Browsing by Subject "Hybrid prediction"

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Comparison of omics technologies for hybrid prediction
(2019) Westhues, Matthias; Melchinger, Albrecht E.
One of the great challenges for plant breeders is dealing with the vast number of putative candidates, which cannot be tested exhaustively in multi-environment field trials. Using pedigree records helped breeders narrowing down the number of candidates substantially. With pedigree information, only a subset of candidates need to be subjected to exhaustive tests of their phenotype whereas the phenotype of the majority of untested relatives is inferred from their common pedigree. A caveat of pedigree information is its inability to capture Mendelian sampling and to accurately reflect relationships among individuals. This shortcoming was mitigated with the advent of marker assays covering regions harboring causal quantitative trait loci. Today, the prediction of untested candidates using information from genomic markers, called genomic prediction, is a routine procedure in larger plant breeding companies. Genomic prediction has revolutionized the prediction of traits with complex genetic architecture but, just as pedigree, cannot properly capture physiological epistasis, referring to complex interactions among genes and endophenotypes, such as RNA, proteins and metabolites. Given their intermediate position in the genotype-phenotype cascade, endophenotypes are expected to represent some of the information missing from the genome, thereby potentially improving predictive abilities. In a first study we explored the ability of several predictor types to forecast genetic values for complex agronomic traits recorded on maize hybrids. Pedigree and genomic information were included as the benchmark for evaluating the merit of metabolites and gene expression data in genetic value prediction. Metabolites, sampled from maize plants grown in field trials, were poor predictors for all traits. Conversely, root-metabolites, grown under controlled conditions, were moderate to competitive predictors for the traits fat as well as dry matter yield. Gene expression data outperformed other individual predictors for the prediction of genetic values for protein and the economically most relevant trait dry matter yield. A genome-wide association study suggested that gene expression data integrated SNP interactions. This might explain the superior performance of this predictor type in the prediction of protein and dry matter yield. Small RNAs were probed for their potential as predictors, given their involvement in transcriptional, post-transcriptional and post-translational regulation. Regardless of the trait, small RNAs could not outperform other predictors. Combinations of predictors did not considerably improve the predictive ability of the best single predictor for any trait but improved the stability of their performance across traits. By assigning different weights to each predictor, we evaluated each predictors optimal contribution for attaining maximum predictive ability. This approach revealed that pedigree, genomic information and gene expression data contribute equally when maximizing predictive ability for grain dry matter content. When attempting to maximize predictive ability for grain yield, pedigree information was superfluous. For genotypes having only genomic information, gene expression data were imputed by using genotypes having both, genomic as well as gene expression data. Previously, this single-step prediction framework was only used for qualitative predictors. Our study revealed that this framework can be employed for improving the cost-effectiveness of quantitative endophenotypes in hybrid prediction. We hope that these studies will further promote exploring endophenotypes as additional predictor types in breeding.
Genomic and phenotypic improvement of triticale (×Triticosecale Wittmack) line and hybrid breeding programs
(2021) Trini, Johannes Philipp; Würschum, Tobias
Triticale (×Triticosecale Wittmack) breeding is a success story as it evolved to a serious alternative in farmer’s crop rotations since the 1970s and is grown globally on around 4 million hectares today. New developments, however, pointed out additional possibilities to improve triticale line and hybrid breeding programs increasing its future competitiveness and were evaluated in this study. In more detail, these were to (i) examine the genetic control and evaluate long term genetic trends of plant height in Central European winter triticale, (ii) evaluate the potential of triticale hybrid breeding and hybrid prediction approaches in triticale with a focus on biomass yield, (iii) introduce and examine a concept bypassing the time and resource consuming evaluation of female candidate lines in cytoplasmatic male sterility (CMS) based hybrid breeding, and (iv) to draw conclusions for the future improvement of triticale line and hybrid breeding programs. The genome wide association study detected markers significantly associated with plant height and developmental stage, respectively. These explained 42,16% and 29,31% of the total genotypic variance of plant height and development stage and are probably related to four and three quantitative trait loci (QTL), respectively. The two major QTL detected for plant height were located on chromosomes 5A and 5R which most likely could be assigned to the known height reducing genes Rht12 from wheat and Ddw1 from rye. The third major QTL detected located on chromosome 4B could not be assigned to a known height reducing gene and it cannot be precluded, that these significantly associated markers are identifying one and the same QTL as the markers located on chromosome 5R, as these showed a high linkage disequilibrium amongst each other. Evaluating the 129 registered cultivars showed that plant height decreased since the 1980’s. Evaluating their genetic constitution revealed that most cultivars carried at least one height reducing QTL and that plant height could be reduced even further in cultivars combining more than one height reducing QTL. It was further observed that the frequency of cultivars carrying one or a combination of height reducing QTL increased since the 1980’s. A considerable amount of heterosis has been observed for biomass related traits in triticale hybrids before. However, the use of hybrid prediction approaches for these traits has not been evaluated. Hybrid prediction based on mid parent values already showed very good results illustrating their potential to preselect the most promising parents as prediction accuracies based on parental general combining ability (GCA) effects were only slightly better. When incorporating molecular markers into GCA based prediction accuracies, prediction accuracies decreased slightly compared to prediction accuracies solely based on phenotypic GCA effects. Predicting hybrids incorporating one or two untested parental lines, imitating a scenario where novel female and/or male candidate lines are introduced into a hybrid breeding program, reduced genomic prediction accuracies even further due to the decreasing amount of information which could be exploited from the parents. Additionally including specific combining ability (SCA) effects in the genomic prediction models did not yield additional use. A high proportion of SCA variance compared to the total genetic variance decreased prediction accuracies for the traits fresh and dry biomass yield. In this study simulation studies were used to demonstrate what a prediction accuracy of a specific value actually means for a hybrid breeding programs. Further, an approach was introduced and evaluated showing great potential to evaluate novel female candidate lines for their use in a CMS based hybrid breeding program by bypassing their time and resource demanding introgression into a male sterile cytoplasm using three way hybrids. Prediction accuracies obtained by this novel approach showed highly promising results for most evaluated traits compared to prediction accuracies based on GCA effects or mid parent performance. Additionally incorporating SCA effects into the prediction models showed only a little increase of the prediction accuracies. Further, the results were supported by simulation studies adjusting different parameters, such as the number of parents or the proportion of SCA variance compared to the total genetic variance.