Biology subjects

Graef, G.

Publications and source records attributed to Graef, G..

4 recordsLinked to original sources

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

Leaf nitrogen does not explain changes in soybean radiation-use efficiency in vegetative and early reproductive stages

Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed photosynthetically active radiation (APAR), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated APAR, accounting for changes in biomass composition. The SLN and RUE exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. The level of N supply influenced SLN more than RUE via changes in leaf N concentration, with small changes in specific leaf weight. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages. A simple approach based on phenological stages may give more realistic estimates of RUE before seed filling, improving crop growth and yield prediction via crop models and remote sensing. HighlightChanges in radiation-use efficiency during soybean vegetative and early reproductive stages are not related to specific leaf nitrogen.

physiology↗

bioRxiv2019

Generating High Density, Low Cost Genotype Data in Soybean

Obtaining genome-wide genotype information for millions of SNPs in soybean [Glycine max (L.) Merr.] often involves completely resequencing a line at 5X or greater coverage. Currently, hundreds of soybean lines have been resequenced at high depth levels with their data deposited in the NCBI short read achieve. This publicly available dataset may be leveraged as an imputation reference panel in combination with skim (low coverage) sequencing of new soybean genotypes to economically obtain high-density SNP information. Ninety-nine soybean lines resequenced at an average of 17.1X were used to generate a reference panel, with over 10 million SNPs called using GATKs Haplotype Caller tool. Whole genome resequencing at approximately 1X depth was performed on 114 previously ungenotyped experimental soybean lines. Coverages down to 0.1X were analyzed by randomly subsetting raw reads from the original 1X sequence data. SNPs discovered in the reference panel were genotyped in the experimental lines after aligning to the soybean reference genome, and missing markers imputed using Beagle 4.1. Sequencing depth of the experimental lines could be reduced to 0.3X while still retaining an accuracy of 97.8%. Accuracy was inversely related to minor allele frequency, and highly correlated with marker linkage disequilibrium. The high accuracy of skim sequencing combined with imputation provides a low cost method for obtaining dense genotypic information that can be used for various genomics applications in soybean.

genomics↗

bioRxiv2017

An epigenetic breeding system in soybean for increased yield and stability

Epigenetic variation has been associated with a wide range of adaptive phenotypes in plants, but there exist few direct means for exploiting this variation. RNAi suppression of the plant-specific gene, MutS HOMOLOG1 (MSH1), in multiple plant species produces a range of developmental changes accompanied by modulation of defense, phytohormone, and abiotic stress response pathways. This msh1-conditioned developmental reprogramming is retained independent of transgene segregation, giving rise to transgene-null memory effects. An isogenic memory line crossed to wild type produces progeny families displaying increased variation in adaptive traits that respond to selection. This study investigates amenability of the MSH1 system for inducing epigenetic variation in soybean that may be of value agronomically. We developed epi-line populations by crossing with msh1-acquired soybean memory lines. Derived soybean epi-lines showed increase in variance for multiple yield-related traits including pods per plant, seed weight, and maturity time in both greenhouse and field trials. Selected epi-F2:4 and epi-F2:5 lines showed an increase in seed yield over wild type. By epi-F2:6, we observed a return of MSH1-derived enhanced growth back to wild type levels. Epi-populations also showed evidence of reduced epitype-by-environment (e x E) interaction, indicating higher yield stability. Transcript profiling of the soybean epi-lines identified putative signatures of enhanced growth behavior across generations. Genes related to cell cycle, abscisic acid biosynthesis, and auxin-response, particularly SMALL AUXIN UP RNAs (SAURs), were differentially expressed in epi-F2:4 lines that showed increased yield when compared to epi-F2:6. These data support the potential of msh1-derived epigenetic variation in plant breeding for enhanced yield and yield stability.

plant biology↗