Whereas breeders have exploited diversity in maize for yield improvements, there has been limited progress in using beneficial alleles in undomesticated varieties. Characterizing standing variation in this complex genome has been challenging, with only a small fraction of it described to date. Using a population genetics scoring model, we identified 55 million SNPs in 103 lines across pre-domestication and domesticated Zea mays varieties, including a representative from the sister genus Tripsacum. We find that structural variations are pervasive in the Z. mays genome and are enriched at loci associated with important traits. By investigating the drivers of genome size variation, we find that the larger Tripsacum genome can be explained by transposable element abundance rather than an allopolyploid origin. In contrast, intraspecies genome size variation seems to be controlled by chromosomal knob content. There is tremendous overlap in key gene content in maize and Tripsacum, suggesting that adaptations from Tripsacum (for example, perennialism and frost and drought tolerance) can likely be integrated into maize.

SUMMARYMaize is an important model species and a major constituent of human and animal diets. It has also emerged as a potential feedstock and model system for bioenergy research due to recent worldwide interest in developing plant biomass-based, carbon-neutral liquid fuels. To understand how the underlying genome sequence results in specific plant phenotypes, information on the temporal and spatial transcription patterns of genes is crucial. Here we present a comprehensive atlas of global transcription profiles across developmental stages and plant organs. We used a NimbleGen microarray containing 80 301 probe sets to profile transcription patterns in 60 distinct tissues representing 11 major organ systems of inbred line B73. Of the 30 892 probe sets representing the filtered B73 gene models, 91.4% were expressed in at least one tissue. Interestingly, 44.5% of the probe sets were expressed in all tissues, indicating a substantial overlap of gene expression among plant organs. Clustering of maize tissues based on global gene expression profiles resulted in formation of groups of biologically related tissues. We utilized this dataset to examine the expression of genes that encode enzymes in the lignin biosynthetic pathway, and found that expansion of distinct gene families was accompanied by divergent, tissue-specific transcription patterns of the paralogs. This comprehensive expression atlas represents a valuable resource for gene discovery and functional characterization in maize.

Genomes at the species level are dynamic, with genes present in every individual (core) and genes in a subset of individuals (dispensable) that collectively constitute the pan-genome. Using transcriptome sequencing of seedling RNA from 503 maize (Zea mays) inbred lines to characterize the maize pan-genome, we identified 8681 representative transcript assemblies (RTAs) with 16.4% expressed in all lines and 82.7% expressed in subsets of the lines. Interestingly, with linkage disequilibrium mapping, 76.7% of the RTAs with at least one single nucleotide polymorphism (SNP) could be mapped to a single genetic position, distributed primarily throughout the nonpericentromeric portion of the genome. Stepwise iterative clustering of RTAs suggests, within the context of the genotypes used in this study, that the maize genome is restricted and further sampling of seedling RNA within this germplasm base will result in minimal discovery. Genome-wide association studies based on SNPs and transcript abundance in the pan-genome revealed loci associated with the timing of the juvenile-to-adult vegetative and vegetative-to-reproductive developmental transitions, two traits important for fitness and adaptation. This study revealed the dynamic nature of the maize pan-genome and demonstrated that a substantial portion of variation may lie outside the single reference genome for a species.

Comprehensive and systematic transcriptome profiling provides valuable insight into biological and developmental processes that occur throughout the life cycle of a plant. We have enhanced our previously published microarray-based gene atlas of maize (Zea mays L.) inbred B73 to now include 79 distinct replicated samples that have been interrogated using RNA sequencing (RNAseq). The current version of the atlas includes 50 original arraybased gene atlas samples, a time-course of 12 stalk and leaf samples postflowering, and an additional set of 17 samples from the maize seedling and adult root system. The entire dataset contains 4.6 billion mapped reads, with an average of 20.5 million mapped reads per biological replicate, allowing for detection of genes with lower transcript abundance. As the new root samples represent key additions to the previously examined tissues, we highlight insights into the root transcriptome, which is represented by 28,894 (73.2%) annotated genes in maize. Additionally, we observed remarkable expression differences across both the longitudinal (four zones) and radial gradients (cortical parenchyma and stele) of the primary root supported by fourfold differential expression of 9353 and 4728 genes, respectively. Among the latter were 1110 genes that encode transcription factors, some of which are orthologs of previously characterized transcription factors known to regulate root development in Arabidopsis thaliana (L.) Heynh., while most are novel, and represent attractive targets for reverse genetics approaches to determine their roles in this important organ. This comprehensive transcriptome dataset is a powerful tool toward understanding maize development, physiology, and phenotypic diversity.

