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Predicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years. The competition attracted registrants from around the world with representation from academic, government, industry, and non-profit institutions as well as unaffiliated. These participants came from diverse disciplines include plant science, animal science, breeding, statistics, computational biology and others. Some participants had no formal genetics or plant-related training, and some were just beginning their graduate education. The teams applied varied methods and strategies, providing a wealth of modeling knowledge based on a common dataset. The winner's strategy involved two models combining machine learning and traditional breeding tools: one model emphasized environment using features extracted by Random Forest, Ridge Regression and Least-squares, and one focused on genetics. Other high-performing teams' methods included quantitative genetics, machine learning/deep learning, mechanistic models, and model ensembles. The dataset factors used, such as genetics; weather; and management data, were also diverse, demonstrating that no single model or strategy is far superior to all others within the context of this competition., (Published by Oxford University Press on behalf of The Genetics Society of America 2024.)

Competing Interests: Conflicts of interest The authors declare no conflicts of interest.

The presence or absence of awns-whether wheat heads are 'bearded' or 'smooth' - is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data for wheat (Triticum aestivum) to estimate the yield effects of awns under different environmental conditions over a 12-year period in the southeastern USA. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates., (Published by Oxford University Press on behalf of the Society for Experimental Biology 2023.)

Tropical maize can be used to diversify the genetic base of temperate germplasm and help create climate-adapted cultivars. However, tropical maize is unadapted to temperate environments, in which sensitivities to long photoperiods and cooler temperatures result in severely delayed flowering times, developmental defects, and little to no yield. Overcoming this maladaptive syndrome can require a decade of phenotypic selection in a targeted, temperate environment. To accelerate the incorporation of tropical diversity in temperate breeding pools, we tested if an additional generation of genomic selection can be used in an off-season nursery where phenotypic selection is not very effective. Prediction models were trained using flowering time recorded on random individuals in separate lineages of a heterogenous population grown at two northern U.S. latitudes. Direct phenotypic selection and genomic prediction model training was performed within each target environment and lineage, followed by genomic prediction of random intermated progenies in the off-season nursery. Performance of genomic prediction models was evaluated on self-fertilized progenies of prediction candidates grown in both target locations in the following summer season. Prediction abilities ranged from 0.30 to 0.40 among populations and evaluation environments. Prediction models with varying marker effect distributions or spatial field effects had similar accuracies. Our results suggest that genomic selection in a single off-season generation could increase genetic gains for flowering time by more than 50% compared to direct selection in summer seasons only, reducing the time required to change the population mean to an acceptably adapted flowering time by about one-third to one-half., (Published by Oxford University Press on behalf of The Genetics Society of America 2023.)

Crop genetic diversity for climate adaptations is globally partitioned. We performed experimental evolution in maize to understand the response to selection and how plant germplasm can be moved across geographical zones. Initialized with a common population of tropical origin, artificial selection on flowering time was performed for two generations at eight field sites spanning 25° latitude, a 2800 km transect. We then jointly tested all selection lineages across the original sites of selection, for the target trait and 23 other traits. Modeling intergenerational shifts in a physiological reaction norm revealed separate components for flowering-time plasticity. Generalized and local modes of selection altered the plasticity of each lineage, leading to a latitudinal pattern in the responses to selection that were strongly driven by photoperiod. This transformation led to widespread changes in developmental, architectural, and yield traits, expressed collectively in an environment-dependent manner. Furthermore, selection for flowering time alone alleviated a maladaptive syndrome and improved yields for tropical maize in the temperate zone. Our findings show how phenotypic selection can rapidly shift the flowering phenology and plasticity of maize. They also demonstrate that selecting crops to local conditions can accelerate adaptation to climate change., (© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.)

The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public- and private-sector maize (Zea mays L.) breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multienvironment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on 6 yr of topcross evaluations from the two GEM programs in Raleigh, NC, and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from GS ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm., (© 2022 Bayer Crop Science and The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Key Message: GS and PS performed similarly in improving resistance to FER and FUM content. With cheaper and faster genotyping methods, GS has the potential to be more efficient than PS. Fusarium verticillioides is a common maize (Zea mays L.) pathogen that causes Fusarium ear rot (FER) and produces the mycotoxin fumonisin (FUM). This study empirically compared phenotypic selection (PS) and genomic selection (GS) for improving FER and FUM resistance. Three intermating generations of recurrent GS were conducted in the same time frame and from a common base population as two generations of recurrent PS. Lines sampled from each PS and GS cycle were evaluated in three North Carolina environments in 2020. We observed similar cumulative responses to GS and PS, representing decreases of about 50% of mean FER and FUM compared to the base population. The first cycle of GS was more effective than later cycles. PS and GS both achieved about 70% of predicted total gain from selection for FER, but only about 26% of predicted gains for FUM, suggesting that heritability for FUM was overestimated. We observed a 20% decrease in genetic marker variation from PS and 30% decrease from GS. Our greatest challenge was our inability to quickly obtain dense and consistent set of marker genotypes across generations of GS. Practical implementation of GS in individual small-scale breeding programs will require cheaper and faster genotyping methods, and such technological advances will present opportunities to significantly optimize selection and mating schemes for future GS efforts beyond what we were able to achieve in this study., (© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Researchers have used quantitative genetics to map cotton fiber quality and agronomic performance loci, but many alleles may be population or environment-specific, limiting their usefulness in a pedigree selection, inbreeding-based system. Here, we utilized genotypic and phenotypic data on a panel of 80 important historical Upland cotton ( Gossypium hirsutum L.) lines to investigate the potential for genomics-based selection within a cotton breeding program's relatively closed gene pool. We performed a genome-wide association study (GWAS) to identify alleles correlated to 20 fiber quality, seed composition, and yield traits and looked for a consistent detection of GWAS hits across 14 individual field trials. We also explored the potential for genomic prediction to capture genotypic variation for these quantitative traits and tested the incorporation of GWAS hits into the prediction model. Overall, we found that genomic selection programs for fiber quality can begin immediately, and the prediction ability for most other traits is lower but commensurate with heritability. Stably detected GWAS hits can improve prediction accuracy, although a significance threshold must be carefully chosen to include a marker as a fixed effect. We place these results in the context of modern public cotton line-breeding and highlight the need for a community-based approach to amass the data and expertise necessary to launch US public-sector cotton breeders into the genomics-based selection era.

