Genome wide association study identifies novel single nucleotide polymorphic loci and candidate genes involved in soybean sudden death syndrome resistance

Ideotype breeding and genome engineering for legume crop improvement

Ideotype breeding is a strategy whereby traits are modeled a priori and then introduced into a model or crop species to assess their impact on yield. Thus, knowledge about the connection between genotype and phenotype is required for ideotype breeding to be deployed successfully. The growing understanding of the genetic basis of yield-related traits, combined with increasingly efficient genome engineering tools, improved transformation efficiency, and high-throughput genotyping of regenerants paves the way for the widespread adoption of ideotype breeding as a complement to conventional breeding. We briefly discuss how ideotype breeding, coupled with such state-of-the-art biotechnological tools, could contribute to knowledge-based legume breeding and accelerate yield gains to ensure food security in the coming decades.

Breeding for disease resistance in soybean: a global perspective

KEY MESSAGE

This review provides a comprehensive atlas of QTLs, genes, and alleles conferring resistance to 28 important diseases in all major soybean production regions in the world. Breeding disease-resistant soybean [Glycine max (L.) Merr.] varieties is a common goal for soybean breeding programs to ensure the sustainability and growth of soybean production worldwide. However, due to global climate change, soybean breeders are facing strong challenges to defeat diseases. Marker-assisted selection and genomic selection have been demonstrated to be successful methods in quickly integrating vertical resistance or horizontal resistance into improved soybean varieties, where vertical resistance refers to R genes and major effect QTLs, and horizontal resistance is a combination of major and minor effect genes or QTLs. This review summarized more than 800 resistant loci/alleles and their tightly linked markers for 28 soybean diseases worldwide, caused by nematodes, oomycetes, fungi, bacteria, and viruses. The major breakthroughs in the discovery of disease resistance gene atlas of soybean were also emphasized which include: (1) identification and characterization of vertical resistance genes reside rhg1 and Rhg4 for soybean cyst nematode, and exploration of the underlying regulation mechanisms through copy number variation and (2) map-based cloning and characterization of Rps11 conferring resistance to 80% isolates of Phytophthora sojae across the USA. In this review, we also highlight the validated QTLs in overlapping genomic regions from at least two studies and applied a consistent naming nomenclature for these QTLs. Our review provides a comprehensive summary of important resistant genes/QTLs and can be used as a toolbox for soybean improvement. Finally, the summarized genetic knowledge sheds light on future directions of accelerated soybean breeding and translational genomics studies.

Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean

Using a reliable and accurate method to phenotype disease incidence and severity is essential to unravel the complex genetic architecture of disease resistance in plants, and to develop disease resistant cultivars. Genome-wide association studies (GWAS) involve phenotyping large numbers of accessions, and have been used for a myriad of traits. In field studies, genetic accessions are phenotyped across multiple environments and replications, which takes a significant amount of labor and resources. Deep Learning (DL) techniques can be effective for analyzing image-based tasks; thus DL methods are becoming more routine for phenotyping traits to save time and effort. This research aims to conduct GWAS on sudden death syndrome (SDS) of soybean [Glycine max L. (Merr.)] using disease severity from both visual field ratings and DL-based (using images) severity ratings collected from 473 accessions. Images were processed through a DL framework that identified soybean leaflets with SDS symptoms, and then quantified the disease severity on those leaflets into a few classes with mean Average Precision of 0.34 on unseen test data. Both visual field ratings and image-based ratings identified significant single nucleotide polymorphism (SNP) markers associated with disease resistance. These significant SNP markers are either in the proximity of previously reported candidate genes for SDS or near potentially novel candidate genes. Four previously reported SDS QTL were identified that contained a significant SNPs, from this study, from both a visual field rating and an image-based rating. The results of this study provide an exciting avenue of using DL to capture complex phenotypic traits from images to get comparable or more insightful results compared to subjective visual field phenotyping of traits for disease symptoms.

Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

Soybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application ( https://soyfungigcn.venanciogroup.uenf.br/ ) and an R/Shiny package ( https://github.com/almeidasilvaf/SoyFungiGCN ) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

Comparative genomic analyses of two segregating mutants reveal seven genes likely involved in resistance to Fusarium equiseti in soybean via whole genome re-sequencing

KEY MESSAGE

The candidate genes involved in resistance to Fusarium equiseti in soybean PI 437654 were identified through comparative genomic analyses of mutants via whole genome re-sequencing. The fungus Fusarium infects each stage of the growth and development of soybean and causes soybean (Glycine max (L.)) seed and root rot and seedling damping-off and wilt with a large quantity of annual yield loss worldwide. It is very important to identify the resistant genes in soybean to prevent and control this pathogen. One Fusarium equiseti isolate was previously identified to be incompatible with 'PI 437654' but compatible with a Chinese soybean cultivar 'Zhonghuang 13'. In this study, with the infection of this isolate on the seedling roots of developed PI 437654 mutants, 6 mutants were identified from 500 mutants to significantly alter their phenotypes to F. equiseti compared to wild-type PI 437654. Then, two identified segregating mutants were selected to directly perform whole genome re-sequencing. Finally, through comparative genomic analyses 7 genes including one cluster of 4 nucleotide binding site-leucine-rich repeat genes on one genomic region of chromosome 7, a 60S ribosomal protein L12 gene and 2 uncharacterized genes were identified to be likely involved in the resistance to F. equiseti. These genes will facilitate the breeding of resistant germplasm resources and the identification of resistance of soybean to Fusarium spp.

Effective identification of soybean candidate genes involved in resistance to soybean cyst nematode via direct whole genome re-sequencing of two segregating mutants

KEY MESSAGE

Three soybean candidate genes involved in resistance to soybean cyst nematode race 4 were identified via direct whole genome re-sequencing of two segregating mutants. The genes conferring resistance to soybean cyst nematode (SCN) race 4 (Hg type 1.2.3.5.7) in soybean (Glycine max L. Merr.) remains unknown. Next generation sequencing-based methods identify a wide range of targets, it is difficult to identify genes underlying traits. Use of the MutMap and QTL-seq methods to identify trait candidate genes needs backcrossing and is very time-consuming. Here we report a simple method to effectively identify candidate genes involved in resistance to SCN race 4. Two ethane methylsulfonate mutagenized mutants of soybean 'PI 437654', whose SCN race 4-infection phenotype altered, were selected. Six relevant whole genomes were re-sequenced, and then calling of genomic variants (SNPs and InDels) was conducted and compared to 'Williams 82'. The comparison eliminated many genomic variants from the mutant lines that overlapped two non-phenotypic but mutant progeny plants, wild-type PI 437654 and 'Zhonghuang 13'. Finally, only 27 mutations were found among 10 genes. Of these 10 genes, 3 genes, Glyma.09g054000, Glyma.16g065700 and Glyma.18g192200 were overlapped between two phenotypic mutant progeny plants. Therefore, the three genes may be the candidate genes involved in resistance of PI 437654 to soybean cyst nematode race 4. This method simplifies the effective identification of candidate genes.