Loss-of-Function Mutation of Soybean R2R3 MYB Transcription Factor Dilutes Tawny Pubescence Color

Natural and artificial selection of multiple alleles revealed through genomic analyses

Genome-to-phenome research in agriculture aims to improve crops through in silico predictions. Genome-wide association study (GWAS) is potent in identifying genomic loci that underlie important traits. As a statistical method, increasing the sample quantity, data quality, or diversity of the GWAS dataset positively impacts GWAS power. For more precise breeding, concrete candidate genes with exact functional variants must be discovered. Many post-GWAS methods have been developed to narrow down the associated genomic regions and, ideally, to predict candidate genes and causative mutations (CMs). Historical natural selection and breeding-related artificial selection both act to change the frequencies of different alleles of genes that control phenotypes. With higher diversity and more extensive GWAS datasets, there is an increased chance of multiple alleles with independent CMs in a single causal gene. This can be caused by the presence of samples from geographically isolated regions that arose during natural or artificial selection. This simple fact is a complicating factor in GWAS-driven discoveries. Currently, none of the existing association methods address this issue and need to identify multiple alleles and, more specifically, the actual CMs. Therefore, we developed a tool that computes a score for a combination of variant positions in a single candidate gene and, based on the highest score, identifies the best number and combination of CMs. The tool is publicly available as a Python package on GitHub, and we further created a web-based Multiple Alleles discovery (MADis) tool that supports soybean and is hosted in SoyKB (https://soykb.org/SoybeanMADisTool/). We tested and validated the algorithm and presented the utilization of MADis in a pod pigmentation L1 gene case study with multiple CMs from natural or artificial selection. Finally, we identified a candidate gene for the pod color L2 locus and predicted the existence of multiple alleles that potentially cause loss of pod pigmentation. In this work, we show how a genomic analysis can be employed to explore the natural and artificial selection of multiple alleles and, thus, improve and accelerate crop breeding in agriculture.

k-mer-based GWAS enhances the discovery of causal variants and candidate genes in soybean

Genome-wide association studies (GWAS) are powerful statistical methods that detect associations between genotype and phenotype at genome scale. Despite their power, GWAS frequently fail to pinpoint the causal variant or the gene controlling a given trait in crop species. Assessing genetic variants other than single-nucleotide polymorphisms (SNPs) could alleviate this problem. In this study, we tested the potential of structural variant (SV)- and k-mer-based GWAS in soybean by applying these methods as well as conventional SNP/indel-based GWAS to 13 traits. We assessed the performance of each GWAS approach based on loci for which the causal genes or variants were known from previous genetic studies. We found that k-mer-based GWAS was the most versatile approach and the best at pinpointing causal variants or candidate genes. Moreover, k-mer-based analyses identified promising candidate genes for loci related to pod color, pubescence form, and resistance to Phytophthora sojae. In our dataset, SV-based GWAS did not add value compared to k-mer-based GWAS and may not be worth the time and computational resources invested. Despite promising results, significant challenges remain regarding the downstream analysis of k-mer-based GWAS. Notably, better methods are needed to associate significant k-mers with sequence variation. Our results suggest that coupling k-mer- and SNP/indel-based GWAS is a powerful approach for discovering candidate genes in crop species.

Function of the R2R3-MYB Transcription Factors in Dalbergia odorifera and Their Relationship with Heartwood Formation