Technology advances have made possible the collection of a wealth of genomic, environmental, and phenotypic data for use in plant breeding. Incorporation of environmental data into environment-specific genomic prediction is hindered in part because of inherently high data dimensionality. Computationally efficient approaches to combining genomic and environmental information may facilitate extension of genomic prediction models to new environments and germplasm, and better understanding of genotype-by-environment (G × E) interactions. Using genomic, yield trial, and environmental data on 1,918 unique hybrids evaluated in 59 environments from the maize Genomes to Fields project, we determined that a set of 10,153 SNP dominance coefficients and a 5-day temporal window size for summarizing environmental variables were optimal for genomic prediction using only genetic and environmental main effects. Adding marker-by-environment variable interactions required dimension reduction, and we found that reducing dimensionality of the genetic data while keeping the full set of environmental covariates was best for environment-specific genomic prediction of grain yield, leading to an increase in prediction ability of 2.7% to achieve a prediction ability of 80% across environments when data were masked at random. We then measured how prediction ability within environments was affected under stratified training-testing sets to approximate scenarios commonly encountered by plant breeders, finding that incorporation of marker-by-environment effects improved prediction ability in cases where training and test sets shared environments, but did not improve prediction in new untested environments. The environmental similarity between training and testing sets had a greater impact on the efficacy of prediction than genetic similarity between training and test sets., (Published by Oxford University Press on behalf of Genetics Society of America 2021. This work is written by US Government employees and is in the public domain in the US.)

Inbreeding depression is the reduction in fitness and vigor resulting from mating of close relatives observed in many plant and animal species. The extent to which the genetic load of mutations contributing to inbreeding depression is due to large-effect mutations versus variants with very small individual effects is unknown and may be affected by population history. We compared the effects of outcrossing and self-fertilization on 18 traits in a landrace population of maize, which underwent a population bottleneck during domestication, and a neighboring population of its wild relative teosinte. Inbreeding depression was greater in maize than teosinte for 15 of 18 traits, congruent with the greater segregating genetic load in the maize population that we predicted from sequence data. Parental breeding values were highly consistent between outcross and selfed offspring, indicating that additive effects determine most of the genetic value even in the presence of strong inbreeding depression. We developed a novel linkage scan to identify quantitative trait loci (QTL) representing large-effect rare variants carried by only a single parent, which were more important in teosinte than maize. Teosinte also carried more putative juvenile-acting lethal variants identified by segregation distortion. These results suggest a mixture of mostly polygenic, small-effect partially recessive effects in linkage disequilibrium underlying inbreeding depression, with an additional contribution from rare larger-effect variants that was more important in teosinte but depleted in maize following the domestication bottleneck. Purging associated with the maize domestication bottleneck may have selected against some large effect variants, but polygenic load is harder to purge and overall segregating mutational burden increased in maize compared to teosinte., Competing Interests: The authors have declared that no competing interests exist.

