Indução de haploides em milho tropical superdoce e determinação de ploidia no estágio de plântulas

The objective of this work was to determine the possibility of haploid induction in tropical supersweet corn (Zea mays L. var. saccharata) using a maternal inducer, as well as to identify alternative methods for haploid selection. A single-cross hybrid of field corn and 11 tropical supersweet corn populations were crossed with the haploid inducer. The haploids were pre-selected using the R1-navajo marker and were differentiated into haploids or false positives at the V2–V3 stage, based on the color of the first leaf sheath and on the length of stomata guard cells. The obtained results are indicative of the possibility of inducing maternal haploids in populations of tropical supersweet corn. However, a large number of false-positive haploids were incorrectly selected by the R1-navajo marker. The color of the first leaf sheath was efficient for haploid identification in supersweet corn populations, and the method based on stomata morphometry can be used when there is no color distinction on the first leaf sheath.O objetivo deste trabalho foi verificar a possibilidade de indução de haploidia em milho superdoce tropical (Zea mays L. var. saccharata) por meio de indutor maternal, bem como identificar métodos alternativos para seleção de haploides. Um híbrido simples de milho comum e 11 populações de milho superdoce tropical foram cruzados com o indutor de haploidia. Os haploides foram pré-selecionados pelo marcador R1-navajo e diferenciados em haploides ou falsos positivos, no estágio V2–V3, com base na cor da primeira bainha foliar e no comprimento das células-guarda dos estômatos foliares. Os resultados obtidos são indicativos da possibilidade de induzir haploides maternos em populações de milho superdoce tropical. Contudo, muitos haploides falso-positivos foram selecionados incorretamente pelo marcador R1-navajo. A coloração da primeira bainha foliar foi eficiente na identificação de haploides em populações de milho superdoce, e o método baseado na morfometria dos estômatos pode ser usado quando não há distinção de cor da primeira bainha foliar

Boosting predictive ability of tropical maize hybrids via genotype-by-environment interaction under multivariate GBLUP models.

Genomic selection has been implemented in several plant and animal breeding programs and it has proven to improve efficiency and maximize genetic gains. Phenotypic data of grain yield was measured in 147 maize (Zea mays L.) singlecross hybrids at 12 environments. Single-cross hybrids genotypes were inferred based on their parents (inbred lines) via single nucleotide polymorphism (SNP) markers obtained from genotyping-by-sequencing (GBS). Factor analytic multiplicative genomic best linear unbiased prediction (GBLUP) models, in the framework of multienvironment trials, were used to predict grain yield performance of unobserved tropical maize single-cross hybrids. Predictions were performed for two situations: untested hybrids (CV1), and hybrids evaluated in some environments but missing in others (CV2). Models that borrowed information across individuals through genomic relationships and within individuals across environments presented higher predictive accuracy than those models that ignored it. For these models, predictive accuracies were up to 0.4 until eight environments were considered as missing for the validation set, which represents 67% of missing data for a given hybrid. These results highlight the importance of including genotype-by-environment interactions and genomic relationship information for boosting predictions of tropical maize single-cross hybrids for grain yield

Strategies to untangle genetic and non-genetic sources of variation in cultivar development programs

Prospects of population growth along with the increased demand for protein-based diets require plant variety development programs, especially for commodity crops, to deliver cultivars that have increased yields and are able to tolerate environmental stresses such as heat, drought, disease, and insect pests. The phenotypic expression (P) of quantitative traits is influenced not only by genetic factors (G) but also by climate and agronomic practices (E) which interact with G. For over a century, the expression of P has been modeled as G + E + GE and various experimental and analytical methods have attempted to disentangle confounded G and non-G components of P. Herein, we investigated various linear mixed models to provide unbiased estimates of the G component for yield evaluated in annual multi-environment trials (MET). Data from MET are used to make decisions about which replicable genotypes have the potential to be released as varieties. Phenotypic data from MET conducted by publicly supported plant breeders of commodity crops such as soybean are available for investigations of analytic methods and models. We hypothesized it is possible to estimate Realized Genetic Gain (RGG) using published data from routine annual MET. We approached the research question using a combination of analyses of empirical data and simulated data and present the results in two independent manuscripts, presented as Chapters 2 and 3. In Chapter 2, we explored an empirical dataset of advanced MET obtained from public soybean varietal development programs responsible for genetic improvement in maturity zones II and III of the United States. This dataset is composed of 39,006 phenotypic records from 4,257 experimental lines, 63 locations, and 31 years (1989-2019), and is available in the R package SoyURT. The results from this chapter revealed that for seed yield (i) the variation due to genotype by location was more important than the variation due to genotype by year, (ii) the observed 63 locations can be grouped into mega-environments using phenotypic, geographic, and meteorological data, and (iii) information about the estimated variances of GE interaction (GEI) component of the P variance can be represented as probability distributions. The value of such information is to provide sampling distributions for simulation studies, such as we conducted in Chapter 3. In Chapter 3, we evaluated several linear mixed models to estimate RGG for seed yield from simulated MET. Simulation models were designed based on information from Chapter 2. For example, in the simulated GEI models, correlated quantitative trait loci effects were simulated for genotype by year and genotype by location interaction effects. We further extended the simulator to incorporate a positive rate of non-genetic gain to represent advances in agronomic management practices. The analytic models used to estimate RGG in the simulated data were then compared in terms of bias and linearity. Bias was quantified according to a definition of RGG that is applicable to variety development programs. We proposed RGG be defined as the accumulation of beneficial alleles in breeding lines (i.e., experimental lines used in crossing blocks) across years of breeding operation. This definition is consistent with the original concept of genetic gain. Simulation results indicated all analytic models used to estimate RGG provided biased results. Covariance modeling as well as direct versus indirect estimation resulted in substantial differences in RGG estimation. Although there were no unbiased models, the three models with the least bias and smallest values of root mean squared error resulted in an average bias of $\pm$7.41 kg/ha$^{-1}$/yr$^{-1}$ ($\pm$0.11 bu/ac$^{-1}$/yr$^{-1}$). Rather than relying on a single model to estimate RGG from multiple years of field trials, we recommend the application of multiple models and utilizing the range of the estimated values for decision-making. Further, based on our simulations (number of environments, experimental genotypes, etc.), we do not think it is appropriate to use any single one of these models to compare breeding programs or quantify the efficiency of proposed new breeding strategies