R2R3-MYB transcription factors (TFs) form one of the most important TF families involved in regulating various physiological functions in plants. The heartwood of Dalbergia odorifera is a kind of high-grade mahogany and valuable herbal medicine with wide application. However, the role of R2R3-MYB genes in the growth and development of D. odorifera, especially their relevance to heartwood formation, has not been revealed. A total of 126 R2R3-MYBs were screened from the D. odorifera genome and named DodMYB1-126 based on their location on 10 chromosomes. The collinearity results showed that purification selection was the main driving force for the evolution of the R2R3-MYB TFs family, and whole genome/fragment replication event was the main form for expanding the R2R3-MYB family, generating a divergence of gene structure and function. Comparative phylogenetic analysis classified the R2R3-MYB TFs into 33 subfamilies. S3-7,10,12-13,21 and N4-7 were extensively involved in the metabolic process; S9,13,16-19,24-25 and N1-3,8 were associated with the growth and development of D. odorifera. Based on the differential transcriptional expression levels of R2R3-MYBs in different tissues, DodMYB32, DodMYB55, and DodMYB89 were tentatively screened for involvement in the regulatory process of heartwood. Further studies have shown that the DodMYB89, localized in the nucleus, has transcriptional activation activity and is involved in regulating the biosynthesis of the secondary metabolites of heartwood by activating the promoters of the structural genes DodI2'H and DodCOMT. This study aimed to comprehensively analyze the functions of the R2R3-MYB TFs and screen for candidate genes that might be involved in heartwood formation of D. odorifera.

k-mer-Based Genome-Wide Association Studies in Plants: Advances, Challenges, and Perspectives

Genome-wide association studies (GWAS) have allowed the discovery of marker-trait associations in crops over recent decades. However, their power is hampered by a number of limitations, with the key one among them being an overreliance on single-nucleotide polymorphisms (SNPs) as molecular markers. Indeed, SNPs represent only one type of genetic variation and are usually derived from alignment to a single genome assembly that may be poorly representative of the population under study. To overcome this, k-mer-based GWAS approaches have recently been developed. k-mer-based GWAS provide a universal way to assess variation due to SNPs, insertions/deletions, and structural variations without having to specifically detect and genotype these variants. In addition, k-mer-based analyses can be used in species that lack a reference genome. However, the use of k-mers for GWAS presents challenges such as data size and complexity, lack of standard tools, and potential detection of false associations. Nevertheless, efforts are being made to overcome these challenges and a general analysis workflow has started to emerge. We identify the priorities for k-mer-based GWAS in years to come, notably in the development of user-friendly programs for their analysis and approaches for linking significant k-mers to sequence variation.

A genome-wide analysis of the USDA Soybean Isoline Collection

The USDA Soybean Isoline Collection has been an invaluable resource for the soybean genetics and breeding community. This collection, established in 1972, consists of 611 near-isogenic lines (NILs) carrying one or multiple genes conferring traits that had been determined to exhibit Mendelian inheritance. It has been used in multiple studies on the genetic basis, physiology, and agronomy of these qualitative traits. Here, we used publicly available genotype (SoySNP50K), phenotype, and pedigree data on this collection to characterize the isogenicity of the NILs and identify chromosomal positions of unmapped genes. A total of 368 NILs had at least 80% identity to their recurrent parent and, thus, were useful for what can be called introgression mapping. Both on-target and off-target introgressions were evaluated. The size of on-target introgressions into individual NILs ranged from 61 kb to 8.4 Mb, whereas off-target introgressions ranged from 2.6 kb to 54.8 Mb. The observed large off-target introgressions indicated that some NILs carry introgressions nearly the size of an entire chromosome. By applying introgression mapping to genes that had never been mapped, we identified the likely chromosomal positions of six such genes: ab, im, lo, Np, pc, and Rpm. The size of mapping intervals was large in some cases (10.28 Mb for im) but small in others (0.21 Mb for Np). The results reported herein will provide future researchers with a resource to help select informative NILs for future studies, and provide a starting point to further fine map, and ultimately clone and functionally characterize these six soybean genes.

Branch angle and leaflet shape are associated with canopy coverage in soybean

Early canopy coverage is a desirable trait that is a major determinant of yield in soybean (Glycine max). Variation in traits comprising shoot architecture can influence canopy coverage, canopy light interception, canopy-level photosynthesis, and source-sink partitioning efficiency. However, little is known about the extent of phenotypic diversity of shoot architecture traits and their genetic control in soybean. Thus, we sought to understand the contribution of shoot architecture traits to canopy coverage and to determine the genetic control of these traits. We examined the natural variation for shoot architecture traits in a set of 399 diverse maturity group I soybean (SoyMGI) accessions to identify relationships between traits, and to identify loci that are associated with canopy coverage and shoot architecture traits. Canopy coverage was correlated with branch angle, number of branches, plant height, and leaf shape. Using previously collected 50K single nucleotide polymorphism data, we identified quantitative trait locus (QTL) associated with branch angle, number of branches, branch density, leaflet shape, days to flowering, maturity, plant height, number of nodes, and stem termination. In many cases, QTL intervals overlapped with previously described genes or QTL. We also found QTL associated with branch angle and leaflet shape located on chromosomes 19 and 4, respectively, and these QTL overlapped with QTL associated with canopy coverage, suggesting the importance of branch angle and leaflet shape in determining canopy coverage. Our results highlight the role individual architecture traits play in canopy coverage and contribute information on their genetic control that could help facilitate future efforts in their genetic manipulation.

