2018-2019 field seasons of the Maize Genomes to Fields (G2F) G x E project

OBJECTIVES

This report provides information about the public release of the 2018-2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions.

DATA DESCRIPTION

Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project's inception.

Hybrid allele-specific ChIP-seq analysis identifies variation in brassinosteroid-responsive transcription factor binding linked to traits in maize

BACKGROUND

Genetic variation in regulatory sequences that alter transcription factor (TF) binding is a major cause of phenotypic diversity. Brassinosteroid is a growth hormone that has major effects on plant phenotypes. Genetic variation in brassinosteroid-responsive cis-elements likely contributes to trait variation. Pinpointing such regulatory variations and quantitative genomic analysis of the variation in TF-target binding, however, remains challenging. How variation in transcriptional targets of signaling pathways such as the brassinosteroid pathway contributes to phenotypic variation is an important question to be investigated with innovative approaches.

RESULTS

Here, we use a hybrid allele-specific chromatin binding sequencing (HASCh-seq) approach and identify variations in target binding of the brassinosteroid-responsive TF ZmBZR1 in maize. HASCh-seq in the B73xMo17 F1s identifies thousands of target genes of ZmBZR1. Allele-specific ZmBZR1 binding (ASB) has been observed for 18.3% of target genes and is enriched in promoter and enhancer regions. About a quarter of the ASB sites correlate with sequence variation in BZR1-binding motifs and another quarter correlate with haplotype-specific DNA methylation, suggesting that both genetic and epigenetic variations contribute to the high level of variation in ZmBZR1 occupancy. Comparison with GWAS data shows linkage of hundreds of ASB loci to important yield and disease-related traits.

CONCLUSION

Our study provides a robust method for analyzing genome-wide variations of TF occupancy and identifies genetic and epigenetic variations of the brassinosteroid response transcription network in maize.

Yield prediction through integration of genetic, environment, and management data through deep learning

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied toward this goal. Here we predict maize yield using deep neural networks, compare the efficacy of 2 model development methods, and contextualize model performance using conventional linear and machine learning models. We examine the usefulness of incorporating interactions between disparate data types. We find deep learning and best linear unbiased predictor (BLUP) models with interactions had the best overall performance. BLUP models achieved the lowest average error, but deep learning models performed more consistently with similar average error. Optimizing deep neural network submodules for each data type improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best-performing model revealed that including interactions altered the model's sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have a limited physiological basis for influencing yield-those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for the phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

Brown midrib mutant and genome-wide association analysis uncover lignin genes for disease resistance in maize

Brown midrib (BMR) maize (Zea mays L.) harbors mutations that result in lower lignin levels and higher feed digestibility, making it a desirable silage market class for ruminant nutrition. Northern leaf blight (NLB) epidemics in upstate New York highlighted the disease susceptibility of commercially grown BMR maize hybrids. We found the bm1, bm2, bm3, and bm4 mutants in a W64A genetic background to be more susceptible to foliar fungal (NLB, gray leaf spot [GLS], and anthracnose leaf blight [ALB]) and bacterial (Stewart's wilt) diseases. The bm1, bm2, and bm3 mutants showed enhanced susceptibility to anthracnose stalk rot (ASR), and the bm1 and bm3 mutants were more susceptible to Gibberella ear rot (GER). Colocalization of quantitative trait loci (QTL) and correlations between stalk strength and disease traits in recombinant inbred line families suggest possible pleiotropies. The role of lignin in plant defense was explored using high-resolution, genome-wide association analysis for resistance to NLB in the Goodman diversity panel. Association analysis identified 100 single and clustered single-nucleotide polymorphism (SNP) associations for resistance to NLB but did not implicate natural functional variation at bm1-bm5. Strong associations implicated a suite of diverse candidate genes including lignin-related genes such as a β-glucosidase gene cluster, hct11, knox1, knox2, zim36, lbd35, CASP-like protein 8, and xat3. The candidate genes are targets for breeding quantitative resistance to NLB in maize for use in silage and nonsilage purposes.