Very little is known about how domestication was constrained by the quantitative genetic architecture of crop progenitors and how quantitative genetic architecture was altered by domestication. Yang et al. [C. J. Yang et al. , Proc. Natl. Acad. Sci. U.S.A. 116, 5643-5652 (2019)] drew multiple conclusions about how genetic architecture influenced and was altered by maize domestication based on one sympatric pair of teosinte and maize populations. To test the generality of their conclusions, we assayed the structure of genetic variances, genetic correlations among traits, strength of selection during domestication, and diversity in genetic architecture within teosinte and maize. Our results confirm that additive genetic variance is decreased, while dominance genetic variance is increased, during maize domestication. The genetic correlations are moderately conserved among traits between teosinte and maize, while the genetic variance-covariance matrices ( G -matrices) of teosinte and maize are quite different, primarily due to changes in the submatrix for reproductive traits. The inferred long-term selection intensities during domestication were weak, and the neutral hypothesis was rejected for reproductive and environmental response traits, suggesting that they were targets of selection during domestication. The G -matrix of teosinte imposed considerable constraint on selection during the early domestication process, and constraint increased further along the domestication trajectory. Finally, we assayed variation among populations and observed that genetic architecture is generally conserved among populations within teosinte and maize but is radically different between teosinte and maize. While selection drove changes in essentially all traits between teosinte and maize, selection explains little of the difference in domestication traits among populations within teosinte or maize., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Vitamin A deficiency remains prevalent in parts of Asia, Latin America, and sub-Saharan Africa where maize (Zea mays) is a food staple. Extensive natural variation exists for carotenoids in maize grain. Here, to understand its genetic basis, we conducted a joint linkage and genome-wide association study of the US maize nested association mapping panel. Eleven of the 44 detected quantitative trait loci (QTL) were resolved to individual genes. Six of these were correlated expression and effect QTL (ceeQTL), showing strong correlations between RNA-seq expression abundances and QTL allelic effect estimates across six stages of grain development. These six ceeQTL also had the largest percentage of phenotypic variance explained, and in major part comprised the three to five loci capturing the bulk of genetic variation for each trait. Most of these ceeQTL had strongly correlated QTL allelic effect estimates across multiple traits. These findings provide an in-depth genome-level understanding of the genetic and molecular control of carotenoids in plants. In addition, these findings provide a roadmap to accelerate breeding for provitamin A and other priority carotenoid traits in maize grain that should be readily extendable to other cereals., (� American Society of Plant Biologists 2020. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Genetic variation in growth over the course of the season is a major source of grain yield variation in wheat, and for this reason variants controlling heading date and plant height are among the best-characterized in wheat genetics. While the major variants for these traits have been cloned, the importance of these variants in contributing to genetic variation for plant growth over time is not fully understood. Here we develop a biparental population segregating for major variants for both plant height and flowering time to characterize the genetic architecture of the traits and identify additional novel QTL., Results: We find that additive genetic variation for both traits is almost entirely associated with major and moderate-effect QTL, including four novel heading date QTL and four novel plant height QTL. FT2 and Vrn-A3 are proposed as candidate genes underlying QTL on chromosomes 3A and 7A, while Rht8 is mapped to chromosome 2D. These mapped QTL also underlie genetic variation in a longitudinal analysis of plant growth over time. The oligogenic architecture of these traits is further demonstrated by the superior trait prediction accuracy of QTL-based prediction models compared to polygenic genomic selection models., Conclusions: In a population constructed from two modern wheat cultivars adapted to the southeast U.S., almost all additive genetic variation in plant growth traits is associated with known major variants or novel moderate-effect QTL. Major transgressive segregation was observed in this population despite the similar plant height and heading date characters of the parental lines. This segregation is being driven primarily by a small number of mapped QTL, instead of by many small-effect, undetected QTL. As most breeding populations in the southeast U.S. segregate for known QTL for these traits, genetic variation in plant height and heading date in these populations likely emerges from similar combinations of major and moderate effect QTL. We can make more accurate and cost-effective prediction models by targeted genotyping of key SNPs.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Jarquin, de Leon, Romay, Bohn, Buckler, Ciampitti, Edwards, Ertl, Flint-Garcia, Gore, Graham, Hirsch, Holland, Hooker, Kaeppler, Knoll, Lee, Lawrence-Dill, Lynch, Moose, Murray, Nelson, Rocheford, Schnable, Schnable, Smith, Springer, Thomison, Tuinstra, Wisser, Xu, Yu and Lorenz.)

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., (Published by Oxford University Press on behalf of Genetics Society of America 2021. This work is written by US Government employees and is in the public domain in the US.)

Plants have the capacity to respond to conserved molecular features known as microbe-associated molecular patterns (MAMPs). The goal of this work was to assess variation in the MAMP response in sorghum, to map loci associated with this variation, and to investigate possible connections with variation in quantitative disease resistance. Using an assay that measures the production of reactive oxygen species, we assessed variation in the MAMP response in a sorghum association mapping population known as the sorghum conversion population (SCP). We identified consistent variation for the response to chitin and flg22-an epitope of flagellin. We identified two SNP loci associated with variation in the flg22 response and one with the chitin response. We also assessed resistance to Target Leaf Spot (TLS) disease caused by the necrotrophic fungus Bipolaris cookei in the SCP. We identified one strong association on chromosome 5 near a previously characterized disease resistance gene. A moderately significant correlation was observed between stronger flg22 response and lower TLS resistance. Possible reasons for this are discussed.

Macroorganisms' genotypes shape their phenotypes, which in turn shape the habitat available to potential microbial symbionts. This influence of host genotype on microbiome composition has been demonstrated in many systems; however, most previous studies have either compared unrelated genotypes or delved into molecular mechanisms. As a result, it is currently unclear whether the heritability of host-associated microbiomes follows similar patterns to the heritability of other complex traits. We take a new approach to this question by comparing the microbiomes of diverse maize inbred lines and their F 1 hybrid offspring, which we quantified in both rhizosphere and leaves of field-grown plants using 16S-v4 and ITS1 amplicon sequencing. We show that inbred lines and hybrids differ consistently in the composition of bacterial and fungal rhizosphere communities, as well as leaf-associated fungal communities. A wide range of microbiome features display heterosis within individual crosses, consistent with patterns for nonmicrobial maize phenotypes. For leaf microbiomes, these results were supported by the observation that broad-sense heritability in hybrids was substantially higher than narrow-sense heritability. Our results support our hypothesis that at least some heterotic host traits affect microbiome composition in maize., (© 2020 The Authors. New Phytologist © 2020 New Phytologist Trust.)

The genetics of domestication has been extensively studied ever since the rediscovery of Mendel's law of inheritance and much has been learned about the genetic control of trait differences between crops and their ancestors. Here, we ask how domestication has altered genetic architecture by comparing the genetic architecture of 18 domestication traits in maize and its ancestor teosinte using matched populations. We observed a strongly reduced number of QTL for domestication traits in maize relative to teosinte, which is consistent with the previously reported depletion of additive variance by selection during domestication. We also observed more dominance in maize than teosinte, likely a consequence of selective removal of additive variants. We observed that large effect QTL have low minor allele frequency (MAF) in both maize and teosinte. Regions of the genome that are strongly differentiated between teosinte and maize (high FST) explain less quantitative variation in maize than teosinte, suggesting that, in these regions, allelic variants were brought to (or near) fixation during domestication. We also observed that genomic regions of high recombination explain a disproportionately large proportion of heritable variance both before and after domestication. Finally, we observed that about 75% of the additive variance in both teosinte and maize is "missing" in the sense that it cannot be ascribed to detectable QTL and only 25% of variance maps to specific QTL. This latter result suggests that morphological evolution during domestication is largely attributable to very large numbers of QTL of very small effect., Competing Interests: The authors have declared that no competing interests exist.

