Genetic Dissection of Quantitative Resistance to Common Rust (Puccinia sorghi) in Tropical Maize (Zea mays L.) by Combined Genome-Wide Association Study, Linkage Mapping, and Genomic Prediction

Genome-wide association analysis and genomic selection for leaf-related traits of maize

Maize is an important food crop worldwide. The length, width, and area of leaves are crucial traits of plant architecture and further influencing plant density, photosynthesis, and crop yield. To dissect the genetic architecture of leaf length, leaf width, and leaf area, a multi-parents doubled haploid (DH) population was used for genome-wide association study (GWAS) and genomic selection (GS). The length, width, and area of the first leaf above the uppermost ear, the uppermost ear leaf, and the first leaf below the uppermost ear were evaluated in multi-environment trials. Using BLINK and FarmCPU for GWAS, 19 significant single nucleotide polymorphisms (SNPs) on chromosomes 1, 2, 5, 6, 8, 9, and 10 were associated with leaf length, 49 SNPs distributed over all 10 chromosomes were associated with leaf width, and 37 SNPs distributed on all 10 chromosomes except for chromosome 3 were associated with leaf area. The phenotypic variation explained (PVE) by each QTL ranged from 0.05% to 27.46%. Fourteen pleiotropic SNPs were detected by at least two leaf-related traits. A total of 57 candidate genes were identified for leaf-related traits, of which 44 were annotated with known functions. Candidate genes Zm00001d032866, Zm00001D022209, and Zm00001d001980 are involved in leaf senescence. Zm00001d026130, Zm00001d002429, Zm00001d023225, and Zm00001d046767 play important roles in leaf development. GS analysis showed that when 60% of the total genotypes was used as the training population and 3000 SNPs were used for prediction, moderate prediction accuracy was obtained for leaf length, leaf width, and leaf area. The prediction accuracy would be improved by using top significantly associated SNPs for GS. The current study provides a better understanding of the genetic basis of leaf length, leaf width, and leaf area, and valuable information for improving plant architecture by implementing GS.

Integration of GWAS models and GS reveals the genetic architecture of ear shank in maize

Maize is one of the most important crops for human food, animal feed, and industrial raw materials. Ear shank length (ESL) and ear shank node number (ESNN) are crucial selection criteria in maize breeding, impacting grain yield and dehydration rate during mechanical harvesting. To unravel the genetic basis of ESL and ESNN in maize, an association panel consisting of 379 multi-parent doubled-haploid (DH) lines was developed for genome-wide association studies (GWAS) and genomic selection (GS). The heritabilities of ESL and ESNN were 0.68 and 0.55, respectively, which were controlled by genetic factors and genotype-environment interaction factors. Using five different models for GWAS, 11 significant single nucleotide polymorphisms (SNPs) located on chromosomes 1, 2, and 4 were identified for ESL, with the phenotypic variation explained (PVE) value of each single SNP ranging from 4.91% to 21.35%, and 11 significant SNPs located on chromosomes 1, 2, 4, and 5 were identified for ESNN, with the PVE value of each SNP ranging from 1.22% to 18.42%. Genetic regions in bins 1.06, 2.06, and 2.08 were significantly enriched in SNPs associated with ear shank-related traits. The GS prediction accuracy using all markers by the five-fold cross-validation method for ESL and ESNN was 0.39 and 0.37, respectively, which was significantly improved by using only 500-1000 significant SNPs with the lowest P-values. The optimal training population size (TPS) and marker density (MD) for ear shank-related traits were 50%-60% and 3000, respectively. Our results provide new insights into the GS of ear shank-related traits.

Genome-wide association study and genomic selection of brace root traits related to lodging resistance in maize

Brace roots are the primary organs for water and nutrient absorption, and play an important role in lodging resistance. Dissecting the genetic basis of brace root traits will facilitate breeding new varieties with lodging resistance and high yield. In present study, genome-wide association study (GWAS) and genomic selection (GS) for brace root penetrometer resistance (PR), root number (RN), and tier number (TN) were conducted in a multi-parent doubled haploid (DH) population. Although the three brace root traits were significantly influenced by genotype-by-environment interactions, relatively high heritabilities were observed for RN (79.25%), TN (81.00%), and PR (74.28%). At the threshold of P-value < 7.42 × 10- 6, 22 SNPs distributed on all 10 chromosomes, except for chromosome 7, and 50 candidate genes were identified using the FarmCPU model. Zm00001d028733, Zm00001d002350, Zm00001d002349, and Zm00001d043694 may be involved in brace root growth. The prediction accuracies of RN, TN, and PR estimated by five-fold cross-validation method with genome-wide SNPs were 0.39, 0.36, and 0.51, respectively, which can be greatly improved by using 100-300 significant SNPs. This study provides new insights into the genetic architecture of brace root traits and genomic selection for breeding lodging resistance maize varieties.