Dominant, Heritable Resistance to Stewart's Wilt in Maize Is Associated with an Enhanced Vascular Defense Response to Infection with Pantoea stewartii

Vascular wilt bacteria such as Pantoea stewartii, the causal agent of Stewart's bacterial wilt of maize (SW), are destructive pathogens that are difficult to control. These bacteria colonize the xylem, where they form biofilms that block sap flow leading to characteristic wilting symptoms. Heritable forms of SW resistance exist and are used in maize breeding programs but the underlying genes and mechanisms are mostly unknown. Here, we show that seedlings of maize inbred lines with pan1 mutations are highly resistant to SW. However, current evidence suggests that other genes introgressed along with pan1 are responsible for resistance. Genomic analyses of pan1 lines were used to identify candidate resistance genes. In-depth comparison of P. stewartii interaction with susceptible and resistant maize lines revealed an enhanced vascular defense response in pan1 lines characterized by accumulation of electron-dense materials in xylem conduits visible by electron microscopy. We propose that this vascular defense response restricts P. stewartii spread through the vasculature, reducing both systemic bacterial colonization of the xylem network and consequent wilting. Though apparently unrelated to the resistance phenotype of pan1 lines, we also demonstrate that the effector WtsE is essential for P. stewartii xylem dissemination, show evidence for a nutritional immunity response to P. stewartii that alters xylem sap composition, and present the first analysis of maize transcriptional responses to P. stewartii infection.

Using Maize Chromosome Segment Substitution Line Populations for the Identification of Loci Associated with Multiple Disease Resistance

Southern Leaf Blight (SLB), Northern Leaf Blight (NLB), and Gray Leaf Spot (GLS) caused by Cochliobolus heterostrophus, Setosphaeria turcica, and Cercospora zeae-maydis respectively, are among the most important diseases of corn worldwide. Previously, moderately high and significantly positive genetic correlations between resistance levels to each of these diseases were identified in a panel of 253 diverse maize inbred lines. The goal of this study was to identify loci underlying disease resistance in some of the most multiple disease resistant (MDR) lines by the creation of chromosome segment substitution line (CSSL) populations in multiple disease susceptible (MDS) backgrounds. Four MDR lines (NC304, NC344, Ki3, NC262) were used as donor parents and two MDS lines (Oh7B, H100) were used as recurrent parents to produce eight BC3F4:5 CSSL populations comprising 1,611 lines in total. Each population was genotyped and assessed for each disease in replicated trials in two environments. Moderate to high heritabilities on an entry mean basis were observed (0.32 to 0.83). Several lines in each population were significantly more resistant than the MDS parental lines for each disease. Multiple quantitative trait loci (QTL) for disease resistance were detected for each disease in most of the populations. Seventeen QTL were associated with variation in resistance to more than one disease (SLB/NLB: 2; SLB/GLS: 7; NLB/GLS: 2 and 6 to all three diseases). For most populations and most disease combinations, significant correlations were observed between disease scores and also between marker effects for each disease. The number of lines that were resistant to more than one disease was significantly higher than would be expected by chance. Using the results from individual QTL analyses, a composite statistic based on Mahalanobis distance (Md) was used to identify joint marker associations with multiple diseases. Across all populations and diseases, 246 markers had significant Md values. However further analysis revealed that most of these associations were due to strong QTL effects on a single disease. Together, these findings reinforce our previous conclusions that loci associated with resistance to different diseases are clustered in the genome more often than would be expected by chance. Nevertheless true MDR loci which have significant effects on more than one disease are still much rarer than loci with single disease effects.

A remorin gene is implicated in quantitative disease resistance in maize

Quantitative disease resistance is used by plant breeders to improve host resistance. We demonstrate a role for a maize remorin ( ZmREM6.3 ) in quantitative resistance against northern leaf blight using high-resolution fine mapping, expression analysis, and mutants. This is the first evidence of a role for remorins in plant-fungal interactions. Quantitative disease resistance (QDR) is important for the development of crop cultivars and is particularly useful when loci also confer multiple disease resistance. Despite its widespread use, the underlying mechanisms of QDR remain largely unknown. In this study, we fine-mapped a known quantitative trait locus (QTL) conditioning disease resistance on chromosome 1 of maize. This locus confers resistance to three foliar diseases: northern leaf blight (NLB), caused by the fungus Setosphaeria turcica; Stewart's wilt, caused by the bacterium Pantoea stewartii; and common rust, caused by the fungus Puccinia sorghi. The Stewart's wilt QTL was confined to a 5.26-Mb interval, while the rust QTL was reduced to an overlapping 2.56-Mb region. We show tight linkage between the NLB QTL locus and the loci conferring resistance to Stewart's wilt and common rust. Pleiotropy cannot be excluded for the Stewart's wilt and the common rust QTL, as they were fine-mapped to overlapping regions. Four positional candidate genes within the 243-kb NLB interval were examined with expression and mutant analysis: a gene with homology to an F-box gene, a remorin gene (ZmREM6.3), a chaperonin gene, and an uncharacterized gene. The F-box gene and ZmREM6.3 were more highly expressed in the resistant line. Transposon tagging mutants were tested for the chaperonin and ZmREM6.3, and the remorin mutant was found to be more susceptible to NLB. The putative F-box is a strong candidate, but mutants were not available to test this gene. Multiple lines of evidence strongly suggest a role for ZmREM6.3 in quantitative disease resistance.

