Variation in shoot architecture traits and their relationship to canopy coverage and light interception in soybean (Glycine max).

BACKGROUND: In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive. RESULTS: This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI. CONCLUSION: In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology.

Cataloging SCN resistance loci in North American public soybean breeding programs.

Soybean cyst nematode (SCN) is a destructive pathogen of soybeans responsible for annual yield loss exceeding $1.5 billion in the United States. Here, we conducted a series of genome-wide association studies (GWASs) to understand the genetic landscape of SCN resistance in the University of Missouri soybean breeding programs (Missouri panel), as well as germplasm and cultivars within the United States Department of Agriculture (USDA) Uniform Soybean Tests-Northern Region (NUST). For the Missouri panel, we evaluated the resistance of breeding lines to SCN populations HG 2.5.7 (Race 1), HG 1.2.5.7 (Race 2), HG 0 (Race 3), HG 2.5.7 (Race 5), and HG 1.3.6.7 (Race 14) and identified seven quantitative trait nucleotides (QTNs) associated with SCN resistance on chromosomes 2, 8, 11, 14, 17, and 18. Additionally, we evaluated breeding lines in the NUST panel for resistance to SCN populations HG 2.5.7 (Race 1) and HG 0 (Race 3), and we found three SCN resistance-associated QTNs on chromosomes 7 and 18. Through these analyses, we were able to decipher the impact of seven major genetic loci, including three novel loci, on resistance to several SCN populations and identified candidate genes within each locus. Further, we identified favorable allelic combinations for resistance to individual SCN HG types and provided a list of available germplasm for integration of these unique alleles into soybean breeding programs. Overall, this study offers valuable insight into the landscape of SCN resistance loci in U.S. public soybean breeding programs and provides a framework to develop new and improved soybean cultivars with diverse plant genetic modes of SCN resistance.

A genomic analysis of the University of Nebraska Replicated Recurrent Selection program.

The inbred-hybrid system of maize breeding closely resembles a reciprocal full-sib (RFS) selection program. Studying changes in genetic variation as a result of RFS selection can help illuminate long-standing questions regarding the relative roles of selection and genetic drift and help understand the nature of adaptation occurring in selection programs. The University of Nebraska-Lincoln Replicated Recurrent Selection (UNL-RpRS) program underwent eight cycles of replicated RFS and S1-progeny selection, making it a powerful system to study genomic changes accompanying selection for inter-population performance. The objectives of this study were to identify regions of the genome under selection after eight cycles of selection and evaluate the effect eight cycles of selection for inter-population full-sib performance had in expanding genome-wide and localized population structure. We address these questions with a large set of individuals sampled from the UNL-RpRS program with dense genotyping-by-sequence data. We found evidence of parallel selection signatures in the UNL-RpRS program, with a region on chromosome 7 being implicated in three of the four selection systems studied. Regions that displayed selection signatures across independently run selection programs represent regions likely to be capitalizing on standing genetic variation and support a soft sweep model of adaptation. We did not find selection to be a strong force in diverging populations undergoing RFS. This could be due to the nature of adaptation occurring in these populations, underlying gene action, or a result of unstable genetic topographies.

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.

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.

Increasing the rate of genetic gain for seed yield remains the primary breeding objective in both public and private soybean [Glycine max (L.) Merr.] breeding programs. Genomic selection (GS) has the potential to accelerate the rate of genetic gain for soybean seed yield. Limited studies to date have validated GS accuracy and directly compared GS with phenotypic selection (PS), and none have been reported in soybean. This study conducted the first empirical validation of GS for increasing seed yield using over 1,500 lines and over 7 yr (2010-2016) of replicated experiments in the University of Nebraska-Lincoln soybean breeding program. The study was designed to capture the varying genetic relatedness of the training population to three validation sets: two large biparental populations (TBP-1 and TBP-2) and a large validation set comprised of 457 preselected advanced lines derived from 45 biparental populations (TMP). We found that prediction accuracy (.54) realized in our validation experiments was comparable with what we obtained from a series of cross-validation experiments (.64). Both GS and PS were more effective for increasing the population mean performance compared with random selection (RS). We found a selection advantage of GS over PS, where higher genetic gain and identification of top-performing lines was maximized at 10-20% selected proportion. Genomic selection led to small increases in genetic similarity when compared with PS and RS presumably because of a significant shift on allelic frequencies toward the extremes, suggesting that it could erode genetic diversity more quickly. Overall, we found that GS can perform as effectively as PS but that measures should be considered to protect against loss of genetic variance when using GS.

