Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics

N. Lopes I, Abdelnoor RV, Marcelino-Guimarães FC. Mapping of a soybean rust resistance in PI 594756 at the Rpp1 locus

Asian soybean rust (ASR), caused by the fungus Phakopsora pachyrhizi, is the main disease affecting soybean in Brazil. This study aimed at investigating and mapping the resistance of the PI 594756 to P. pachyrhizi, by using Bulked Segregant Analysis (BSA). The PI 594756 and the susceptible PI 594891 were crossed and the resulting F2 and F2:3 populations (208 and 1770 plants, respectively) were tested against ASR. Also, these PIs and differential varieties were tested against a panel of monosporic isolates. Plants presenting tan lesions were classified as susceptible (S) while plants presenting reddish-brown (RB) lesions were classified as resistant. DNA bulks were genotyped with Infinium BeadChips and the genomic region identified was further analyzed in the F2 individuals with target GBS (tGBS). PI 594,56 presented a unique resistance profile compared to the differential varieties. The resistance was monogenic dominant; however, it was classified as incompletely dominant when quantitatively studied. Genetic and QTL mapping placed the PI 594756 gene between the genomic region located at 55,863,741 and 56,123,516 bp of chromosome 18. This position is slightly upstream mapping positions of Rpp1 (PI 200492) and Rpp1-b (PI 594538A). Finally, we performed a haplotype analysis in a whole genomic sequencing-SNP database composed of Brazilian historical germplasm and sources of Rpp genes. We found SNPs that successfully differentiated the new PI 594756 allele from Rpp1 and Rpp1-b sources. The haplotype identified can be used as a tool for marker-assisted selection (MAS).

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01358-4.

Genetic relationships and genome selection signatures between soybean cultivars from Brazil and United States after decades of breeding

Soybean is one of the most important crops worldwide. Brazil and the United States (US) are the world's two biggest producers of this legume. The increase of publicly available DNA sequencing data as well as high-density genotyping data of multiple soybean germplasms has made it possible to understand the genetic relationships and identify genomics regions that underwent selection pressure during soy domestication and breeding. In this study, we analyzed the genetic relationships between Brazilian (N = 235) and US soybean cultivars (N = 675) released in different decades and screened for genomic signatures between Brazilian and US cultivars. The population structure analysis demonstrated that the Brazilian germplasm has a narrower genetic base than the US germplasm. The US cultivars were grouped according to maturity groups, while Brazilian cultivars were separated according to decade of release. We found 73 SNPs that differentiate Brazilian and US soybean germplasm. Maturity-associated SNPs showed high allelic frequency differences between Brazilian and US accessions. Other important loci were identified separating cultivars released before and after 1996 in Brazil. Our data showed important genomic regions under selection during decades of soybean breeding in Brazil and the US that should be targeted to adapt lines from different origins in these countries.

Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies

Soybean is one of the most valuable agricultural crops in the world. Besides, this legume is constantly attacked by a wide range of pathogens (fungi, bacteria, viruses, and nematodes) compromising yield and increasing production costs. One of the major disease management strategies is the genetic resistance provided by single genes and quantitative trait loci (QTL). Identifying the genomic regions underlying the resistance against these pathogens on soybean is one of the first steps performed by molecular breeders. In the past, genetic mapping studies have been widely used to discover these genomic regions. However, over the last decade, advances in next-generation sequencing technologies and their subsequent cost decreasing led to the development of cost-effective approaches to high-throughput genotyping. Thus, genome-wide association studies applying thousands of SNPs in large sets composed of diverse soybean accessions have been successfully done. In this chapter, a comprehensive review of the majority of GWAS for soybean diseases published since this approach was developed is provided. Important diseases caused by Heterodera glycines, Phytophthora sojae, and Sclerotinia sclerotiorum have been the focus of the several GWAS. However, other bacterial and fungi diseases also have been targets of GWAS. As such, this GWAS summary can serve as a guide for future studies of these diseases. The protocol begins by describing several considerations about the pathogens and bringing different procedures of molecular characterization of them. Advice to choose the best isolate/race to maximize the discovery of multiple R genes or to directly map an effective R gene is provided. A summary of protocols, methods, and tools to phenotyping the soybean panel is given to several diseases. We also give details of options of DNA extraction protocols and genotyping methods, and we describe parameters of SNP quality to soybean data. Websites and their online tools to obtain genotypic and phenotypic data for thousands of soybean accessions are highlighted. Finally, we report several tricks and tips in Subheading 4, especially related to composing the soybean panel as well as generating and analyzing the phenotype data. We hope this protocol will be helpful to achieve GWAS success in identifying resistance genes on soybean.

