects of the individual as measured by the performance of its progeny. Statistically, it is measured as twice the deviation of the offspring from the population mean (since the individual only contributes half of the alleles to its offspring). This estimate measures the ability of an individual to produce superior offspring. This is the part of an individual's genotypic value that is due to independent gene effects and hence can be transmitted. The mean breeding value becomes zero with random mating. This estimate is of importance to breeders because it assists them in selecting the best parents to use in their programs.

The Best Linear Unbiased Prediction (BLUP )is a common statistical method for estimating breeding values. It is unbiased because as more data are accumulated, the predicted breeding values approach the true values. BLUP is a method of estimating random effects. The context of this statistical method is the linear model

where y = is a vector of n observable random variables; B is vector of p unknown parameters with fixed value or effects; X and Z are known matrices; u and e are vectors of q and n , respectively, unobservable random variables (random effects).

To apply this technique, numerical scores are assigned to traits and compiled as predictions of the future. Simple traits can be most accurately and objectively measured and possibly predicted. Only one trait may be predicted in a model. This trait has to be objectively measurable with high accuracy. Further, it has to be heritable.

Genomic selection and its application in plant breeding is the subject of Chapter 25. Selection in conventional plant breeding generally relies on breeding values estimated from pedigree‐based mixed models that cannot account for Mendelian segregation, and in the absence of inbreeding, can only explain one half of the genetic variability (individual contributes only half of its alleles to the next generation as previously stated). Molecular markers have the capacity to track mendelian segregation as several positions of the genome of the organism, thereby increasing the accuracy of estimates of genetic values (and the genetic progress achievable when the predictions are used for selection in breeding). Even though marker‐assisted selection (MAS) (see Chapter 24) has achieved some success, its application to improving quantitative traits is hampered by various factors. The biparental mating designs used for detection of loci affecting quantitative traits and statistical methods used are not well‐suited to traits that are under polygenic control (MAS uses molecular markers in linkage disequilibrium with QTL).

Genomic selection (or genome‐wide selection) is proposed as a more effective approach to improving quantitative traits. It uses all the available molecular markers across the entire genome (there are thousands of genome‐wide molecular markers) to estimate genetic or breeding values. Using high‐density marker scores in the prediction model and high throughput genotyping, genomic selection avoids biased marker effect estimates and captures more of the variation due to the small‐effect QTL. Genomic selection has advantages. It can accelerate the selection cycles and increase the selection gains per unit time.

The subject of mapping is treated in detail in Chapter 22. Quantitative traits pose peculiar challenges to plant breeders compared to qualitative traits. They are difficult to map and breed. Over the years, researchers have developed new methodologies to address these challenges, thereby enabling breeders to achieve genetic gain more rapidly in their endeavors.

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