Fusarium verticillioides , which causes ear, kernel and stem rots, has been reported as the most prevalent species on maize worldwide. Kernel infection by F. verticillioides results in reduced seed yield and quality as well as fumonisin contamination, and may affect seedling traits like germination rate, entire plant seedling length and weight. Maize resistance to Fusarium is a quantitative and complex trait controlled by numerous genes with small effects. In the present work, a Genome Wide Association Study (GWAS) of traits related to Fusarium seedling rot was carried out in 230 lines of a maize association population using 226,446 SNP markers. Phenotypes were scored on artificially infected kernels applying the rolled towel assay screening method and three traits related to disease response were measured in inoculated and not-inoculated seedlings: plant seedling length (PL), plant seedling weight (PW) and germination rate (GERM). Overall, GWAS resulted in 42 SNPs significantly associated with the examined traits. Two and eleven SNPs were associated with PL in inoculated and not-inoculated samples, respectively. Additionally, six and one SNPs were associated with PW and GERM traits in not-inoculated kernels, and further nine and thirteen SNPs were associated to the same traits in inoculated kernels. Five genes containing the significant SNPs or physically closed to them were proposed for Fusarium resistance, and 18 out of 25 genes containing or adjacent to significant SNPs identified by GWAS in the current research co-localized within QTL regions previously reported for resistance to Fusarium seed rot, Fusarium ear rot and fumonisin accumulation. Furthermore, linkage disequilibrium analysis revealed an additional gene not directly observed by GWAS analysis. These findings could aid to better understand the complex interaction between maize and F. verticillioides ., (Copyright © 2020 Stagnati et al.)

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., (Copyright © 2020 Ramstein et al.)

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.

Understanding the evolutionary capacity of populations to adapt to novel environments is one of the major pursuits in genetics. Moreover, for plant breeding, maladaptation is the foremost barrier to capitalizing on intraspecific variation in order to develop new breeds for future climate scenarios in agriculture. Using a unique study design, we simultaneously dissected the population and quantitative genomic basis of short-term evolution in a tropical landrace of maize that was translocated to a temperate environment and phenotypically selected for adaptation in flowering time phenology. Underlying 10 generations of directional selection, which resulted in a 26-day mean decrease in female-flowering time, [Formula: see text] of the heritable variation mapped to [Formula: see text] of the genome, where, overall, alleles shifted in frequency beyond the boundaries of genetic drift in the expected direction given their flowering time effects. However, clustering these non-neutral alleles based on their profiles of frequency change revealed transient shifts underpinning a transition in genotype-phenotype relationships across generations. This was distinguished by initial reductions in the frequencies of few relatively large positive effect alleles and subsequent enrichment of many rare negative effect alleles, some of which appear to represent allelic series. With these genomic shifts, the population reached an adapted state while retaining [Formula: see text] of the standing molecular marker variation in the founding population. Robust selection and association mapping tests highlighted several key genes driving the phenotypic response to selection. Our results reveal the evolutionary dynamics of a finite polygenic architecture conditioning a capacity for rapid environmental adaptation in maize., (Copyright © 2019 by the Genetics Society of America.)

Southern Leaf Blight, Northern Leaf Blight, and Gray Leaf Spot, caused by ascomycete fungi, are among the most important foliar diseases of maize worldwide. Previously, disease resistance quantitative trait loci (QTL) for all three diseases were identified in a connected set of chromosome segment substitution line (CSSL) populations designed for the identification of disease resistance QTL. Some QTL for different diseases co-localized, indicating the presence of multiple disease resistance (MDR) QTL. The goal of this study was to perform an independent test of several of the MDR QTL identified to confirm their existence and derive a more precise estimate of allele additive and dominance effects. Twelve F 2:3 family populations were produced, in which selected QTL were segregating in an otherwise uniform genetic background. The populations were assessed for each of the three diseases in replicated trials and genotyped with markers previously associated with disease resistance. Pairwise phenotypic correlations across all the populations for resistance to the three diseases ranged from 0.2 to 0.3 and were all significant at the alpha level of 0.01. Of the 44 QTL tested, 16 were validated (identified at the same genomic location for the same disease or diseases) and several novel QTL/disease associations were found. Two MDR QTL were associated with resistance to all three diseases. This study identifies several potentially important MDR QTL and demonstrates the importance of independently evaluating QTL effects following their initial identification., (Copyright © 2019 Martins et al.)

Key Message: The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.

The process of evolution under domestication has been studied using phylogenetics, population genetics-genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance-covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Improved capacity of genomics and biotechnology has greatly enhanced genetic studies in different areas. Genomic selection exploits the genotype-to-phenotype relationship at the whole-genome level and is being implemented in many crops. Here we show that design-thinking and data-mining techniques can be leveraged to optimize genomic prediction of hybrid performance. We phenotyped a set of 276 maize hybrids generated by crossing founder inbreds of nested association mapping populations for flowering time, ear height, and grain yield. With 10 296 310 SNPs available from the parental inbreds, we explored the patterns of genomic relationships and phenotypic variation to establish training samples based on clustering, graphic network analysis, and genetic mating scheme. Our analysis showed that training set designs outperformed random sampling and earlier methods that either minimize the mean of prediction error variance or maximize the mean of generalized coefficient of determination. Additional analyses of 2556 wheat hybrids from an early-stage hybrid breeding system and 1439 rice hybrids from an established hybrid breeding system validated the approaches. Together, we demonstrated that effective genomic prediction models can be established with a training set 2%-13% of the size of the whole set, enabling an efficient exploration of enormous inference space of genetic combinations., (Copyright © 2019 The Author. Published by Elsevier Inc. All rights reserved.)