Dual-Model GWAS Analysis and Genomic Selection of Maize Flowering Time-Related Traits

An appropriate flowering period is an important selection criterion in maize breeding. It plays a crucial role in the ecological adaptability of maize varieties. To explore the genetic basis of flowering time, GWAS and GS analyses were conducted using an associating panel consisting of 379 multi-parent DH lines. The DH population was phenotyped for days to tasseling (DTT), days to pollen-shedding (DTP), and days to silking (DTS) in different environments. The heritability was 82.75%, 86.09%, and 85.26% for DTT, DTP, and DTS, respectively. The GWAS analysis with the FarmCPU model identified 10 single-nucleotide polymorphisms (SNPs) distributed on chromosomes 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. The GWAS analysis with the BLINK model identified seven SNPs distributed on chromosomes 1, 3, 8, 9, and 10 that were significantly associated with flowering time-related traits. Three SNPs 3_198946071, 9_146646966, and 9_152140631 showed a pleiotropic effect, indicating a significant genetic correlation between DTT, DTP, and DTS. A total of 24 candidate genes were detected. A relatively high prediction accuracy was achieved with 100 significantly associated SNPs detected from GWAS, and the optimal training population size was 70%. This study provides a better understanding of the genetic architecture of flowering time-related traits and provides an optimal strategy for GS.

Dissection of Common Rust Resistance in Tropical Maize Multiparent Population through GWAS and Linkage Studies

Common rust (CR), caused by Puccina sorghi, is a major foliar disease in maize that leads to quality deterioration and yield losses. To dissect the genetic architecture of CR resistance in maize, this study utilized the susceptible temperate inbred line Ye107 as the male parent crossed with three resistant tropical maize inbred lines (CML312, D39, and Y32) to generate 627 F7 recombinant inbred lines (RILs), with the aim of identifying maize disease-resistant loci and candidate genes for common rust. Phenotypic data showed good segregation between resistance and susceptibility, with varying degrees of resistance observed across different subpopulations. Significant genotype effects and genotype × environment interactions were observed, with heritability ranging from 85.7% to 92.2%. Linkage and genome-wide association analyses across the three environments identified 20 QTLs and 62 significant SNPs. Among these, seven major QTLs explained 66% of the phenotypic variance. Comparison with six SNPs repeatedly identified across different environments revealed overlap between qRUST3-3 and Snp-203,116,453, and Snp-204,202,469. Haplotype analysis indicated two different haplotypes for CR resistance for both the SNPs. Based on LD decay plots, three co-located candidate genes, Zm00001d043536, Zm00001d043566, and Zm00001d043569, were identified within 20 kb upstream and downstream of these two SNPs. Zm00001d043536 regulates hormone regulation, Zm00001d043566 controls stomatal opening and closure, related to trichome, and Zm00001d043569 is associated with plant disease immune responses. Additionally, we performed candidate gene screening for five additional SNPs that were repeatedly detected across different environments, resulting in the identification of five candidate genes. These findings contribute to the development of genetic resources for common rust resistance in maize breeding programs.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher - log p values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.

Utilizing Two Populations Derived from Tropical Maize for Genome-Wide Association Analysis of Banded Leaf and Sheath Blight Resistance

Banded leaf and sheath blight (BLSB) in maize is a soil-borne fungal disease caused by Rhizoctonia solani Kühn, resulting in significant yield losses. Investigating the genes responsible for regulating resistance to BLSB is crucial for yield enhancement. In this study, a multiparent maize population was developed, comprising two recombinant inbred line (RIL) populations totaling 442 F8RILs. The populations were generated by crossing two tropical inbred lines, CML444 and NK40-1, known for their BLSB resistance, as female parents, with the high-yielding but BLSB-susceptible inbred line Ye107 serving as the common male parent. Subsequently, we utilized 562,212 high-quality single nucleotide polymorphisms (SNPs) generated through genotyping-by-sequencing (GBS) for a comprehensive genome-wide association study (GWAS) aimed at identifying genes responsible for BLSB resistance. The objectives of this study were to (1) identify SNPs associated with BLSB resistance through genome-wide association analyses, (2) explore candidate genes regulating BLSB resistance in maize, and (3) investigate pathways involved in BLSB resistance and discover key candidate genes through Gene Ontology (GO) analysis. The GWAS analysis revealed nineteen SNPs significantly associated with BLSB that were consistently identified across four environments in the GWAS, with phenotypic variation explained (PVE) ranging from 2.48% to 11.71%. Screening a 40 kb region upstream and downstream of the significant SNPs revealed several potential candidate genes. By integrating information from maize GDB and the NCBI, we identified five novel candidate genes, namely, Zm00001d009723, Zm00001d009975, Zm00001d009566, Zm00001d009567, located on chromosome 8, and Zm00001d026376, on chromosome 10, related to BLSB resistance. These candidate genes exhibit association with various aspects, including maize cell membrane proteins and cell immune proteins, as well as connections to cell metabolism, transport, transcriptional regulation, and structural proteins. These proteins and biochemical processes play crucial roles in maize defense against BLSB. When Rhizoctonia solani invades maize plants, it induces the expression of genes encoding specific proteins and regulates corresponding metabolic pathways to thwart the invasion of this fungus. The present study significantly contributes to our understanding of the genetic basis of BLSB resistance in maize, offering valuable insights into novel candidate genes that could be instrumental in future breeding efforts to develop maize varieties with enhanced BLSB resistance.

