Genetic regulation of self-organizing azimuthal canopy orientations and their impacts on light interception in maize.

The efficiency of solar radiation interception contributes to the photosynthetic efficiency of crop plants. Light interception is a function of canopy architecture, including plant density; leaf number, length, width, and angle; and azimuthal canopy orientation. We report on the ability of some maize (Zea mays) genotypes to alter the orientations of their leaves during development in coordination with adjacent plants. Although the upper canopies of these genotypes retain the typical alternate-distichous phyllotaxy of maize, their leaves grow parallel to those of adjacent plants. A genome-wide association study (GWAS) on this parallel canopy trait identified candidate genes, many of which are associated with shade avoidance syndrome, including phytochromeC2. GWAS conducted on the fraction of photosynthetically active radiation (PAR) intercepted by canopies also identified multiple candidate genes, including liguleless1 (lg1), previously defined by its role in ligule development. Under high plant densities, mutants of shade avoidance syndrome and liguleless genes (lg1, lg2, and Lg3) exhibit altered canopy patterns, viz, the numbers of interrow leaves are greatly reduced as compared to those of nonmutant controls, resulting in dramatically decreased PAR interception. In at least the case of lg2, this phenotype is not a consequence of abnormal ligule development. Instead, liguleless gene functions are required for normal light responses, including azimuth canopy re-orientation.

A genetic tradeoff for tolerance to moderate and severe heat stress in US hybrid maize.

Global climate change is increasing both average temperatures and the frequencies of extreme high temperatures. Past studies have documented a strong negative effect of exposures to temperatures >30°C on hybrid maize yields. However, these studies could not disentangle genetic adaptation via artificial selection from changes in agronomic practices. Because most of the earliest maize hybrids are no longer available, side-by-side comparisons with modern hybrids under current field conditions are generally impossible. Here, we report on the collection and curation of 81 years of public yield trial records covering 4,730 maize hybrids, which enabled us to model genetic variation for temperature responses among maize hybrids. We show that selection may have indirectly and inconsistently contributed to the genetic adaptation of maize to moderate heat stress over this time period while preserving genetic variance for continued adaptation. However, our results reveal the existence of a genetic tradeoff for tolerance to moderate and severe heat stress, leading to a decrease in tolerance to severe heat stress over the same time period. Both trends are particularly conspicuous since the mid-1970s. Such a tradeoff poses challenges to the continued adaptation of maize to warming climates due to a projected increase in the frequency of extreme heat events. Nevertheless, given recent advances in phenomics, enviromics, and physiological modeling, our results offer a degree of optimism for the capacity of plant breeders to adapt maize to warming climates, assuming appropriate levels of R&D investment.

New insights into trait introgression with the look-ahead intercrossing strategy.

Trait introgression (TI) can be a time-consuming and costly task that typically requires multiple generations of backcrossing (BC). Usually, the aim is to introduce one or more alleles (e.g. QTLs) from a single donor into an elite recipient, both of which are fully inbred. This article studies the potential advantages of incorporating intercrossing (IC) into TI programs when compared with relying solely on the traditional BC framework. We simulate a TI breeding pipeline using 3 previously proposed selection strategies for the traditional BC scheme and 3 modified strategies that allow IC. Our proposed look-ahead intercrossing method (LAS-IC) combines look-ahead Monte Carlo simulations, intercrossing, and additional selection criteria to improve computational efficiency. We compared the efficiency of the 6 strategies across 5 levels of resource availability considering the generation when the major QTLs have been successfully introduced into the recipient and a desired background recovery rate reached. Simulations demonstrate that the inclusion of intercrossing in a TI program can substantially increase efficiency and the probability of success. The proposed LAS-IC provides the highest probability of success across the different scenarios using fewer resources compared with BC-only strategies.

Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping.

The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited "open" versus. "closed" branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.

Multi-trait Genomic Selection Methods for Crop Improvement.

Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection.

Semiautomated Feature Extraction from RGB Images for Sorghum Panicle Architecture GWAS.

