Edward S. Buckler

Entering the second century of maize quantitative genetics.

Maize is the most widely grown cereal in the world. In addition to its role in global agriculture, it has also long served as a model organism for genetic research. Maize stands at a genetic crossroads, as it has access to all the tools available for plant genetics but exhibits a genetic architecture more similar to other outcrossing organisms than to self-pollinating crops and model plants. In this review, we summarize recent advances in maize genetics, including the development of powerful populations for genetic mapping and genome-wide association studies (GWAS), and the insights these studies yield on the mechanisms underlying complex maize traits. Most maize traits are controlled by a large number of genes, and linkage analysis of several traits implicates a ‘common gene, rare allele’ model of genetic variation where some genes have many individually rare alleles contributing. Most natural alleles exhibit small effect sizes with little-to-no detectable pleiotropy or epistasis. Additionally, many of these genes are locked away in low-recombination regions that encourage the formation of multi-gene blocks that may underlie maize’s strong heterotic effect. Domestication left strong marks on the maize genome, and some of the differences in trait architectures may be due to different selective pressures over time. Overall, maize’s advantages as a model system make it highly desirable for studying the genetics of outcrossing species, and results from it can provide insight into other such species, including humans.

Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs

One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs), respectively. An extension of the Genomic Best Linear Unbiased Predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross-validation methods were applied to quantify prediction accuracy. Our results showed that: (1) low-density SNPs (~200 markers) were largely sufficient to get good prediction in biparental maize populations for simple traits with moderate-to-high heritability, but GBS outperformed low-density SNPs for complex traits and simple traits evaluated under stress conditions with low-to-moderate heritability; (2) heritability and genetic architecture of target traits affected prediction performance, prediction accuracy of complex traits (grain yield) were consistently lower than those of simple traits (anthesis date and plant height) and prediction accuracy under stress conditions was consistently lower and more variable than under well-watered conditions for all the target traits because of their poor heritability under stress conditions; and (3) the prediction accuracy of GE models was found to be superior to that of non-GE models for complex traits and marginal for simple traits.

Association mapping across numerous traits reveals patterns of functional variation in maize.

Phenotypic variation in natural populations results from a combination of genetic effects, environmental effects, and gene-by-environment interactions. Despite the vast amount of genomic data becoming available, many pressing questions remain about the nature of genetic mutations that underlie functional variation. We present the results of combining genome-wide association analysis of 41 different phenotypes in ∼5,000 inbred maize lines to analyze patterns of high-resolution genetic association among of 28.9 million single-nucleotide polymorphisms (SNPs) and ∼800,000 copy-number variants (CNVs). We show that genic and intergenic regions have opposite patterns of enrichment, minor allele frequencies, and effect sizes, implying tradeoffs among the probability that a given polymorphism will have an effect, the detectable size of that effect, and its frequency in the population. We also find that genes tagged by GWAS are enriched for regulatory functions and are ∼50% more likely to have a paralog than expected by chance, indicating that gene regulation and gene duplication are strong drivers of phenotypic variation. These results will likely apply to many other organisms, especially ones with large and complex genomes like maize.

A Proposal Regarding Best Practices for Validating the Identity of Genetic Stocks and the Effects of Genetic Variants.

Colleagues from the medical field have estimated that up to one-third of cell lines are contaminated with other cell lines or are misidentified; in addition, repeated passaging substantially changes cell line properties (reviewed in [Hughes et al., 2007][1]). The medical community has therefore

A large scale joint analysis of flowering time reveals independent temperate adaptations in maize

Modulating days to flowering is a key mechanism in plants for adapting to new environments, and variation in days to flowering drives population structure by limiting mating. To elucidate the genetic architecture of flowering across maize, a quantitative trait, we mapped flowering in five global populations, a diversity panel (Ames) and four half-sib mapping designs, Chinese (CNNAM), US (USNAM), and European Dent (EUNAM-Dent) and Flint (EUNAM-Flint). Using whole-genome projected SNPs, we tested for joint association using GWAS, resampling GWAS and two regional approaches; Regional Heritability Mapping (RHM) (1, 2) and a novel method, Boosted Regional Heritability Mapping (BRHM). Direct overlap in significant regions detected between populations and flowering candidate genes was limited, but whole-genome cross-population predictive abilities were ≤0.78. Poor predictive ability correlated with increased population differentiation (r = 0.41), unless the parents were broadly sampled from across the North American temperate-tropical germplasm gradient; uncorrected GWAS results from populations with broadly sampled parents were well predicted by temperate-tropical FSTs in machine learning. Machine learning between GWAS results also suggested shared architecture between the American panels and, more distantly, the European panels, but not the Chinese panel. Machine learning approaches can reconcile non-linear relationships, but the combined predictive ability of all of the populations did not significantly enhance prediction of physiological candidates. While the North American-European temperate adaption is well studied, this study suggest independent temperate adaptation evolved in the Chinese panel, most likely in China after 1500, a finding supported by differential gene ontology term enrichment between populations.

RNA polymerase mapping in plants identifies enhancers enriched in causal variants

Promoter-proximal pausing and divergent transcription at promoters and enhancers, which are prominent features in animals, have been reported to be absent in plants based on a study of Arabidopsis thaliana. Here, our PRO-Seq analysis in cassava (Manihot esculenta) identified peaks of transcriptionally-engaged RNA polymerase II (Pol2) at both 5’ and 3’ ends of genes, consistent with paused or slowly-moving Pol2, and divergent transcription at potential intragenic enhancers. A full genome search for bi-directional transcription using an algorithm for enhancer detection developed in mammals (dREG) identified many enhancer candidates. These sites show distinct patterns of methylation and nucleotide variation based on genomic evolutionary rate profiling characteristic of active enhancers. Maize GRO-Seq data showed RNA polymerase occupancy at promoters and enhancers consistent with cassava but not Arabidopsis. Furthermore, putative enhancers in maize identified by dREG significantly overlapped with sites previously identified on the basis of open chromatin, histone marks, and methylation. We show that SNPs within these divergently transcribed intergenic regions predict significantly more variation in fitness and root composition than SNPs in chromosomal segments randomly ascertained from the same intergenic distribution, suggesting a functional importance of these sites on cassava. The findings shed new light on plant transcription regulation and its impact on development and plasticity.

Evolutionarily informed deep learning methods: Predicting transcript abundance from DNA sequence

Deep learning methodologies have revolutionized prediction in many fields, and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two novel approaches that account for evolutionary relatedness in machine learning models: 1) gene-family guided splitting, and 2) ortholog contrasts. The first approach accounts for evolution by constraining the models training and testing sets to include different gene families. The second, uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction, and have prediction auROC values ranging from 0.72 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the novel hypothesis that the 3’ UTR is more important for fine tuning mRNA abundance levels while the 5’ UTR is more important for large scale changes.

De novo Transcriptome Assemblies Reveal Parallel Gene Evolution with Maize after Ancient Polyploidy

Maize genes with protein evidence have higher expression and GC content Tripsacum homologs of maize genes exhibit the same trends as in maize Maize proteome genes have more highly correlated gene expression with Tripsacum Expression dominance for homeologs occurs similarly between maize and Tripsacum homologs A similar set of genes may be decaying into pseudogenes in maize and Tripsacum