Fusarium verticillioides infects maize, causing ear rot, yield loss and contamination by fumonisin mycotoxins. The fungus can be transmitted via kernels and cause systemic infection in maize. Maize resistance to the fungus may occur at different developmental stages, from seedling to maturity. Resistance during kernel germination is part of the plant-pathogen interaction and so far this aspect has not been investigated. In the present study, a genome wide association study (GWAS) of resistance to Fusarium during the seedling developmental stage was conducted in a maize diversity panel using 226,446 SNP markers. Seedling germination and disease phenotypes were scored on artificially inoculated kernels using the rolled towel assay. GWAS identified 164 SNPs significantly associated with the traits examined. Four SNPs were associated with disease severity score after inoculation, 153 were associated with severity in asymptomatic kernels and 7 with the difference between the severity ratings in inoculated and non-inoculated seeds. A set of genes containing or physically near the significant SNPs were identified as candidates for Fusarium resistance at the seedling stage. Functional analysis revealed that many of these genes are directly involved in plant defense against pathogens and stress responses, including transcription factors, chitinase, cytochrome P450, and ubiquitination proteins. In addition, 25 genes were found in high linkage disequilibrium with the associated SNPs identified by GWAS and four of them directly involved in disease resistance. These findings contribute to understanding the complex system of maize- F. verticillioides and may improve genomic selection for Fusarium resistance at the seedling stage., (Copyright © 2019 Stagnati et al.)

The fungus Fusarium verticillioides can infect maize ears, causing Fusarium ear rot (FER) and contaminating the grain with fumonisins (FUM), which are harmful to humans and animals. Breeding for resistance to FER and FUM and post-harvest sorting of grain are two strategies for reducing FUM in the food system. Kernel and cob tissues have been previously associated with differential FER and FUM. Four recombinant inbred line families from the maize nested associated mapping population were grown and inoculated with F. verticillioides across four environments, and we evaluated the kernels for external and internal infection severity as well as FUM contamination. We also employed publicly available phenotypes on innate ear morphology to explore genetic relationships between ear architecture and resistance to FER and FUM. The four families revealed wide variation in external symptomatology at the phenotypic level. Kernel bulk density under inoculation was an accurate indicator of FUM levels. Genotypes with lower kernel density-under both inoculated and uninoculated conditions-and larger cobs were more susceptible to infection and FUM contamination. Quantitative trait locus (QTL) intervals could be classified as putatively resistance-specific and putatively shared for ear and resistance traits. Both types of QTL mapped in this study had substantial overlap with previously reported loci for resistance to FER and FUM. Ear morphology may be a component of resistance to F. verticillioides infection and FUM accumulation.

The fungus Fusarium verticillioides can infect maize ears, contaminating the grain with mycotoxins, including fumonisins. This global public health threat can be managed by breeding maize varieties that are resistant to colonization by F. verticillioides and by sorting grain after harvest to reduce fumonisin levels in food systems. Here, we employed two F. verticillioides inoculation techniques representing distinct infection pathways to dissect ear symptomatology and morphological resistance mechanisms in a diverse panel of maize inbred lines. The "point" method involved penetrating the ear with a spore-coated toothpick and the "inundative" method introduced a liquid spore suspension under the husk of the ear. We evaluated quantitative and qualitative indicators of external and internal symptom severity as low-cost proxies for fumonisin contamination, and found that kernel bulk density was predictive of fumonisin levels (78 to 84% sensitivity; 97 to 99% specificity). Inundative inoculation resulted in greater disease severity and fumonisin contamination than point inoculation. We also found that the two inoculation methods implicated different ear tissues in defense, with cob morphology being a more important component of resistance under point inoculation. Across both inoculation methods, traits related to cob size were positively associated with disease severity and fumonisin content. Our work demonstrates that (i) the use of diverse modes of inoculation is necessary for combining complementary mechanisms of genetic resistance, (ii) kernel bulk density can be used effectively as a proxy for fumonisin levels, and (iii) trade-offs may exist between yield potential and resistance to fumonisin contamination.

Two distinct variations in the promoter of a key flowering time gene were selected during the spread of maize from its tropical origin to northern North America., (Published by Elsevier Ltd.)

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F's genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available., Data Description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed.

Abiotic stress tolerance traits are often complex and recalcitrant targets for conventional breeding improvement in many crop species. This study evaluated the potential of genomic selection to predict water-soluble carbohydrate concentration (WSCC), an important drought tolerance trait, in wheat under field conditions. A panel of 358 varieties and breeding lines constrained for maturity was evaluated under rainfed and irrigated treatments across two locations and two years. Whole-genome marker profiles and factor analytic mixed models were used to generate genomic estimated breeding values (GEBVs) for specific environments and environment groups. Additive genetic variance was smaller than residual genetic variance for WSCC, such that genotypic values were dominated by residual genetic effects rather than additive breeding values. As a result, GEBVs were not accurate predictors of genotypic values of the extant lines, but GEBVs should be reliable selection criteria to choose parents for intermating to produce new populations. The accuracy of GEBVs for untested lines was sufficient to increase predicted genetic gain from genomic selection per unit time compared to phenotypic selection if the breeding cycle is reduced by half by the use of GEBVs in off-season generations. Further, genomic prediction accuracy depended on having phenotypic data from environments with strong correlations with target production environments to build prediction models. By combining high-density marker genotypes, stress-managed field evaluations, and mixed models that model simultaneously covariances among genotypes and covariances of complex trait performance between pairs of environments, we were able to train models with good accuracy to facilitate genetic gain from genomic selection., (Copyright © 2018 Ovenden et al.)

