Genomic selection-based breeding programs offer significant advantages over conventional phenotypic selection, particularly in accelerating genetic gains in plant breeding, as demonstrated by simulations focused on combating Fusarium head blight (FHB) in wheat. FHB resistance, a crucial trait, is challenging to breed for due to its quantitative inheritance and environmental influence, leading to slow progress using conventional breeding methods. Stochastic simulations in our study compared various breeding schemes, incorporating genomic selection (GS) and combining it with speed breeding, against conventional phenotypic selection. Two datasets were simulated, reflecting real-life genotypic data (MASBASIS) and a simulated wheat breeding program (EXAMPLE). Initially a 20-year burn-in phase using a conventional phenotypic selection method followed by a 20-year advancement phase with three GS-based breeding programs (GSF2F8, GSF8, and SpeedBreeding + GS) were evaluated alongside over a conventional phenotypic selection method. Results consistently showed significant increases in genetic gain with GS-based programs compared to phenotypic selection, irrespective of the selection strategies employed. Among the GS schemes, SpeedBreeding + GS consistently outperformed others, generating the highest genetic gains. This combination effectively minimized generation intervals within the breeding cycle, enhancing efficiency. This study underscores the advantages of genomic selection in accelerating breeding gains for wheat, particularly in combating FHB. By leveraging genomic information and innovative techniques like speed breeding, breeders can efficiently select for desired traits, significantly reducing testing time and costs associated with conventional phenotypic methods.

Increasing planting density is one of the most important strategies for generating higher maize yields. Moderate leaf rolling decreases mutual shading of leaves and increases the photosynthesis of the population and hence increases the tolerance for high-density planting. Few genes that control leaf rolling in maize have been identified, however, and their applicability for breeding programs remains unclear. Here we identified a maize abaxially rolled leaf1 (arl1) mutant with extreme abaxially rolled leaves and found that the size of the bulliform cells within the adaxial leaf blade surface increased in the arl1 mutant. Bulk segregation analysis mapping in an F2 population derived from a single cross between arl1 and inbred line Gui18421 with normal leaves identified the arl1 locus on chromosome 2. Sequential fine-mapping delimited the arl1 locus to a 233.56-kb genomic interval containing three candidate genes. Sequence alignment between arl1 and Gui18421 identified an 8-bp insertion in the coding region of Zm00001eb082500, which led to a frame shift causing premature transcription termination in arl1 mutant. Meanwhile, both deep sequencing and Sanger sequencing showed that Zm00001eb082520 was present in Gui18421 but was absent in arl1. A pair of near isogenic lines (NILs) carrying the Gui18421 allele (NILGui18421) and the arl1 allele (NILarl1) were developed, and the leaves of NILarl1 plants had greater light transmission and photosynthetic rate in the middle and lower canopy than did those of NILGui18421 plants under high-density planting. Furthermore, NILarl1 had a higher seed setting rate, more kernels per ear, and an increased kernel weight per ear than NILGui18421, and the grain yield of NILarl1 was not affected as the planting density increased, suggesting that the arl1 locus can be used for genetic improvement of high-density planting tolerance. Taken together, the identification of arl1 and evaluation of yield-related traits for NILGui18421 and NILarl1 provide an excellent target for future maize improvement.

The online version contains supplementary material available at 10.1007/s11032-023-01410-3.

The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.

The online version contains supplementary material available at 10.1007/s11032-023-01379-z.

The online version contains supplementary material available at 10.1007/s11032-023-01373-5.

In eukaryotic cells, 3D genome plays an important role in the regulation of gene spatiotemporal expression, which is essential for the biological and developmental processes in a life cycle. In the past decade, the development of high-throughput technologies greatly enhances our ability to map the 3D genome organization, identifies multiple 3D genome structures, and investigates the functional role of 3D genome organization in gene regulation, which facilitates our understandings of cis-regulatory landscape and biological development. Comparing with the comprehensive analyses of 3D genome in mammals and model plants, the progress in soybean is much less. Future development and application of tools to precisely manipulate 3D genome structure at different levels will significantly strengthen the functional genome study and molecular breeding in soybean. Here, we review the recent progresses in 3D genome study and discuss future directions, which may help to improve soybean 3D functional genome study and molecular breeding.

The online version contains supplementary material available at 10.1007/s11032-023-01355-7.

The online version contains supplementary material available at 10.1007/s11032-023-01356-6.