Because structural variation in the inflorescence architecture of cereal crops can influence yield, it is of interest to identify the genes responsible for this variation. However, the manual collection of inflorescence phenotypes can be time consuming for the large populations needed to conduct genome-wide association studies (GWAS) and is difficult for multidimensional traits such as volume. A semiautomated phenotyping pipeline, TIM (Toolkit for Inflorescence Measurement), was developed and used to extract unidimensional and multidimensional features from images of 1,064 sorghum (Sorghum bicolor) panicles from 272 genotypes comprising a subset of the Sorghum Association Panel. GWAS detected 35 unique single-nucleotide polymorphisms associated with variation in inflorescence architecture. The accuracy of the TIM pipeline is supported by the fact that several of these trait-associated single-nucleotide polymorphisms (TASs) are located within chromosomal regions associated with similar traits in previously published quantitative trait locus and GWAS analyses of sorghum. Additionally, sorghum homologs of maize (Zea mays) and rice (Oryza sativa) genes known to affect inflorescence architecture are enriched in the vicinities of TASs. Finally, our TASs are enriched within genomic regions that exhibit high levels of divergence between converted tropical lines and cultivars, consistent with the hypothesis that these chromosomal intervals were targets of selection during modern breeding.

Harnessing Phenotypic Plasticity to Improve Maize Yields.

Plants can produce different phenotypes when exposed to different environments. Understanding the genetic basis of these plastic responses is crucial for crop breeding efforts. We discuss two recent studies that suggest that yield plasticity in maize has been under selection but is controlled by different genes than yield.

FarmCPUpp: Efficient large-scale genomewide association studies.

Genomewide association studies (GWAS) are computationally demanding analyses that use large sample sizes and dense marker sets to discover associations between quantitative trait variation and genetic variants. FarmCPU is a powerful new method for performing GWAS. However, its performance is hampered by details of its implementation and its reliance on the R programming language. In this paper, we present an efficient implementation of FarmCPU, called FarmCPUpp, that retains the R user interface but improves memory management and speed through the use of C++ code and parallel computing.

Distinct genetic architectures for phenotype means and plasticities in Zea mays.

Phenotypic plasticity describes the phenotypic variation of a trait when a genotype is exposed to different environments. Understanding the genetic control of phenotypic plasticity in crops such as maize is of paramount importance for maintaining and increasing yields in a world experiencing climate change. Here, we report the results of genome-wide association analyses of multiple phenotypes and two measures of phenotypic plasticity in a maize nested association mapping (US-NAM) population grown in multiple environments and genotyped with ~2.5 million single-nucleotide polymorphisms. We show that across all traits the candidate genes for mean phenotype values and plasticity measures form structurally and functionally distinct groups. Such independent genetic control suggests that breeders will be able to select semi-independently for mean phenotype values and plasticity, thereby generating varieties with both high mean phenotype values and levels of plasticity that are appropriate for the target performance environments.

Improving Response in Genomic Selection with a Population-Based Selection Strategy: Optimal Population Value Selection.

Genomic selection (GS) identifies individuals for inclusion in breeding programs based on the sum of their estimated marker effects or genomic estimated breeding values (GEBVs). Due to significant correlation between GEBVs and true breeding values, this has resulted in enhanced rates of genetic gain as compared to traditional methods of selection. Three extensions to GS, weighted genomic selection (WGS), optimal haploid value (OHV) selection, and genotype building (GB) selection have been proposed to improve long-term response, and to facilitate the efficient development of doubled haploids. In separate simulation studies, these methods were shown to outperform GS under various assumptions. However, further potential for improvement exists. In this paper, optimal population value (OPV) selection is introduced as selection based on the maximum possible haploid value in a subset of the population. Instead of evaluating the breeding merit of individuals as in GS, WGS, and OHV selection, the proposed method evaluates the breeding merit of a set of individuals as in GB. After testing these selection methods extensively, OPV and GB selection were found to achieve greater responses than GS, WGS, and OHV, with OPV outperforming GB across most percentiles. These results suggest a new paradigm for selection methods in which an individual's value is dependent upon its complementarity with others.