Biology subjects

Lorenz, A. J.

Publications and source records attributed to Lorenz, A. J..

9 recordsLinked to original sources

bioRxiv2026

Inter-variety competition dynamics in US inbred and hybrid maize

Variety mixtures provide a potential avenue in US cropping systems to improve yield stability and disease resistance. However, implementation of variety mixtures requires an understanding of the competitive dynamics of the crop. In this study, we examine the effects of plant competition both between and within plots through five unique experiments: 1) 5,000 diverse inbred lines in single-row plots, 2) hybrids in two-row plots developed from the above inbred lines, 3) over 4,000 hybrids measured in 141 locations in two-row plots as part of Genomes to Fields, 4) mixtures of two hybrids within a two-row plot planted across two years and five locations, and 5) mixtures of up to twenty hybrids in four-row plots in three locations. Across all experiments, we find that competitive interactions are extremely limited. Within inbred lines, height of the neighboring plot accounts for 1.2% of the variance in focal plot height. Similarly, neighbor height explains 1.7% of the variance in focal plot yield in hybrids developed from the inbred lines. The genetics of neighboring plots explains 1.55% of the variation in yield across 141 location-year environments, reinforcing the generally modest impacts of neighbor competition. In evaluating mixtures of hybrids in both two and four-row plots, we observe no yield penalty compared to conventional single hybrid plots, even with large height differentials of the hybrids included in the mixture or in mixtures of up to 20 hybrids within a plot. Finally, we observe that mixtures have more yield stability compared to conventional plots, highlighting a new avenue for increased stability in higher risk environments. The lack of yield penalty and stability benefits are promising for future investigations of mixtures that may complement each other in disease resistance or abiotic stress tolerance and increase overall yield stability in the field.

plant biology↗

bioRxiv2024

Genomic and Phenomic Prediction for Soybean Seed Yield, Protein, and Oil

Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine max L. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the use and performance of GP and PP for predicting soybean seed yield, protein content, and oil content. We additionally conducted Genome Wide Association Studies (GWAS) to identify significant SNPs associated with the traits of interest. These SNPs were also used to train the GP models. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at three timepoints during the growing season. A GBLUP model was trained on 268 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that for PP, Random Forest (RF) algorithm had the highest rank correlation between the predicted and the actual phenotype rank. PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new RF model was trained with the inclusion of selected SNPs from the GWAS results. These models did not outperform the original GP and PP models. These results show the capability of using ML for in-season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs. PLAIN LANGUAGE SUMMARYObtaining DNA information can often be costly for a public breeding program to obtain, which is a barrier of entry for using genomic prediction in these programs to make selections. Phenomic prediction provides an alternative opportunity that does not require the recurring costs of DNA sequencing. This research aimed to compare genomic and phenomic prediction in a diverse panel of soybeans. We found that phenomic prediction had the highest accuracy for seed yield, while genomic prediction was the best model for seed protein and oil. Combining genomic and phenomic data did not improve the predictive ability of the models. Core ideas O_LIIn-season phenomic prediction is able to outperform genomic prediction for seed yield. C_LIO_LIGenomic prediction outperformed in-season phenomic prediction models for seed protein and oil. C_LIO_LIOverlapping spectral indices were identified as the most predictive for seed yield, protein, and oil. C_LIO_LIFusion of genomic and phenomic prediction did not increase the predictive ability of the combined models. C_LI

plant biology↗

bioRxiv2024

High-throughput phenotyping of soybean transpiration response curves to rising atmospheric drying in a mapping population

In soybean, limiting whole-plant transpiration rate (TR) response to increasing vapor pressure deficit (VPD) has been associated with the slow-wilting phenotype and with water- conservation enabling higher yields under terminal drought. Despite the promise of this trait, it is still unknown whether it has a genetic basis in soybean, a challenge limiting the prospects of breeding climate-resilient varieties. Here we present the results of a first attempt at a high- throughput phenotyping of TR and stomatal conductance response curves to increasing VPD conducted on a soybean mapping population consisting of 140 recombinant inbred lines (RIL). This effort was conducted over two consecutive years, using a controlled-environment, gravimetric phenotyping platform which enabled characterizing 900 plants for these responses, yielding regression parameters (R2 from 0.92 to 0.99) that were used for genetic mapping. Several quantitative trait loci (QTL) were identified for these parameters on chromosomes (Ch) 4, 6 and 10, including a VPD-conditional QTL on Ch 4 and a constitutive QTL controlling all parameters on Ch 6. This study demonstrated for the first time that canopy water use in response to rising VPD has a genetic basis in soybean, opening novel avenues for identifying alleles enabling water conservation under current and future climate scenarios.