Progress in Soybean Genetic Transformation Over the Last Decade

Soybean is one of the important food, feed, and biofuel crops in the world. Soybean genome modification by genetic transformation has been carried out for trait improvement for more than 4 decades. However, compared to other major crops such as rice, soybean is still recalcitrant to genetic transformation, and transgenic soybean production has been hampered by limitations such as low transformation efficiency and genotype specificity, and prolonged and tedious protocols. The primary goal in soybean transformation over the last decade is to achieve high efficiency and genotype flexibility. Soybean transformation has been improved by modifying tissue culture conditions such as selection of explant types, adjustment of culture medium components and choice of selection reagents, as well as better understanding the transformation mechanisms of specific approaches such as Agrobacterium infection. Transgenesis-based breeding of soybean varieties with new traits is now possible by development of improved protocols. In this review, we summarize the developments in soybean genetic transformation to date, especially focusing on the progress made using Agrobacterium-mediated methods and biolistic methods over the past decade. We also discuss current challenges and future directions.

Genetic loci and causal genes for seed fatty acids accumulation across multiple environments and genetic backgrounds in soybean

Soybean is a major oil crop in the world, and fatty acids are the predominant components for oil bio-synthesis and catabolism metabolisms and also are the most important energy resources for organisms. In view of this, two recombinant inbred line (RIL) populations (ZL-RIL and ZQ-RIL) and one natural population were evaluated for five individual seed fatty acid contents (palmitic acid, stearic acid, oleic acid, linoleic acid, and linolenic acid) under four different environments, simultaneously. In total, sixteen additive QTL clusters were identified in ZL-RIL population, and fifteen were stably expressed across multiple environments or had pleiotropic effects on various fatty acid contents. Furthermore, five and five of these 16 QTL clusters were verified in ZQ-RIL population and natural population, respectively. Among these consistent and stable QTL clusters, one QTL cluster controlling fatty acid on chromosome 5 with pleiotropic effect was identified under all of the environments in ZL-RIL and ZQ-RIL populations and also was validated in the natural population. Meanwhile, another stable QTL cluster was detected on chromosome 9 with pleiotropic effect under multiple environments in ZL-RIL population and was further verified by the natural population. More importantly, some causal genes, such as the genes on chromosome 9, involving in the fatty acid catabolism process were found in these stable QTL clusters, and some of them, such as Gm09G042000, Gm09G041500, and Gm09G047200 on chromosome 9, showed different expressions in ZL-RIL parents (Zheng92116 and Liaodou14) based on the transcriptome sequencing analysis at different seed developmental stages. Thus, the study results provided insights into the genetic basis and molecular markers for regulating seed fatty acid contents in soybean breeding program.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01227-y.

The population genomics of adaptive loss of function

Discoveries of adaptive gene knockouts and widespread losses of complete genes have in recent years led to a major rethink of the early view that loss-of-function alleles are almost always deleterious. Today, surveys of population genomic diversity are revealing extensive loss-of-function and gene content variation, yet the adaptive significance of much of this variation remains unknown. Here we examine the evolutionary dynamics of adaptive loss of function through the lens of population genomics and consider the challenges and opportunities of studying adaptive loss-of-function alleles using population genetics models. We discuss how the theoretically expected existence of allelic heterogeneity, defined as multiple functionally analogous mutations at the same locus, has proven consistent with empirical evidence and why this impedes both the detection of selection and causal relationships with phenotypes. We then review technical progress towards new functionally explicit population genomic tools and genotype-phenotype methods to overcome these limitations. More broadly, we discuss how the challenges of studying adaptive loss of function highlight the value of classifying genomic variation in a way consistent with the functional concept of an allele from classical population genetics.

GCViT: a method for interactive, genome-wide visualization of resequencing and SNP array data

Background

Large genotyping datasets have become commonplace due to efficient, cheap methods for SNP identification. Typical genotyping datasets may have thousands to millions of data points per accession, across tens to thousands of accessions. There is a need for tools to help rapidly explore such datasets, to assess characteristics such as overall differences between accessions and regional anomalies across the genome.

Results

We present GCViT (Genotype Comparison Visualization Tool), for visualizing and exploring large genotyping datasets. GCViT can be used to identify introgressions, conserved or divergent genomic regions, pedigrees, and other features for more detailed exploration. The program can be used online or as a local instance for whole genome visualization of resequencing or SNP array data. The program performs comparisons of variants among user-selected accessions to identify allele differences and similarities between accessions and a user-selected reference, providing visualizations through histogram, heatmap, or haplotype views. The resulting analyses and images can be exported in various formats.

Conclusions

GCViT provides methods for interactively visualizing SNP data on a whole genome scale, and can produce publication-ready figures. It can be used in online or local installations. GCViT enables users to confirm or identify genomics regions of interest associated with particular traits.

GCViT is freely available at https://github.com/LegumeFederation/gcvit. The 1.0 version described here is available at 10.5281/zenodo.4008713.