Genomic scans for genes that show the signature of past selection have been widely applied to a number of species and have identified a large number of selection candidate genes. In cultivated maize (Zea mays ssp. mays) selection scans have identified several hundred candidate domestication genes by comparing nucleotide diversity and differentiation between maize and its progenitor, teosinte (Z. mays ssp. parviglumis). One of these is a gene called zea agamous-like1 (zagl1), a MADS-box transcription factor, that is known for its function in the control of flowering time. To determine the trait(s) controlled by zagl1 that was (were) the target(s) of selection during maize domestication, we created a set of recombinant chromosome isogenic lines that differ for the maize versus teosinte alleles of zagl1 and which carry cross-overs between zagl1 and its neighbor genes. These lines were grown in a randomized trial and scored for flowering time and domestication related traits. The results indicated that the maize versus teosinte alleles of zagl1 affect flowering time as expected, as well as multiple traits related to ear size with the maize allele conferring larger ears with more kernels. Our results suggest that zagl1 may have been under selection during domestication to increase the size of the maize ear.

Adaptation of crops to climate change has motivated an increasing interest in the potential value of novel traits from wild species; maize wild relatives, the teosintes, harbor traits that may be useful to maize breeding. To study the ecogeographic distribution of teosinte we constructed a robust database of 2363 teosinte occurrences from published sources for the period 1842-2016. A geographical information system integrating 216 environmental variables was created for Mexico and Central America and was used to characterize the environment of each teosinte occurrence site. The natural geographic distribution of teosinte extends from the Western Sierra Madre of the State of Chihuahua, Mexico to the Pacific coast of Nicaragua and Costa Rica, including practically the entire western part of Mesoamerica. The Mexican annuals Zea mays ssp. parviglumis and Zea mays ssp. mexicana show a wide distribution in Mexico, while Zea diploperennis, Zea luxurians, Zea perennis, Zea mays ssp. huehuetenangensis, Zea vespertilio and Zea nicaraguensis had more restricted and distinct ranges, representing less than 20% of the total occurrences. Only 11.2% of teosinte populations are found in Protected Natural Areas in Mexico and Central America. Ecogeographical analysis showed that teosinte can cope with extreme levels of precipitation and temperatures during growing season. Modelling teosinte geographic distribution demonstrated congruence between actual and potential distributions; however, some areas with no occurrences appear to be within the range of adaptation of teosintes. Field surveys should be prioritized to such regions to accelerate the discovery of unknown populations. Potential areas for teosintes Zea mays ssp. mexicana races Chalco, Nobogame, and Durango, Zea mays ssp. huehuetenangensis, Zea luxurians, Zea diploperennis and Zea nicaraguensis are geographically separated; however, partial overlapping occurs between Zea mays ssp. parviglumis and Zea perennis, between Zea mays ssp. parviglumis and Zea diploperennis, and between Zea mays ssp. mexicana race Chalco and Zea mays ssp. mexicana race Central Plateau. Assessing priority of collecting for conservation showed that permanent monitoring programs and in-situ conservation projects with participation of local farmer communities are critically needed; Zea mays ssp. mexicana (races Durango and Nobogame), Zea luxurians, Zea diploperennis, Zea perennis and Zea vespertilio should be considered as the highest priority taxa.

Key Message: Water-soluble carbohydrate accumulation can be selected in wheat breeding programs with consideration of genetic × environmental interactions and relationships with other important characteristics such as relative maturity and nitrogen concentration, although the correlation between WSC traits and grain yield is low and inconsistent. The potential to increase the genetic capacity for water-soluble carbohydrate (WSC) accumulation is an opportunity to improve the drought tolerance capability of rainfed wheat varieties, particularly in environments where terminal drought is a significant constraint to wheat production. A population of elite breeding germplasm was characterized to investigate the potential for selection of improved WSC concentration and total amount in water deficit and well-watered environments. Accumulation of WSC involves complex interactions with other traits and the environment. For both WSC concentration (WSCC) and total WSC per area (WSCA), strong genotype × environment interactions were reflected in the clear grouping of experiments into well-watered and water deficit environment clusters. Genetic correlations between experiments were high within clusters. Heritability for WSCC was larger than for WSCA, and significant associations were observed in both well-watered and water deficit experiment clusters between the WSC traits and nitrogen concentration, tillering, grains per m 2 , and grain size. However, correlations between grain yield and WSCC or WSCA were weak and variable, suggesting that selection for these traits is not a better strategy for improving yield under drought than direct selection for yield.

Improving water-use efficiency by incorporating drought avoidance traits into new wheat varieties is an important objective for wheat breeding in water-limited environments. This study uses genome wide association studies (GWAS) to identify candidate loci for water-soluble carbohydrate accumulation-an important drought-avoidance characteristic in wheat. Phenotypes from a multi-environment trial with experiments differing in water availability and separate single nucleotide polymorphism (SNP) and diversity arrays technology (DArT) marker sets were used to perform the analyses. Significant associations for water-soluble carbohydrate accumulation were identified on chromosomes 1A, 1B, 1D, 2D, and 4A. Notably, these loci did not collocate with the major loci identified for relative maturity. Loci on chromosome 1D collocated with markers previously associated with the high molecular weight glutenin Glu-D1 locus. Genetic × environmental interactions impacted the results strongly, with significant associations for carbohydrate accumulation identified only in the water-deficit experiments. The markers associated with carbohydrate accumulation may be useful for marker-assisted selection of drought tolerance in wheat., (Copyright © 2017 Ovenden et al.)

