A Genome-Wide Characterization of MicroRNA Genes in Maize

MicroRNAs (miRNAs) are small, non-coding RNAs that play essential roles in plant growth, development, and stress response. We conducted a genome-wide survey of maize miRNA genes, characterizing their structure, expression, and evolution. Computational approaches based on homology and secondary structure modeling identified 150 high-confidence genes within 26 miRNA families. For 25 families, expression was verified by deep-sequencing of small RNA libraries that were prepared from an assortment of maize tissues. PCR–RACE amplification of 68 miRNA transcript precursors, representing 18 families conserved across several plant species, showed that splice variation and the use of alternative transcriptional start and stop sites is common within this class of genes. Comparison of sequence variation data from diverse maize inbred lines versus teosinte accessions suggest that the mature miRNAs are under strong purifying selection while the flanking sequences evolve equivalently to other genes. Since maize is derived from an ancient tetraploid, the effect of whole-genome duplication on miRNA evolution was examined. We found that, like protein-coding genes, duplicated miRNA genes underwent extensive gene-loss, with ∼35% of ancestral sites retained as duplicate homoeologous miRNA genes. This number is higher than that observed with protein-coding genes. A search for putative miRNA targets indicated bias towards genes in regulatory and metabolic pathways. As maize is one of the principal models for plant growth and development, this study will serve as a foundation for future research into the functional roles of miRNA genes

Correction: Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize.

[This corrects the article DOI: 10.1371/journal.pgen.1007019.]

Correction: Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize.

[This corrects the article DOI: 10.1371/journal.pgen.1007019.]

Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize

<div><p>Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding.</p></div

Heterosis and deleterious variants.

<p>(<b>a</b>) Boxplots (median and interquartile range) of percent mid-parent heterosis (MPH). (<b>b</b>) Proportion of deleterious alleles in landraces (LR, green) and elite maize (MZ, blue) lines. (<b>c</b>) The allele frequency of the minor alleles in the multi-species alignment in bins of 0.01 GERP score (including GERP < = 0 sites). (<b>d</b>) The mean GERP score for putatively deleterious sites (GERP >0). Each point represents a 1 Mb window. In (<b>c</b>) and (<b>d</b>) the solid blue and dashed black lines define the best-fit regression line and its 95% confidence interval.</p

Variance explained and degree of dominance (<i>k</i>) of GERP-SNPs for traits <i>per se</i>.

<p><b>(a)</b> Total per-SNP variance explained for grain yield trait <i>per se</i> by GERP-SNPs (red lines) and randomly sampled SNPs (grey beanplots). <b>(b)</b> Density plots of the degree of dominance (<i>k</i>). Extreme values of <i>k</i> were truncated at 2 and -2. <b>(c-e)</b> Linear regressions of additive effects <b>(c)</b>, dominance effects <b>(d)</b>, and degree of dominance <b>(e)</b> of seven traits <i>per se</i> against SNP GERP scores. Solid and dashed lines represent significant and nonsignificant linear regressions, with grey bands representing 95% confidence intervals. Data are only shown for SNPs that explain more than the mean genome-wide per-SNP variance (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007019#sec002" target="_blank">Methods</a> for details).</p

Genomic prediction models incorporating GERP.

<p><b>(a-b)</b> Total phenotypic variance explained for traits <i>per se</i> <b>(a)</b> and heterosis (MPH) (<b>b</b>) under models of additivity (red), dominance (green), and incomplete dominance (blue). <b>(c-d)</b> Beanplots represent prediction accuracy estimated from cross-validation experiments for traits <i>per se</i> <b>(c)</b> and heterosis (MPH) <b>(d)</b> under a model of incomplete dominance. Prediction accuracy using estimated values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the beans indicate prediction accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007019#pgen.1007019.s013" target="_blank">S13 Fig</a>.</p

Improved maize reference genome with single-molecule technologies

Complete and accurate reference genomes and annotations provide fundamental tools for characterization of genetic and functional variation. These resources facilitate the determination of biological processes and support translation of research findings into improved and sustainable agricultural technologies. Many reference genomes for crop plants have been generated over the past decade, but these genomes are often fragmented and missing complex repeat regions. Here we report the assembly and annotation of a reference genome of maize, a genetic and agricultural model species, using single-molecule real-time sequencing and high-resolution optical mapping. Relative to the previous reference genome, our assembly features a 52-fold increase in contig length and notable improvements in the assembly of intergenic spaces and centromeres. Characterization of the repetitive portion of the genome revealed more than 130,000 intact transposable elements, allowing us to identify transposable element lineage expansions that are unique to maize. Gene annotations were updated using 111,000 full-length transcripts obtained by single-molecule real-time sequencing. In addition, comparative optical mapping of two other inbred maize lines revealed a prevalence of deletions in regions of low gene density and maize lineage-specific genes