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Pearson later analyzed Galton’s data as well as subsequent data (Pearson and Lee 1903), and found that the correlation of the height of fathers and mothers with the height of their sons and daughters had a mean of 0.51. The height of their offspring would vary but, on average, was expected to exceed the population mean by 2/3 × 9 = 6 cm. To illustrate, suppose the mean height of a father and mother exceeded the mean of the population by 9 cm.
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For height, Galton estimated that the regression of offspring on mid-parent values was 2/3. Galton observed a regression toward mediocrity (now known as regression toward the mean) in that tall parents tended to have offspring shorter than them, and short parents tended to have offspring taller than them. The field of biometry began with Francis Galton’s work on how human characteristics, particularly height, were passed from parent to offspring (Galton 1869, 1889). Weldon, and the Mendelists led by William Bateson (Provine 1971). Quantitative genetics originated more than a century ago in the aftermath of a heated dispute between two groups of eminent English scientists: the biometricians led by Karl Pearson and W.F.R. However, quantitative genetics was developed not to provide a basis for artificial selection in different species, but to model how variation for continuous traits arises from unknown genes. After all, most economically important traits in crops and livestock species are quantitative rather than qualitative. Plant breeders may sometimes think that quantitative genetics was developed for the purpose of enhancing plant and animal improvement. The ideas proposed herein apply mainly to this context and they might not apply to other fields, such as human genetics and animal improvement, where quantitative genetics has also played an important role. The purpose of this Opinion article is to provide a framework for reflection, discussion, and constructive debate of reasons and ways to reinvent quantitative genetics within the context of current plant breeding. That being said, developments in both plant breeding and quantitative genetics in the last several decades should cause us to pause and ponder how quantitative genetics can best be applied in contemporary plant breeding. Mapping studies have identified major quantitative trait loci (QTL) that have been found useful in crop improvement, and that have helped elucidate the nature of quantitative variation (Kearsey and Farquhar 1998 Bernardo 2020). Quantitative genetics principles have led to the design and refinement of breeding methods for continuous traits (Dudley and Moll 1969). Estimates of genetic variances and heritability in many plant species have increased our understanding of the inheritance and variation of important traits related to yield, quality, and adaptation (Gardner 1963 Matzinger 1963 Hallauer and Miranda 1988). The marriage between quantitative genetics and plant breeding, albeit nonexclusive, has reaped benefits for both during the last 100 years.
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This trend will continue as the amount and types of data available in a breeding program increase. Over the years, quantitative genetics in plant breeding has become increasingly empirical and computational and less grounded in theory. The genetic entities in such simulations should not be generic but should be represented by the pedigrees, marker data, and phenotypic data for the actual germplasm in a breeding program. Examples include reliability as a new measure of the influence of genetic versus nongenetic effects, and operations research and simulation approaches for designing breeding programs.
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Plant breeding would benefit from borrowing approaches found useful in other disciplines. Doing so is feasible because with molecular markers, mixed-model approaches that require minimal genetic assumptions can be used for best linear unbiased estimation (BLUE) and prediction. Because the entirety of germplasm available in a breeding program is not in Hardy–Weinberg equilibrium, classical concepts that assume random mating, such as the average effect of an allele and additive variance, need to be retired in plant breeding. The core concept of continuous variation being due to multiple Mendelian loci remains unchanged. Keeping quantitative genetics current requires keeping old concepts that remain useful, letting go of what has become archaic, and introducing new concepts and methods that support contemporary breeding. In plant breeding, the main focus of quantitative genetics is on identifying candidates with the best genotypic value for a target population of environments. The goals of quantitative genetics differ according to its field of application.