Haplotype diversity underlying quantitative traits in Canadian soybean breeding germplasm

KEY MESSAGE

Identification of marker-trait associations and trait-associated haplotypes in breeding germplasm identifies regions under selection and highlights changes in haplotype diversity over decades of soybean improvement in Canada. Understanding marker-trait associations using genome-wide association in soybean is typically carried out in diverse germplasm groups where identified loci are often not applicable to soybean breeding efforts. To address this challenge, this study focuses on defining marker-trait associations in breeding germplasm and studying the underlying haplotypes in these regions to assess genetic change through decades of selection. Phenotype data were generated for 175 accessions across multiple environments in Ontario, Canada. A set of 76,549 SNPs were used in the association analysis. A total of 23 genomic regions were identified as significantly associated with yield (5), days to maturity (5), seed oil (3), seed protein (5) and 100-seed weight (5), of which 14 are novel. Each significant region was haplotyped to assess haplotype diversity of the underlying genomic region, identifying ten regions with trait-associated haplotypes in the breeding germplasm. The range of genomic length for these regions (7.2 kb to 6.8 Mb) indicates variation in regional LD for the trait-associated regions. Six of these regions showed changes between eras of breeding, from historical to modern and experimental soybean accessions. Continued selection on these regions may necessitate introgression of novel parental genetic diversity as some haplotypes were fixed within the breeding germplasm. This finding highlights the importance of studying associations and haplotype diversity at a breeding program scale to understand breeders' selections and trends in soybean improvement over time. The haplotypes may also be used as a tool for selection of parental germplasm to inform breeder's decisions on further soybean improvement.

Time for a paradigm shift in the use of plant genetic resources

For all major crops, sizeable genebanks are maintained across the world and serve as repositories of genetic diversity and key sources of novel traits used in breeding. Although molecular markers have been used to characterize diversity in a broad sense, the most common approach to exploring these resources has been through phenotypic characterization of subsets of these large collections. With the advent of affordable large-scale genotyping technologies and the increasing body of candidate genes for traits of interest, we argue here that it is time for a paradigm shift in the way that we explore and exploit these considerable and highly useful resources. By combining dense genotypic information in and around candidate genes, it is possible to classify accessions based on their haplotype, something approximating the actual alleles at these genes of interest.

Genome-wide genetic diversity is maintained through decades of soybean breeding in Canada

KEY MESSAGE

Genetic diversity in Canadian soybean is maintained over decades of selection in two public breeding programs. Breeders have used a portion of the genetic diversity available in germplasm collections. Both public and private breeding efforts have been critical for the development of soybean cultivars grown around the world. Global genetic diversity of soybean has been well characterized; however, this diversity is not well studied at the breeding program scale. The objective of this study was to characterize genetic diversity over decades of breeding in two public soybean breeding programs at the University of Guelph, Canada. To address this objective, a pedigree-related panel combining 296 soybean accessions from the Ridgetown and Guelph Campus breeding programs was studied. The accessions were genotyped using genotyping-by-sequencing, imputed using the GmHapMap reference genotypes resulting in more than 3.8M SNPs, further filtered to 77k SNPs. Population structure analysis did not identify structure between the breeding programs and historical germplasm. The linkage disequilibrium decay ranged from 400 to 600 kb on average in euchromatic regions. Nucleotide diversity over decades of breeding shows that historical accessions had the highest nucleotide diversity, with significant decreases corresponding to the initial breeding activity in Canada; however, genetic diversity has increased in the last 20 years in both breeding programs. Maturity gene E2 was nearly fixed for e2 in Ridgetown accessions, while unfixed in Guelph accessions. Comparison of the breeding programs to the USDA germplasm collection reveals that breeders have only used a portion of the available genetic diversity, allowing future breeders to exploit this untapped resource. The approach used in this study may be of interest to other breeding programs for evaluating changes in genetic diversity resulting from breeding activities.

A Systematic Gene-Centric Approach to Define Haplotypes and Identify Alleles on the Basis of Dense Single Nucleotide Polymorphism Datasets

A gene-centric approach for haplotype definition was developed and implemented in R. The tool allows for allelic characterization at given loci in germplasm collections. Allelic status at four maturity genes is predicted on the basis of marker genotyping data. Assessing the allelic diversity within a germplasm collection and identifying individuals carrying favorable alleles is challenging. Advances in high-throughput technologies allow the genotyping of many individuals for thousands of markers but bridging the gap between single nucleotide polymorphisms (SNPs) and relevant alleles remains difficult. We developed a systematic approach that defines haplotypes from large SNP catalogs that aims to identify haplotypes that can be equated to alleles at given genes. Unlike haplotype visualization tools, our approach selects SNP markers that flank a gene and define haplotypes that correspond to this gene's alleles. We tested this approach on four known soybean [Glycine max (L.) Merr.] maturity genes (E1, GmGia, GmPhyA3, and GmPhyA2) in a collection of 67 lines and two genotypic datasets [a SNP array and genotyping-by-sequencing (GBS)]. For E1, GmGia, and GmPhyA3, we identified SNP haplotypes such that the allele found at these genes could be accurately predicted from the haplotype in 97.3% of the cases. For these genes, of the 12 known alleles in the collection, 10 and 8 could be correctly predicted from the haplotypes found with the SNP array and GBS datasets, with success rates of 98 and 97% for all allele-line combinations, respectively. The approach proved equally successful for data derived from a SNP array and GBS. However, in the case of GmPhyA2, a lack of markers in the genomic region prevented the identification of alleles, regardless of the dataset. We demonstrate the feasibility and reproducibility of our approach and identify limits to its applicability.