Development of molecular inversion probes for soybean progeny genomic selection genotyping.

Increasing rate of genetic gain for key agronomic traits through genomic selection requires the development of new molecular methods to run genome-wide single-nucleotide polymorphisms (SNPs). The main limitation of current methods is the cost is too high to screen breeding populations. Molecular inversion probes (MIPs) are a targeted genotyping-by-sequencing (GBS) method that could be used for soybean [Glycine max (L.) Merr.] that is both cost-effective, high-throughput, and provides high data quality to screen breeder's germplasm for genomic selection. A 1K MIP SNP set was developed for soybean with uniformly distributed markers across the genome. The SNPs were selected to maximize the number of informative markers in germplasm being tested in soybean breeding programs located in the northern-central and middle-southern regions of the United States. The 1K SNP MIP set was tested on diverse germplasm and a recombinant inbred line (RIL) population. Targeted sequencing with MIPs obtained an 85% enrichment for the targeted SNPs. The MIP genotyping accuracy was 93% overall, whereas homozygous call accuracy was 98% with <10% missing data. The accuracy of MIPs combined with its low per-sample cost makes it a powerful tool to enable genomic selection within soybean breeding programs.

Candidate Genes Modulating Reproductive Timing in Elite US Soybean Lines Identified in Soybean Alleles of Arabidopsis Flowering Orthologs With Divergent Latitude Distribution.

Adaptation of soybean cultivars to the photoperiod in which they are grown is critical for optimizing plant yield. However, despite its importance, only the major loci conferring variation in flowering time and maturity of US soybean have been isolated. By contrast, over 200 genes contributing to floral induction in the model organism Arabidopsis thaliana have been described. In this work, putative alleles of a library of soybean orthologs of these Arabidopsis flowering genes were tested for their latitudinal distribution among elite US soybean lines developed in the United States. Furthermore, variants comprising the alleles of genes with significant differences in latitudinal distribution were assessed for amino acid conservation across disparate genera to infer their impact on gene function. From these efforts, several candidate genes from various biological pathways were identified that are likely being exploited toward adaptation of US soybean to various maturity groups.

Plant Breeding for Intercropping in Temperate Field Crop Systems: A Review.

Monoculture cropping systems currently dominate temperate agroecosystems. However, intercropping can provide valuable benefits, including greater yield stability, increased total productivity, and resilience in the face of pest and disease outbreaks. Plant breeding efforts in temperate field crops are largely focused on monoculture production, but as intercropping becomes more widespread, there is a need for cultivars adapted to these cropping systems. Cultivar development for intercropping systems requires a systems approach, from the decision to breed for intercropping systems through the final stages of variety testing and release. Design of a breeding scheme should include information about species variation for performance in intercropping, presence of genotype × management interaction, observation of key traits conferring success in intercropping systems, and the specificity of intercropping performance. Together this information can help to identify an optimal selection scheme. Agronomic and ecological knowledge are critical in the design of selection schemes in cropping systems with greater complexity, and interaction with other researchers and key stakeholders inform breeding decisions throughout the process. This review explores the above considerations through three case studies: (1) forage mixtures, (2) perennial groundcover systems (PGC), and (3) soybean-pennycress intercropping. We provide an overview of each cropping system, identify relevant considerations for plant breeding efforts, describe previous breeding focused on the cropping system, examine the extent to which proposed theoretical approaches have been implemented in breeding programs, and identify areas for future development.

Mining Fiskeby III and Mandarin (Ottawa) Expression Profiles to Understand Iron Stress Tolerant Responses in Soybean.