Genotyping-by-sequencing (GBS) approaches provide low-cost, high-density genotype information. However, GBS has unique technical considerations, including a substantial amount of missing data and a nonuniform distribution of sequence reads. The goal of this study was to characterize technical variation using this method and to develop methods to optimize read depth to obtain desired marker coverage. To empirically assess the distribution of fragments produced using GBS, ∼8.69 Gb of GBS data were generated on the Zea mays reference inbred B73, utilizing ApeKI for genome reduction and single-end reads between 75 and 81 bp in length. We observed wide variation in sequence coverage across sites. Approximately 76% of potentially observable cut site-adjacent sequence fragments had no sequencing reads whereas a portion had substantially greater read depth than expected, up to 2369 times the expected mean. The methods described in this article facilitate determination of sequencing depth in the context of empirically defined read depth to achieve desired marker density for genetic mapping studies.

Transcriptome analysis is a valuable tool for identification and characterization of genes and pathways underlying plant growth and development. We previously published a microarray-based maize gene atlas from the analysis of 60 unique spatially and temporally separated tissues from 11 maize organs [1]. To enhance the coverage and resolution of the maize gene atlas, we have analyzed 18 selected tissues representing five organs using RNA sequencing (RNA-Seq). For a direct comparison of the two methodologies, the same RNA samples originally used for our microarray-based atlas were evaluated using RNA-Seq. Both technologies produced similar transcriptome profiles as evident from high Pearson's correlation statistics ranging from 0.70 to 0.83, and from nearly identical clustering of the tissues. RNA-Seq provided enhanced coverage of the transcriptome, with 82.1% of the filtered maize genes detected as expressed in at least one tissue by RNA-Seq compared to only 56.5% detected by microarrays. Further, from the set of 465 maize genes that have been historically well characterized by mutant analysis, 427 show significant expression in at least one tissue by RNA-Seq compared to 390 by microarray analysis. RNA-Seq provided higher resolution for identifying tissue-specific expression as well as for distinguishing the expression profiles of closely related paralogs as compared to microarray-derived profiles. Co-expression analysis derived from the microarray and RNA-Seq data revealed that broadly similar networks result from both platforms, and that co-expression estimates are stable even when constructed from mixed data including both RNA-Seq and microarray expression data. The RNA-Seq information provides a useful complement to the microarray-based maize gene atlas and helps to further understand the dynamics of transcription during maize development.

Transcriptome sequencing is a powerful method for studying global expression patterns in large, complex genomes. Evaluation of sequence-based expression profi les during reproductive development would provide functional annotation to genes underlying agronomic traits. We generated transcriptome profi les for 12 diverse maize (Zea mays L.) reproductive tissues representing male, female, developing seed, and leaf tissues using high throughput transcriptome sequencing. Overall, ~80% of annotated genes were expressed. Comparative analysis between sequence and hybridization-based methods demonstrated the utility of ribonucleic acid sequencing (RNA-seq) for expression determination and differentiation of paralagous genes (~85% of maize genes). Analysis of 4975 gene families across reproductive tissues revealed expression divergence is proportional to family size. In all pairwise comparisons between tissues, 7 (pre-vs. postemergence cobs) to 48% (pollen vs. ovule) of genes were differentially expressed. Genes with expression restricted to a single tissue within this study were identifi ed with the highest numbers observed in leaves, endosperm, and pollen. Coexpression network analysis identifi ed 17 gene modules with complex and shared expression patterns containing many previously described maize genes. The data and analyses in this study provide valuable tools through improved gene annotation, gene family characterization, and a core set of candidate genes to further characterize maize reproductive development and improve grain yield potential.

Maize is rich in genetic and phenotypic diversity. Understanding the sequence, structural, and expression variation that contributes to phenotypic diversity would facilitate more efficient varietal improvement. RNA based sequencing (RNA-seq) is a powerful approach for transcriptional analysis, assessing sequence variation, and identifying novel transcript sequences, particularly in large, complex, repetitive genomes such as maize. In this study, we sequenced RNA from whole seedlings of 21 maize inbred lines representing diverse North American and exotic germplasm. Single nucleotide polymorphism (SNP) detection identified 351,710 polymorphic loci distributed throughout the genome covering 22,830 annotated genes. Tight clustering of two distinct heterotic groups and exotic lines was evident using these SNPs as genetic markers. Transcript abundance analysis revealed minimal variation in the total number of genes expressed across these 21 lines (57.1% to 66.0%). However, the transcribed gene set among the 21 lines varied, with 48.7% expressed in all of the lines, 27.9% expressed in one to 20 lines, and 23.4% expressed in none of the lines. De novo assembly of RNA-seq reads that did not map to the reference B73 genome sequence revealed 1,321 high confidence novel transcripts, of which, 564 loci were present in all 21 lines, including B73, and 757 loci were restricted to a subset of the lines. RT-PCR validation demonstrated 87.5% concordance with the computational prediction of these expressed novel transcripts. Intriguingly, 145 of the novel de novo assembled loci were present in lines from only one of the two heterotic groups consistent with the hypothesis that, in addition to sequence polymorphisms and transcript abundance, transcript presence/absence variation is present and, thereby, may be a mechanism contributing to the genetic basis of heterosis.