Genome-wide association mapping using dense marker sets has identified some nucleotide variants affecting complex traits that have been validated with fine-mapping and functional analysis. However, many sequence variants associated with complex traits in maize have small effects and low repeatability. In contrast to genome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporating information from all available markers, rather than modeling effects of individual loci. We considered methods to integrate results of GWASs into GP models in the context of multiple interconnected families. We compared association tests based on a biallelic additive model constraining the effect of a single-nucleotide polymorphism (SNP) to be equal across all families in which it segregates to a model in which the effect of a SNP can vary across families. Association SNPs were then included as fixed effects into a GP model that also included the random effects of the whole genome background. Simulation studies revealed that the effectiveness of this joint approach depends on the extent of polygenicity of the traits. Congruent with this finding, cross-validation studies indicated that GP including the fixed effects of the most significantly associated SNPs along with the polygenic background was more accurate than the polygenic background model alone for moderately complex but not highly polygenic traits measured in the maize nested association mapping population. Individual SNPs with strong and robust association signals can effectively improve GP. Our approach provides a new integrative modeling approach for both reliable gene discovery and robust GP.

Leaf shape varies spectacularly among plants. Leaves are the primary source of photoassimilate in crop plants, and understanding the genetic basis of variation in leaf morphology is critical to improving agricultural productivity. Leaf shape played a unique role in cotton improvement, as breeders have selected for entire and lobed leaf morphs resulting from a single locus, okra (l-D 1 ), which is responsible for the major leaf shapes in cotton. The l-D 1 locus is not only of agricultural importance in cotton, but through pioneering chimeric and morphometric studies, it has contributed to fundamental knowledge about leaf development. Here we show that an HD-Zip transcription factor homologous to the LATE MERISTEM IDENTITY1 (LMI1) gene of Arabidopsis is the causal gene underlying the l-D 1 locus. The classical okra leaf shape allele has a 133-bp tandem duplication in the promoter, correlated with elevated expression, whereas an 8-bp deletion in the third exon of the presumed wild-type normal allele causes a frame-shifted and truncated coding sequence. Our results indicate that subokra is the ancestral leaf shape of tetraploid cotton that gave rise to the okra allele and that normal is a derived mutant allele that came to predominate and define the leaf shape of cultivated cotton. Virus-induced gene silencing (VIGS) of the LMI1-like gene in an okra variety was sufficient to induce normal leaf formation. The developmental changes in leaves conferred by this gene are associated with a photosynthetic transcriptomic signature, substantiating its use by breeders to produce a superior cotton ideotype., Competing Interests: The authors declare no conflict of interest.

Despite the reduction in the price of sequencing, it remains expensive to sequence and assemble whole, complex genomes of multiple samples for population studies, particularly for large genomes like those of many crop species. Enrichment of target genome regions coupled with next generation sequencing is a cost-effective strategy to obtain sequence information for loci of interest across many individuals, providing a less expensive approach to evaluating sequence variation at the population scale. Here we evaluate amplicon-based enrichment coupled with semiconductor sequencing on a validation set consisting of three maize inbred lines, two hybrids and 19 landrace accessions. We report the use of a multiplexed panel of 319 PCR assays that target 20 candidate loci associated with photoperiod sensitivity in maize while requiring 25 ng or less of starting DNA per sample. Enriched regions had an average on-target sequence read depth of 105 with 98% of the sequence data mapping to the maize 'B73' reference and 80% of the reads mapping to the target interval. Sequence reads were aligned to B73 and 1,486 and 1,244 variants were called using SAMtools and GATK, respectively. Of the variants called by both SAMtools and GATK, 30% were not previously reported in maize. Due to the high sequence read depth, heterozygote genotypes could be called with at least 92.5% accuracy in hybrid materials using GATK. The genetic data are congruent with previous reports of high total genetic diversity and substantial population differentiation among maize landraces. In conclusion, semiconductor sequencing of highly multiplexed PCR reactions is a cost-effective strategy for resequencing targeted genomic loci in diverse maize materials., Competing Interests: One author is currently employed by Monsanto Company. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Physiological leaf spotting, or flecking, is a mild-lesion phenotype observed on the leaves of several commonly used maize (Zea mays) inbred lines and has been anecdotally linked to enhanced broad-spectrum disease resistance. Flecking was assessed in the maize nested association mapping (NAM) population, comprising 4,998 recombinant inbred lines from 25 biparental families, and in an association population, comprising 279 diverse maize inbreds. Joint family linkage analysis was conducted with 7,386 markers in the NAM population. Genome-wide association tests were performed with 26.5 million single-nucleotide polymorphisms (SNPs) in the NAM population and with 246,497 SNPs in the association population, resulting in the identification of 18 and three loci associated with variation in flecking, respectively. Many of the candidate genes colocalizing with associated SNPs are similar to genes that function in plant defense response via cell wall modification, salicylic acid- and jasmonic acid-dependent pathways, redox homeostasis, stress response, and vesicle trafficking/remodeling. Significant positive correlations were found between increased flecking, stronger defense response, increased disease resistance, and increased pest resistance. A nonlinear relationship with total kernel weight also was observed whereby lines with relatively high levels of flecking had, on average, lower total kernel weight. We present evidence suggesting that mild flecking could be used as a selection criterion for breeding programs trying to incorporate broad-spectrum disease resistance., (© 2016 American Society of Plant Biologists. All Rights Reserved.)

Strong directional selection occurred during the domestication of maize from its wild ancestor teosinte, reducing its genetic diversity, particularly at genes controlling domestication-related traits. Nevertheless, variability for some domestication-related traits is maintained in maize. The genetic basis of this could be sequence variation at the same key genes controlling maize-teosinte differentiation (due to lack of fixation or arising as new mutations after domestication), distinct loci with large effects, or polygenic background variation. Previous studies permit annotation of maize genome regions associated with the major differences between maize and teosinte or that exhibit population genetic signals of selection during either domestication or postdomestication improvement. Genome-wide association studies and genetic variance partitioning analyses were performed in two diverse maize inbred line panels to compare the phenotypic effects and variances of sequence polymorphisms in regions involved in domestication and improvement to the rest of the genome. Additive polygenic models explained most of the genotypic variation for domestication-related traits; no large-effect loci were detected for any trait. Most trait variance was associated with background genomic regions lacking previous evidence for involvement in domestication. Improvement sweep regions were associated with more trait variation than expected based on the proportion of the genome they represent. Selection during domestication eliminated large-effect genetic variants that would revert maize toward a teosinte type. Small-effect polygenic variants (enriched in the improvement sweep regions of the genome) are responsible for most of the standing variation for domestication-related traits in maize., (Copyright © 2016 by the Genetics Society of America.)

