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Using Genome-Wide Association Studies to Reveal DArTseq and SNP Loci Associated with Agronomic Traits and Yield in Maize
Next-generation sequencing (NGS) has revolutionized genetic research, enabling the massive, rapid, and relatively inexpensive analysis of the genomes, transcriptomes, and epigenomes of various organisms, including maize. Therefore, this paper uses NGS, association mapping, and physical mapping to identify candidate genes associated with yield structure traits and yield in maize (Zea mays L.). Furthermore, expression analysis of selected candidate genes was performed to confirm their contribution to yield formation. The plant material used for the study was 186 F1 hybrids and 20 reference genotypes (high-yielding and low-yielding). Field experiments were conducted simultaneously in two locations (in Smolice and Kobierzyce). NGS yielded a total of 45,876 molecular markers (24,437 SilicoDArT markers and 21,439 SNP markers) relevant to yield and crop structure. The largest number of markers in both localities (Smolice and Kobierzyce) was related to: the number of grain rows (6960), dry matter content after harvest (6616), the number of grains in a row (6721), mass of grain from the cob (6616), and cob length (6564). The smallest number of markers in both localities was related to yield (t ha−1) (1114) and yield from the plot (1237). To narrow down the number of markers for physical mapping, ten were selected from all the significant ones associated with the same traits in both localities (Kobierzyce and Smolice). Significant markers included eight silicoDArT markers (459199, 2447305, 4768759, 4579916, 4764335, 2448946, 2492509, 4774802) and two SNP markers (9692004, 5587791). These markers were used for physical mapping. These markers are located on chromosomes 7, 8, and 10. Some of these markers are located at a considerable distance from characterized genes or within uncharacterized genes. Two markers caught our attention: SNP 5587791 and silicoDArT 4774802. The first one is located on chromosome 8 inside exon 5 of the LOC100383455 U-box domain-containing protein 7 gene, the second marker is also located on chromosome 8 near (300 bp) the LOC103635953 putative WUSCHEL-related homeobox 2 protein gene. Our own research and literature reports indicate the usefulness of next-generation sequencing, association mapping, and physical mapping for identifying candidate genes associated with economically important traits in maize. Furthermore, two genes characterized in detail in the publication, LOC100383455 U-box domain-containing protein 7 gene and LOC103635953 putative WUSCHEL-related homeobox 2 protein gene, may be involved in processes related to maize yield.
Application of Artificial Neural Network Sensitivity Analysis to Identify Key Determinants of Harvesting Date and Yield of Soybean (Glycine max [L.] Merrill) Cultivar Augusta
Genotype and weather conditions play crucial roles in determining the volume and stability of a soybean yield. The aim of this study was to identify the key meteorological factors affecting the harvest date (model M_HARV) and yield of the soybean variety Augusta (model M_YIELD) using a neural network sensitivity analysis. The dates of the start of flowering and maturity, the yield data, the average daily temperatures and precipitation were collected, and the Selyaninov hydrothermal coefficients were calculated during a fifteen-year study (2005–2020 growing seasons). During the experiment, highly variable weather conditions occurred, strongly modifying the course of phenological phases in soybean and the achieved seed yield of Augusta cultivar. The harvesting of mature soybean seeds took place between 131 and 156 days after sowing, while the harvested yield ranged from 0.6 t·ha−1 to 2.6 t·ha−1. The sensitivity analysis of the MLP neural network made it possible to identify the factors which had the greatest impact on the tested dependent variables among all the analyzed factors. It was revealed that the variables assigned ranks 1 and 2 in the sensitivity analysis of the neural network forming the M_HARV model were total rainfall in the first decade of June and the first decade of August. The variables with the highest impact on the Augusta soybean seed yield (model M_YIELD) were the mean daily air temperature in the second decade of May and the Seljaninov coefficient values calculated for the sowing–flowering date period.
Stability of Early Maturing Soybean Genotypes in Poland
Poland is one of the northernmost countries in the world where soybean breeding is actively conducted. A large collection of soybean genotypes was analyzed in three locations in Poland during the 2022–2023 growing seasons. The objective of this research was to determine the stability of yield, as well as the protein and fat content in the seeds of 280 genotypes. The phenotypic correlations across genotypes and environments were assessed using the Spearman coefficient. The statistical analysis was provided using the additive main effects and multiplicative interaction model. The stability of genotypes was assessed using the weighted average of absolute scores. The main factor differentiating the results between locations was the variation in weather conditions. The variety Karok, which ranks among the top 10% in genotype selection indices for yield and thousand-seed weight, is recommended based on studies conducted within the Post-Registration Variety Testing System in three voivodeships in Poland. Among the most stable genotypes, in terms of the traits analyzed in this paper, are many registered and currently grown soybean varieties such as Obelix Pompei, Coraline, Kapral, ES Mentor, NS Mercury, Pepita, Pollux, Silesia, and Heihe 45. The yield stability of soybean genotypes during the development of new varieties should be one of the factors analyzed by breeders.