Plant Breeding: Past, Present and Future

Bradshaw, J. E.

doi:10.1007/978-3-319-23285-0

Cited by 58 publications

(31 citation statements)

References 488 publications

(682 reference statements)

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“…From the genetic point of view, GCA measures additive gene effects, whereas SCA is the expression of non‐additive affects, either dominance or epistasis (Bradshaw, ).…”

Section: Discussionmentioning

confidence: 99%

Genetic analysis of mechanisms associated with inheritance of resistance to sheath rot of rice

et al. 2017

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Understanding genetic mechanisms controlling inheritance of disease resistance traits is essential in breeding investigations targeting development of resistant genotypes. Using North Carolina design II, 32 F 1 hybrids were generated by crossing eight susceptible to four resistant parents and submitted for field evaluation. The analysis of general and specific combining ability (GCA and SCA) indicated involvement of additive and non-additive gene action controlling inheritance of horizontal resistance to sheath rot of rice. High GCA/SCA ratio and high heritability estimates revealed additive effects and were more predominant than none additive ones. The level of dominance indicated dominant genes was more important than recessive genes. Estimates of GCA and SCA analysis suggested that crop improvement programmes should be directed towards selection of superior parents or good combiners, emphasizing on GCA. As far as source of resistance is concerned, most promising genotypes were Cyicaro, Yunertian and Yunkeng. The predominance of additive genetic effects together with the relevance of dominant genes suggested possibilities of improving the resistance by introgression of resistance genes through recurrent selection coupled with phenotypic selection.

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“…From the genetic point of view, GCA measures additive gene effects, whereas SCA is the expression of non‐additive affects, either dominance or epistasis (Bradshaw, ).…”

Section: Discussionmentioning

confidence: 99%

Genetic analysis of mechanisms associated with inheritance of resistance to sheath rot of rice

et al. 2017

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“…In many crops, synthetics also serve as cultivars in agricultural production or as source populations for recurrent selection programs (cf. Bradshaw 2016). Synthetics are usually created by crossing a small number of parents (N P ) and subsequently cross-pollinating the F 1 individuals for one or several generations .…”

mentioning

confidence: 99%

Accuracy of Genomic Prediction in Synthetic Populations Depending on the Number of Parents, Relatedness, and Ancestral Linkage Disequilibrium

Schopp

Müller

Technow³

et al. 2017

Genetics

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Synthetics play an important role in quantitative genetic research and plant breeding, but few studies have investigated the application of genomic prediction (GP) to these populations. Synthetics are generated by intermating a small number of parents ([Formula: see text] and thereby possess unique genetic properties, which make them especially suited for systematic investigations of factors contributing to the accuracy of GP. We generated synthetics in silico from [Formula: see text]2 to 32 maize (Zea mays L.) lines taken from an ancestral population with either short- or long-range linkage disequilibrium (LD). In eight scenarios differing in relatedness of the training and prediction sets and in the types of data used to calculate the relationship matrix (QTL, SNPs, tag markers, and pedigree), we investigated the prediction accuracy (PA) of Genomic best linear unbiased prediction (GBLUP) and analyzed contributions from pedigree relationships captured by SNP markers, as well as from cosegregation and ancestral LD between QTL and SNPs. The effects of training set size [Formula: see text] and marker density were also studied. Sampling few parents ([Formula: see text]) generates substantial sample LD that carries over into synthetics through cosegregation of alleles at linked loci. For fixed [Formula: see text], [Formula: see text] influences PA most strongly. If the training and prediction set are related, using [Formula: see text] parents yields high PA regardless of ancestral LD because SNPs capture pedigree relationships and Mendelian sampling through cosegregation. As [Formula: see text] increases, ancestral LD contributes more information, while other factors contribute less due to lower frequencies of closely related individuals. For unrelated prediction sets, only ancestral LD contributes information and accuracies were poor and highly variable for [Formula: see text] due to large sample LD. For large [Formula: see text], achieving moderate accuracy requires large [Formula: see text], long-range ancestral LD, and high marker density. Our approach for analyzing PA in synthetics provides new insights into the prospects of GP for many types of source populations encountered in plant breeding.

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“…This increase will have to occur in all crops, but especially in those staple crops such as wheat, which provide about 20% of the daily caloric intake for a human being [3]. Thus, the demand for wheat is expected to increase by around 60% in the next few decades, but the potential yield is expected to decrease by 20% due to the climate change [4]. Added to this perspective is the fact that breeders have only achieved an increase in wheat yield of less than 1% per year while forecasts demand an increase of around 2% to meet the growing global food demand [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…Direct methods are the most accurate and helpful for validating indirect measurements, but they are extremely time-consuming and prohibitively expensive when applied to large crop areas [20]. Moreover, their application in breeding programs would be of little use since they are often destructive and the sampled area within the small plots could be non-representative and lead to biased results [4,21]. Alternatively, in recent years, indirect methods based on bottom-up hemispherical photography, FPAR-inversion, spectral reflectance measurements and 3D point-clouds analysis have been developed [19,22,27,28].…”

Section: Introductionmentioning

confidence: 99%

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

et al. 2020

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Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

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Plant Breeding: Past, Present and Future

Cited by 58 publications

References 488 publications

Genetic analysis of mechanisms associated with inheritance of resistance to sheath rot of rice

Genetic analysis of mechanisms associated with inheritance of resistance to sheath rot of rice

Accuracy of Genomic Prediction in Synthetic Populations Depending on the Number of Parents, Relatedness, and Ancestral Linkage Disequilibrium

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

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