A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes

INTRODUCTION

Genome-Wide Association Studies (GWAS) identify tagging variants in the genome that are statistically associated with the phenotype because of their linkage disequilibrium (LD) relationship with the causative mutation (CM). When both low-density genotyped accession panels with phenotypes and resequenced data accession panels are available, tagging variants can assist with post-GWAS challenges in CM discovery.

OBJECTIVES

Our objective was to identify additional GWAS evaluation criteria to assess correspondence between genomic variants and phenotypes, as well as enable deeper analysis of the localized landscape of association.

METHODS

We used genomic variant positions as Synthetic phenotypes in GWAS that we named "Synthetic phenotype association study" (SPAS). The extreme case of SPAS is what we call an "Inverse GWAS" where we used CM positions of cloned soybean genes. We developed and validated the Accuracy concept as a measure of the correspondence between variant positions and phenotypes.

RESULTS

The SPAS approach demonstrated that the genotype status of an associated variant used as a Synthetic phenotype enabled us to explore the relationships between tagging variants and CMs, and further, that utilizing CMs as Synthetic phenotypes in Inverse GWAS illuminated the landscape of association. We implemented the Accuracy calculation for a curated accession panel to an online Accuracy calculation tool (AccuTool) as a resource for gene identification in soybean. We demonstrated our concepts on three examples of soybean cloned genes. As a result of our findings, we devised an enhanced "GWAS to Genes" analysis (Synthetic phenotype to CM strategy, SP2CM). Using SP2CM, we identified a CM for a novel gene.

CONCLUSION

The SP2CM strategy utilizing Synthetic phenotypes and the Accuracy calculation of correspondence provides crucial information to assist researchers in CM discovery. The impact of this work is a more effective evaluation of landscapes of GWAS associations.

Classification of Soybean Pubescence from Multispectral Aerial Imagery

The accurate determination of soybean pubescence is essential for plant breeding programs and cultivar registration. Currently, soybean pubescence is classified visually, which is a labor-intensive and time-consuming activity. Additionally, the three classes of phenotypes (tawny, light tawny, and gray) may be difficult to visually distinguish, especially the light tawny class where misclassification with tawny frequently occurs. The objectives of this study were to solve both the throughput and accuracy issues in the plant breeding workflow, develop a set of indices for distinguishing pubescence classes, and test a machine learning (ML) classification approach. A principal component analysis (PCA) on hyperspectral soybean plot data identified clusters related to pubescence classes, while a Jeffries-Matusita distance analysis indicated that all bands were important for pubescence class separability. Aerial images from 2018, 2019, and 2020 were analyzed in this study. A 60-plot test (2019) of genotypes with known pubescence was used as reference data, while whole-field images from 2018, 2019, and 2020 were used to examine the broad applicability of the classification methodology. Two indices, a red/blue ratio and blue normalized difference vegetation index (blue NDVI), were effective at differentiating tawny and gray pubescence types in high-resolution imagery. A ML approach using a support vector machine (SVM) radial basis function (RBF) classifier was able to differentiate the gray and tawny types (83.1% accuracy and kappa = 0.740 on a pixel basis) on images where reference training data was present. The tested indices and ML model did not generalize across years to imagery that did not contain the reference training panel, indicating limitations of using aerial imagery for pubescence classification in some environmental conditions. High-throughput classification of gray and tawny pubescence types is possible using aerial imagery, but light tawny soybeans remain difficult to classify and may require training data from each field season.