Genome-wide association study presents insights into the genetic architecture of drought tolerance in maize seedlings under field water-deficit conditions

Introduction

Drought stress is one of the most serious abiotic stresses leading to crop yield reduction. Due to the wide range of planting areas, the production of maize is particularly affected by global drought stress. The cultivation of drought-resistant maize varieties can achieve relatively high, stable yield in arid and semi-arid zones and in the erratic rainfall or occasional drought areas. Therefore, to a great degree, the adverse impact of drought on maize yield can be mitigated by developing drought-resistant or -tolerant varieties. However, the efficacy of traditional breeding solely relying on phenotypic selection is not adequate for the need of maize drought-resistant varieties. Revealing the genetic basis enables to guide the genetic improvement of maize drought tolerance.

Methods

We utilized a maize association panel of 379 inbred lines with tropical, subtropical and temperate backgrounds to analyze the genetic structure of maize drought tolerance at seedling stage. We obtained the high quality 7837 SNPs from DArT's and 91,003 SNPs from GBS, and a resultant combination of 97,862 SNPs of GBS with DArT's. The maize population presented the lower her-itabilities of the seedling emergence rate (ER), seedling plant height (SPH) and grain yield (GY) under field drought conditions.

Results

GWAS analysis by MLM and BLINK models with the phenotypic data and 97862 SNPs revealed 15 variants that were significantly independent related to drought-resistant traits at the seedling stage above the threshold of P < 1.02 × 10-5. We found 15 candidate genes for drought resistance at the seedling stage that may involve in (1) metabolism (Zm00001d012176, Zm00001d012101, Zm00001d009488); (2) programmed cell death (Zm00001d053952); (3) transcriptional regulation (Zm00001d037771, Zm00001d053859, Zm00001d031861, Zm00001d038930, Zm00001d049400, Zm00001d045128 and Zm00001d043036); (4) autophagy (Zm00001d028417); and (5) cell growth and development (Zm00001d017495). The most of them in B73 maize line were shown to change the expression pattern in response to drought stress. These results provide useful information for understanding the genetic basis of drought stress tolerance of maize at seedling stage.

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

Genome-Wide Association Study and Genomic Prediction on Plant Architecture Traits in Sweet Corn and Waxy Corn

Sweet corn and waxy corn has a better taste and higher accumulated nutritional value than regular maize, and is widely planted and popularly consumed throughout the world. Plant height (PH), ear height (EH), and tassel branch number (TBN) are key plant architecture traits, which play an important role in improving grain yield in maize. In this study, a genome-wide association study (GWAS) and genomic prediction analysis were conducted on plant architecture traits of PH, EH, and TBN in a fresh edible maize population consisting of 190 sweet corn inbred lines and 287 waxy corn inbred lines. Phenotypic data from two locations showed high heritability for all three traits, with significant differences observed between sweet corn and waxy corn for both PH and EH. The differences between the three subgroups of sweet corn were not obvious for all three traits. Population structure and PCA analysis results divided the whole population into three subgroups, i.e., sweet corn, waxy corn, and the subgroup mixed with sweet and waxy corn. Analysis of GWAS was conducted with 278,592 SNPs obtained from resequencing data; 184, 45, and 68 significantly associated SNPs were detected for PH, EH, and TBN, respectively. The phenotypic variance explained (PVE) values of these significant SNPs ranged from 3.50% to 7.0%. The results of this study lay the foundation for further understanding the genetic basis of plant architecture traits in sweet corn and waxy corn. Genomic selection (GS) is a new approach for improving quantitative traits in large plant breeding populations that uses whole-genome molecular markers. The marker number and marker quality are essential for the application of GS in maize breeding. GWAS can choose the most related markers with the traits, so it can be used to improve the predictive accuracy of GS.