The soybean (Glycine max L. merr) genotype Fiskeby III is highly resistant to a multitude of abiotic stresses, including iron deficiency, incurring only mild yield loss during stress conditions. Conversely, Mandarin (Ottawa) is highly susceptible to disease and suffers severe phenotypic damage and yield loss when exposed to abiotic stresses such as iron deficiency, a major challenge to soybean production in the northern Midwestern United States. Using RNA-seq, we characterize the transcriptional response to iron deficiency in both Fiskeby III and Mandarin (Ottawa) to better understand abiotic stress tolerance. Previous work by our group identified a quantitative trait locus (QTL) on chromosome 5 associated with Fiskeby III iron efficiency, indicating Fiskeby III utilizes iron deficiency stress mechanisms not previously characterized in soybean. We targeted 10 of the potential candidate genes in the Williams 82 genome sequence associated with the QTL using virus-induced gene silencing. Coupling virus-induced gene silencing with RNA-seq, we identified a single high priority candidate gene with a significant impact on iron deficiency response pathways. Characterization of the Fiskeby III responses to iron stress and the genes underlying the chromosome 5 QTL provides novel targets for improved abiotic stress tolerance in soybean.

The utility of genomic prediction models in evolutionary genetics.

Variation in complex traits is the result of contributions from many loci of small effect. Based on this principle, genomic prediction methods are used to make predictions of breeding value for an individual using genome-wide molecular markers. In breeding, genomic prediction models have been used in plant and animal breeding for almost two decades to increase rates of genetic improvement and reduce the length of artificial selection experiments. However, evolutionary genomics studies have been slow to incorporate this technique to select individuals for breeding in a conservation context or to learn more about the genetic architecture of traits, the genetic value of missing individuals or microevolution of breeding values. Here, we outline the utility of genomic prediction and provide an overview of the methodology. We highlight opportunities to apply genomic prediction in evolutionary genetics of wild populations and the best practices when using these methods on field-collected phenotypes.

The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment.

High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

Optimization of training sets for genomic prediction of early-stage single crosses in maize.

KEY MESSAGE: Training population optimization algorithms are useful for efficiently training genomic prediction models for single-cross performance, especially if the population is extended beyond only realized crosses to all possible single crosses. Genomic prediction of single-cross performance could allow effective evaluation of all possible single crosses between all inbreds developed in a hybrid breeding program. The objectives of the present study were to investigate the effect of different levels of relatedness on genomic predictive ability of single crosses, evaluate the usefulness of deterministic formula to forecast prediction accuracy in advance, and determine the potential for TRS optimization based on prediction error variance (PEVmean) and coefficient of determination (CDmean) criteria. We used 481 single crosses made by crossing 89 random recombinant inbred lines (RILs) belonging to the Iowa stiff stalk synthetic group with 103 random RILs belonging to the non-stiff stalk synthetic heterotic group. As expected, predictive ability was enhanced by ensuring close relationships between TRSs and target sets, even when TRS sizes were smaller. We found that designing a TRS based on PEVmean or CDmean criteria is useful for increasing the efficiency of genomic prediction of maize single crosses. We went further and extended the sampling space from that of all observed single crosses to all possible single crosses, providing a much larger genetic space within which to design a training population. Using all possible single crosses increased the advantage of the PEVmean and CDmean methods based on expected prediction accuracy. This finding suggests that it may be worthwhile using an optimization algorithm to select a training population from all possible single crosses to maximize efficiency in training accurate models for hybrid genomic prediction.

Genome-wide association study and genomic selection for tolerance of soybean biomass to soybean cyst nematode infestation.

Soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is one of the most devastating pathogens affecting soybean production in the U.S. and worldwide. The use of SCN-resistant soybean cultivars is one of the most affordable strategies to cope with SCN infestation. Because of the limited sources of SCN resistance and changes in SCN virulence phenotypes, host resistance in current cultivars has increasingly been overcome by the pathogen. Host tolerance has been recognized as an additional tool to manage the SCN. The objectives of this study were to conduct a genome-wide association study (GWAS), to identify single nucleotide polymorphism (SNP) markers, and to perform a genomic selection (GS) study for SCN tolerance in soybean based on reduction in biomass. A total of 234 soybean genotypes (lines) were evaluated for their tolerance to SCN in greenhouse using four replicates. The tolerance index (TI = 100 × Biomass of a line in SCN infested / Biomass of the line without SCN) was used as phenotypic data of SCN tolerance. GWAS was conducted using a total of 3,782 high quality SNPs. GS was performed based upon the whole set of SNPs and the GWAS-derived SNPs, respectively. Results showed that (1) a large variation in soybean TI to SCN infection among the soybean genotypes was identified; (2) a total of 35, 21, and 6 SNPs were found to be associated with SCN tolerance using the models SMR, GLM (PCA), and MLM (PCA+K) with 6 SNPs overlapping between models; (3) GS accuracy was SNP set-, model-, and training population size-dependent; and (4) genes around Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, Glyma.15G107200, and Glyma.19G121200.1 (Table 4). Glyma.06G134900, Glyma.15G097500.1, Glyma.15G100900.3, Glyma.15G105400, and Glyma.19G121200.1 are best candidates. To the best of our knowledge, this is the first report highlighting SNP markers associated with tolerance index based on biomass reduction under SCN infestation in soybean. This research opens a new approach to use SCN tolerance in soybean breeding and the SNP markers will provide a tool for breeders to select for SCN tolerance.

Characterizing introgression-by-environment interactions using maize near isogenic lines.

KEY MESSAGE: Significant introgression-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome. Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to introgression-by-environment interactions. To study potential patterns of introgression-by-environment interactions, fifteen near-isogenic lines (NILs) with > 90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess introgression-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the introgression-by-environment interaction varies across traits. Some introgressed segments were more prone to introgression-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of introgression-by-environment interaction in NILs is useful in consideration of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.

Dominance Effects and Functional Enrichments Improve Prediction of Agronomic Traits in Hybrid Maize.

Single-cross hybrids have been critical to the improvement of maize (Zea mays L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated three agronomic traits: days to silking (DTS), plant height (PH), and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.

Maize genomes to fields (G2F): 2014-2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets.

OBJECTIVES: Advanced tools and resources are needed to efficiently and sustainably produce food for an increasing world population in the context of variable environmental conditions. The maize genomes to fields (G2F) initiative is a multi-institutional initiative effort that seeks to approach this challenge by developing a flexible and distributed infrastructure addressing emerging problems. G2F has generated large-scale phenotypic, genotypic, and environmental datasets using publicly available inbred lines and hybrids evaluated through a network of collaborators that are part of the G2F's genotype-by-environment (G × E) project. This report covers the public release of datasets for 2014-2017. DATA DESCRIPTION: Datasets include inbred genotypic information; phenotypic, climatic, and soil measurements and metadata information for each testing location across years. For a subset of inbreds in 2014 and 2015, yield component phenotypes were quantified by image analysis. Data released are accompanied by README descriptions. For genotypic and phenotypic data, both raw data and a version without outliers are reported. For climatic data, a version calibrated to the nearest airport weather station and a version without outliers are reported. The 2014 and 2015 datasets are updated versions from the previously released files [1] while 2016 and 2017 datasets are newly available to the public.

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project.

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.

Belowground microbial communities respond to water deficit and are shaped by decades of maize hybrid breeding.

Root-associated microbial communities are important for maintaining agricultural productivity. However, belowground microbial community response to drought in temperate maize agroecosystems, as well as how these responses to water-stress are shaped by host genotype are poorly understood. Ten maize hybrids (six newer and four older) were grown in a replicated field trial. The endosphere, rhizosphere and soil bacterial and archaeal communities were sampled and analyzed using 16S rRNA gene amplicon sequencing. Sampling was done at two developmental stages in a water-limited environment with and without supplemental irrigation. Significant shifts in microbial community composition (ß-diversity) were measured between two sampling times during the season, in well-watered and water-stressed conditions and in newer and older generation maize hybrids. The microbial community diversity within samples (α-diversity) was not affected by drought stress or host factors. The phyla Actinobacteria and Firmicutes were more abundant in the rhizosphere of newer hybrids under water stress. These results highlight the importance of temporal variation, environmental stress and plant genetics as influenced by breeding history in shaping the composition of root associated microbial communities. These insights may provide new approaches to the improvement of crop stress tolerance through optimizing microbial communities.