plant biology↗

bioRxiv2022

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean

Increasing the rate of genetic gain for seed yield remains the primary breeding objective in both public and private soybean breeding programs. Genomic selection (GS) has the potential to accelerate the rate of genetic gain for soybean seed yield. To date, limited studies have empirically validated accuracy of GS and compared to phenotypic selection (PS), and none has been done for soybean breeding. This study conducted the first empirical validation of GS for increasing seed yield using over 1,500 lines and over 7 years (2010-2016) of replicated experiments in the University of Nebraska soybean breeding program. The study was designed to capture the varying genetic relatedness of the training population to three validation sets: two large bi-parental populations (TBP-1 and TBP-2), and a large validation set comprised of 457 pre-selected advanced lines derived from 45 bi-parental populations in the variety development program (TMP). We found that prediction accuracy (0.54) from our validation experiments was competitive with what we obtained from a series of cross-validation experiments (0.64). Both GS and PS were more effective for increasing population mean performance with similar realized gain but significantly greater than random selection (RS). We found a selection advantage of GS over PS where higher genetic gain and identification of top-performing lines was maximized at higher selection stringency from 10 to 20% selected proportion. GS led to at least 2% increase in the mean genetic similarity vs. PS and RS, potentially causing a minimal loss of genetic diversity. We showed that loss of genetic variance in the GS set was presumably due to a significant shift on allelic frequencies towards the extremes. Across all loci, an average increase of 0.04 in allelic frequency in the GS set was observed after selection, which is about 5% higher than the base population when no selection was made. Overall, we demonstrate that GS performed as effectively as PS, and the implementation of GS in a public soybean breeding program should be warranted mainly for reducing breeding cycle time and lowering cost per unit gain.

genetics↗

bioRxiv2022

Development of Molecular Inversion Probes for Soybean Progeny Genomic Selection Genotyping

Increasing rate of genetic gain for key agronomic traits through genomic selection requires the development of new molecular methods to run genome-wide single nucleotide polymorphisms (SNPs). The main limitation of current methods is the cost is too high to screen breeding populations. Molecular inversion probes (MIPs) is a targeted genotyping-by-sequencing method that could be used for soybeans that is both cost effective, high-throughput, and provides high data quality to screen breeders germplasm for genomic selection. A 1K MIP SNP set was developed for soybean with uniformly distributed markers across the genome. The SNPs were selected to maximize the number of informative markers in germplasm being tested in soybean breeding programs located in the North Central and Mid-South regions of the United States. The 1K SNP MIP set was tested on diverse germplasm and a recombinant inbred line population. Targeted sequencing with MIPs obtained an 85% enrichment for the targeted SNPs. MIPs genotyping accuracy was 93% overall while homozoygous call accuracy was 98% with less than 10% missing data. The accuracy of MIPs combined with its low per sample cost makes it a powerful tool to enable genomic selection within soybean breeding programs.

genomics↗

bioRxiv2019

The relevance of dominance and functional annotations to predict agronomic traits in hybrid maize

Heterosis has been key to the development of maize breeding but describing its genetic basis has been challenging. Previous studies of heterosis have shown the contribution of within-locus complementation effects (dominance) and their differential importance across genomic regions. However, they have generally considered panels of limited genetic diversity and have shown little benefit to including dominance effects for predicting genotypic value in breeding populations. This study examined within-locus complementation and enrichment of genetic effects by functional classes in maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the United States (1,106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1,640 hybrids). We assayed three agronomic traits: days to silking (DTS), plant height (PH) and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results point to mechanisms by which heterosis arises through local complementation in proximal gene regions and suggest the relevance of dominance and gene annotations for genomic prediction in maize.

genomics↗

bioRxiv2019

Characterizing allele-by-environment interactions using maize introgression lines

Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to allele-by-environment interactions. To study potential patterns of allele-by-environment interactions, fifteen near-isogenic lines (NILs) with >90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess allele-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the allele-by-environment interaction varies across traits. Some introgressed segments were more prone to genotype-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of allele-by-environment interaction is useful in considerations of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.\n\nKey MessageSignificant allele-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome.

plant biology↗

bioRxiv2019

Multi-Trait Improvement by Predicting Genetic Correlations in Breeding Crosses

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e. pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.

genetics↗

bioRxiv2016

Evaluating Methods of Updating Training Data in Long-Term Genomewide Selection

Genomewide selection is hailed for its ability to facilitate greater genetic gains per unit time. Over breeding cycles, the requisite linkage disequilibrium (LD) between quantitative trait loci (QTL) and markers is expected to change as a result of recombination, selection, and drift, leading to a decay in prediction accuracy. Previous research has identified the need to update the training population using data that may capture new LD generated over breeding cycles, however optimal methods of updating have not been explored. In a barley (Hordeum vulgare L.) breeding simulation experiment, we examined prediction accuracy and response to selection when updating the training population each cycle with the best predicted lines, the worst predicted lines, both the best and worst predicted lines, random lines, criterion-selected lines, or no lines. In the short-term, we found that updating with the best predicted lines or the best and worst predicted lines resulted in high prediction accuracy and genetic gain, but in the long-term, all methods (besides not updating) performed similarly. We also examined the impact of including all data in the training population or only the most recent data. Though patterns among update methods were similar, using a smaller, but more recent training population provided a slight advantage in prediction accuracy and genetic gain. In an actual breeding program, a breeder might desire to gather phenotypic data on lines predicted to be the best, perhaps to evaluate possible cultivars. Therefore, our results suggest that an optimal method of updating the training population is also very practical.

genetics↗