A multiparent advanced-generation intercross population of maize has been developed to help plant geneticists identify sequence variants affecting important agricultural traits.

Quantitative trait locus (QTL) models can provide useful insights into trait genetic architecture because of their straightforward interpretability but are less useful for genetic prediction because of the difficulty in including the effects of numerous small effect loci without overfitting. Tight linkage between markers introduces near collinearity among marker genotypes, complicating the detection of QTL and estimation of QTL effects in linkage mapping, and this problem is exacerbated by very high density linkage maps. Here we developed a thinning and aggregating (TAGGING) method as a new ensemble learning approach to QTL mapping. TAGGING reduces collinearity problems by thinning dense linkage maps, maintains aspects of marker selection that characterize standard QTL mapping, and by ensembling, incorporates information from many more markers-trait associations than traditional QTL mapping. The objective of TAGGING was to improve prediction power compared with QTL mapping while also providing more specific insights into genetic architecture than genome-wide prediction models. TAGGING was compared with standard QTL mapping using cross validation of empirical data from the maize (Zea mays L.) nested association mapping population. TAGGING-assisted QTL mapping substantially improved prediction ability for both biparental and multifamily populations by reducing both the variance and bias in prediction. Furthermore, an ensemble model combining predictions from TAGGING-assisted QTL and infinitesimal models improved prediction abilities over the component models, indicating some complementarity between model assumptions and suggesting that some trait genetic architectures involve a mixture of a few major QTL and polygenic effects., (Copyright © 2015 Bian and Holland.)

Quantitative trait locus (QTL) mapping has been used to dissect the genetic architecture of complex traits and predict phenotypes for marker-assisted selection. Many QTL mapping studies in plants have been limited to one biparental family population. Joint analysis of multiple biparental families offers an alternative approach to QTL mapping with a wider scope of inference. Joint-multiple population analysis should have higher power to detect QTL shared among multiple families, but may have lower power to detect rare QTL. We compared prediction ability of single-family and joint-family QTL analysis methods with fivefold cross-validation for 6 diverse traits using the maize nested association mapping population, which comprises 25 biparental recombinant inbred families. Joint-family QTL analysis had higher mean prediction abilities than single-family QTL analysis for all traits at most significance thresholds, and was always better at more stringent significance thresholds. Most robust QTL (detected in >50% of data samples) were restricted to one family and were often not detected at high frequency by joint-family analysis, implying substantial genetic heterogeneity among families for complex traits in maize. The superior predictive ability of joint-family QTL models despite important genetic differences among families suggests that joint-family models capture sufficient smaller effect QTL that are shared across families to compensate for missing some rare large-effect QTL.

Background: Corn borers are the primary maize pest; their feeding on the pith results in stem damage and yield losses. In this study, we performed a genome-wide association study (GWAS) to identify SNPs associated with resistance to Mediterranean corn borer in a maize diversity panel using a set of more than 240,000 SNPs., Results: Twenty five SNPs were significantly associated with three resistance traits: 10 were significantly associated with tunnel length, 4 with stem damage, and 11 with kernel resistance. Allelic variation at each significant SNP was associated with from 6 to 9% of the phenotypic variance. A set of genes containing or physically close to these SNPs are proposed as candidate genes for borer resistance, supported by their involvement in plant defense-related mechanisms in previously published evidence. The linkage disequilibrium decayed (r(2) < 0.10) rapidly within short distance, suggesting high resolution of GWAS associations., Conclusions: Most of the candidate genes found in this study are part of signaling pathways, others act as regulator of expression under biotic stress condition, and a few genes are encoding enzymes with antibiotic effect against insects such as the cystatin1 gene and the defensin proteins. These findings contribute to the understanding the complex relationship between plant-insect interactions.

The coevolution of plants and microbes has shaped plant mechanisms that detect and repel pathogens. A newly identified plant gene confers partial resistance to a fungal pathogen not by preventing initial infection but by limiting its spread through the plant.

Crop species exhibit an astounding capacity for environmental adaptation, but genetic bottlenecks resulting from intense selection for adaptation and productivity can lead to a genetically vulnerable crop. Improving the genetic resiliency of temperate maize depends upon the use of tropical germplasm, which harbors a rich source of natural allelic diversity. Here, the adaptation process was studied in a tropical maize population subjected to 10 recurrent generations of directional selection for early flowering in a single temperate environment in Iowa, USA. We evaluated the response to this selection across a geographical range spanning from 43.05° (WI) to 18.00° (PR) latitude. The capacity for an all-tropical maize population to become adapted to a temperate environment was revealed in a marked fashion: on average, families from generation 10 flowered 20 days earlier than families in generation 0, with a nine-day separation between the latest generation 10 family and the earliest generation 0 family. Results suggest that adaptation was primarily due to selection on genetic main effects tailored to temperature-dependent plasticity in flowering time. Genotype-by-environment interactions represented a relatively small component of the phenotypic variation in flowering time, but were sufficient to produce a signature of localized adaptation that radiated latitudinally, in partial association with daylength and temperature, from the original location of selection. Furthermore, the original population exhibited a maladaptive syndrome including excessive ear and plant heights along with later flowering; this was reduced in frequency by selection for flowering time.