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing plant disease resistance

ABSTRACT The mixed linear model (MLM) is an advanced statistical technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.

SSRs and INDELs mined from the sunflower EST database: abundance, polymorphisms, and cross-taxa utility

Simple sequence repeats (SSRs) are abundant and frequently highly polymorphic in transcribed sequences and widely targeted for marker development in eukaryotes. Sunflower (Helianthus annuus) transcript assemblies were built and mined to identify SSRs and insertions-deletions (INDELs) for marker development, comparative mapping, and other genomics applications in sunflower. We describe the spectrum and frequency of SSRs identified in the sunflower EST database, a catalog of 16,643 EST-SSRs, a collection of 484 EST-SSR and 43 EST-INDEL markers developed from common sunflower ESTs, polymorphisms of the markers among the parents of several intraspecific and interspecific mapping populations, and the transferability of the markers to closely and distantly related species in the Compositae. Of 17,904 unigenes in the transcript assembly, 1,956 (10.9%) harbored one or more SSRs with repeat counts of n > or = 5. EST-SSR markers were 1.6-fold more polymorphic among exotic than elite genotypes and 0.7-fold less polymorphic than non-genic SSR markers. Of 466 EST-SSR or INDEL markers screened for cross-species amplification and polymorphisms, 413 (88.6%) amplified alleles from one or more wild species (H. argophyllus, H. tuberosus, H. anomalus, H. paradoxus, and H. deserticola), whereas 69 (14.8%) amplified alleles from safflower (Carthamus tinctorius) and 67 (14.4%) amplified alleles from lettuce (Lactuca sativa); hence, only a fraction were transferable to distantly related genera in the Compositae, whereas most were transferable to wild relatives of H. annuus. Several thousand additional SSRs were identified in the EST database and supply a wealth of templates for EST-SSR marker development in sunflower.

Selection mapping of loci for quantitative disease resistance in a diverse maize population

The selection response of a complex maize population improved primarily for quantitative disease resistance to northern leaf blight (NLB) and secondarily for common rust resistance and agronomic phenotypes was investigated at the molecular genetic level. A tiered marker analysis with 151 simple sequence repeat (SSR) markers in 90 individuals of the population indicated that on average six alleles per locus were available for selection. An improved test statistic for selection mapping was developed, in which quantitative trait loci (QTL) are identified through the analysis of allele-frequency shifts at mapped multiallelic loci over generations of selection. After correcting for the multiple tests performed, 25 SSR loci showed evidence of selection. Many of the putatively selected loci were unlinked and dispersed across the genome, which was consistent with the diffuse distribution of previously published QTL for NLB resistance. Compelling evidence for selection was found on maize chromosome 8, where several putatively selected loci colocalized with published NLB QTL and a race-specific resistance gene. Analysis of F2 populations derived from the selection mapping population suggested that multiple linked loci in this chromosomal segment were, in part, responsible for the selection response for quantitative resistance to NLB.

Deregulation of maize C4 photosynthetic development in a mesophyll cell-defective mutant

During maize (Zea mays) C(4) differentiation, mesophyll (M) and bundle sheath (BS) cells accumulate distinct sets of photosynthetic enzymes, with very low photosystem II (PSII) content in BS chloroplasts. Consequently, there is little linear electron transport in the BS and ATP is generated by cyclic electron flow. In contrast, M thylakoids are very similar to those of C(3) plants and produce the ATP and NADPH that drive metabolic activities. Regulation of this differentiation process is poorly understood, but involves expression and coordination of nuclear and plastid genomes. Here, we identify a recessive allele of the maize high chlorophyll fluorescence (Hcf136) homolog that in Arabidopsis (Arabidopsis thaliana) functions as a PSII stability or assembly factor located in the thylakoid lumen. Proteome analysis of the thylakoids and electron microscopy reveal that Zmhcf136 lacks PSII complexes and grana thylakoids in M chloroplasts, consistent with the previously defined Arabidopsis function. Interestingly, hcf136 is also defective in processing the full-length psbB-psbT-psbH-petB-petD polycistron specifically in M chloroplasts. To determine whether the loss of PSII in M cells affects C(4) differentiation, we performed cell-type-specific transcript analysis of hcf136 and wild-type seedlings. The results indicate that M and BS cells respond uniquely to the loss of PSII, with little overlap in gene expression changes between data sets. These results are discussed in the context of signals that may drive differential gene expression in C(4) photosynthesis.