Climate change conditions the selection of rust-resistant candidate wild lentil populations for in situ conservation

Crop Wild Relatives (CWR) are a valuable source of genetic diversity that can be transferred to commercial crops, so their conservation will become a priority in the face of climate change. Bizarrely, in situ conserved CWR populations and the traits one might wish to preserve in them are themselves vulnerable to climate change. In this study, we used a quantitative machine learning predictive approach to project the resistance of CWR populations of lentils to a common disease, lentil rust, caused by fungus Uromyces viciae-fabae. Resistance is measured through a proxy quantitative value, DSr (Disease Severity relative), quite complex and expensive to get. Therefore, machine learning is a convenient tool to predict this magnitude using a well-curated georeferenced calibration set. Previous works have provided a binary outcome (resistant vs. non-resistant), but that approach is not fine enough to answer three practical questions: which variables are key to predict rust resistance, which CWR populations are resistant to rust under current environmental conditions, and which of them are likely to keep this trait under different climate change scenarios. We first predict rust resistance in present time for crop wild relatives that grow up inside protected areas. Then, we use the same models under future climate IPCC (Intergovernmental Panel on Climate Change) scenarios to predict future DSr values. Populations that are rust-resistant by now and under future conditions are optimal candidates for further evaluation and in situ conservation of this valuable trait. We have found that rust-resistance variation as a result of climate change is not uniform across the geographic scope of the study (the Mediterranean basin), and that candidate populations share some interesting common environmental conditions.

Identification and fine mapping of a major QTL (qRtsc8-1) conferring resistance to maize tar spot complex and validation of production markers in breeding lines

A major QTL of qRtsc8-1 conferring TSC resistance was identified and fine mapped to a 721 kb region on chromosome 8 at 81 Mb, and production markers were validated in breeding lines. Tar spot complex (TSC) is a major foliar disease of maize in many Central and Latin American countries and leads to severe yield loss. To dissect the genetic architecture of TSC resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid population were used for GWAS and selective genotyping analysis, respectively. A total of 115 SNPs in bin 8.03 were detected by GWAS and three QTL in bins 6.05, 6.07, and 8.03 were detected by selective genotyping. The major QTL qRtsc8-1 located in bin 8.03 was detected by both analyses, and it explained 14.97% of the phenotypic variance. To fine map qRtsc8-1, the recombinant-derived progeny test was implemented. Recombinations in each generation were backcrossed, and the backcross progenies were genotyped with Kompetitive Allele Specific PCR (KASP) markers and phenotyped for TSC resistance individually. The significant tests for comparing the TSC resistance between the two classes of progenies with and without resistant alleles were used for fine mapping. In BC₅ generation, qRtsc8-1 was fine mapped in an interval of  ~ 721 kb flanked by markers of KASP81160138 and KASP81881276. In this interval, the candidate genes GRMZM2G063511 and GRMZM2G073884 were identified, which encode an integral membrane protein-like and a leucine-rich repeat receptor-like protein kinase, respectively. Both genes are involved in maize disease resistance responses. Two production markers KASP81160138 and KASP81160155 were verified in 471 breeding lines. This study provides valuable information for cloning the resistance gene, and it will also facilitate the routine implementation of marker-assisted selection in the breeding pipeline for improving TSC resistance.

Genome-Wide Profiling of Alternative Splicing and Gene Fusion during Rice Black-Streaked Dwarf Virus Stress in Maize (Zea mays L.)

Rice black-streaked dwarf virus (RBSDV) causes maize rough dwarf disease (MRDD), which is a viral disease that significantly affects maize yields worldwide. Plants tolerate stress through transcriptional reprogramming at the alternative splicing (AS), transcriptional, and fusion gene (FG) levels. However, it is unclear whether and how AS and FG interfere with transcriptional reprogramming in MRDD. In this study, we performed global profiling of AS and FG on maize response to RBSDV and compared it with transcriptional changes. There are approximately 1.43 to 2.25 AS events per gene in maize infected with RBSDV. GRMZM2G438622 was only detected in four AS modes (A3SS, A5SS, RI, and SE), whereas GRMZM2G059392 showed downregulated expression and four AS events. A total of 106 and 176 FGs were detected at two time points, respectively, including six differentially expressed genes and five differentially spliced genes. The gene GRMZM2G076798 was the only FG that occurred at two time points and was involved in two FG events. Among these, 104 GOs were enriched, indicating that nodulin-, disease resistance-, and chloroplastic-related genes respond to RBSDV stress in maize. These results provide new insights into the mechanisms underlying post-transcriptional and transcriptional regulation of maize response to RBSDV stress.