Single nucleotide polymorphisms and linkage disequilibrium in sunflower

Genetic diversity in modern sunflower (Helianthus annuus L.) cultivars (elite oilseed inbred lines) has been shaped by domestication and breeding bottlenecks and wild and exotic allele introgression(-)the former narrowing and the latter broadening genetic diversity. To assess single nucleotide polymorphism (SNP) frequencies, nucleotide diversity, and linkage disequilibrium (LD) in modern cultivars, alleles were resequenced from 81 genic loci distributed throughout the sunflower genome. DNA polymorphisms were abundant; 1078 SNPs (1/45.7 bp) and 178 insertions-deletions (INDELs) (1/277.0 bp) were identified in 49.4 kbp of DNA/genotype. SNPs were twofold more frequent in noncoding (1/32.1 bp) than coding (1/62.8 bp) sequences. Nucleotide diversity was only slightly lower in inbred lines ( = 0.0094) than wild populations ( = 0.0128). Mean haplotype diversity was 0.74. When extraploted across the genome ( approximately 3500 Mbp), sunflower was predicted to harbor at least 76.4 million common SNPs among modern cultivar alleles. LD decayed more slowly in inbred lines than wild populations (mean LD declined to 0.32 by 5.5 kbp in the former, the maximum physical distance surveyed), a difference attributed to domestication and breeding bottlenecks. SNP frequencies and LD decay are sufficient in modern sunflower cultivars for very high-density genetic mapping and high-resolution association mapping.

Distribution of Activator (Ac) throughout the maize genome for use in regional mutagenesis

A collection of Activator (Ac)-containing, near-isogenic W22 inbred lines has been generated for use in regional mutagenesis experiments. Each line is homozygous for a single, precisely positioned Ac element and the Ds reporter, r1-sc:m3. Through classical and molecular genetic techniques, 158 transposed Ac elements (tr-Acs) were distributed throughout the maize genome and 41 were precisely placed on the linkage map utilizing multiple recombinant inbred populations. Several PCR techniques were utilized to amplify DNA fragments flanking tr-Ac insertions up to 8 kb in length. Sequencing and database searches of flanking DNA revealed that the majority of insertions are in hypomethylated, low- or single-copy sequences, indicating an insertion site preference for genic sequences in the genome. However, a number of Ac transposition events were to highly repetitive sequences in the genome. We present evidence that suggests Ac expression is regulated by genomic context resulting in subtle variations in Ac-mediated excision patterns. These tr-Ac lines can be utilized to isolate genes with unknown function, to conduct fine-scale genetic mapping experiments, and to generate novel allelic diversity in applied breeding programs.

Acetohydroxyacid synthase mutations conferring resistance to imidazolinone or sulfonylurea herbicides in sunflower

Wild biotypes of cultivated sunflower ( Helianthus annuus L.) are weeds in corn ( Zea mays L.), soybean ( Glycine max L.), and other crops in North America, and are commonly controlled by applying acetohydroxyacid synthase (AHAS)-inhibiting herbicides. Biotypes resistant to two classes of AHAS-inhibiting herbicides-imidazolinones (IMIs) or sulfonylureas (SUs)-have been discovered in wild sunflower populations (ANN-PUR and ANN-KAN) treated with imazethapyr or chlorsulfuron, respectively. The goals of the present study were to isolate AHAS genes from sunflower, identify mutations in AHAS genes conferring herbicide resistance in ANN-PUR and ANN-KAN, and develop tools for marker-assisted selection (MAS) of herbicide resistance genes in sunflower. Three AHAS genes ( AHAS1, AHAS2, and AHAS3) were identified, cloned, and sequenced from herbicide-resistant (mutant) and -susceptible (wild type) genotypes. We identified 48 single-nucleotide polymorphisms (SNPs) in AHAS1, a single six-base pair insertion-deletion in AHAS2, and a single SNP in AHAS3. No DNA polymorphisms were found in AHAS2 among elite inbred lines. AHAS1 from imazethapyr-resistant inbreds harbored a C-to-T mutation in codon 205 ( Arabidopsis thaliana codon nomenclature), conferring resistance to IMI herbicides, whereas AHAS1 from chlorsulfuron-resistant inbreds harbored a C-to-T mutation in codon 197, conferring resistance to SU herbicides. SNP and single-strand conformational polymorphism markers for AHAS1, AHAS2, and AHAS3 were developed and genetically mapped. AHAS1, AHAS2, and AHAS3 mapped to linkage groups 2 ( AHAS3), 6 ( AHAS2), and 9 ( AHAS1). The C/T SNP in codon 205 of AHAS1 cosegregated with a partially dominant gene for resistance to IMI herbicides in two mutant x wild-type populations. The molecular breeding tools described herein create the basis for rapidly identifying new mutations in AHAS and performing MAS for herbicide resistance genes in sunflower.