Transcriptome Profiling Provides Insights into the Early Development of Tiller Buds in High- and Low-Tillering Orchardgrass Genotypes.

Orchardgrass (Dactylis glomerata L.) is among the most economically important perennial cool-season grasses, and is considered an excellent hay, pasture, and silage crop in temperate regions worldwide. Tillering is a vital feature that dominates orchardgrass regeneration and biomass yield. However, transcriptional dynamics underlying early-stage bud development in high- and low-tillering orchardgrass genotypes are unclear. Thus, this study assessed the photosynthetic parameters, the partially essential intermediate biomolecular substances, and the transcriptome to elaborate the early-stage profiles of tiller development. Photosynthetic efficiency and morphological development significantly differed between high- (AKZ-NRGR667) and low-tillering genotypes (D20170203) at the early stage after tiller formation. The 206.41 Gb of high-quality reads revealed stage-specific differentially expressed genes (DEGs), demonstrating that signal transduction and energy-related metabolism pathways, especially photosynthetic-related processes, influence tiller induction and development. Moreover, weighted correlation network analysis (WGCNA) and functional enrichment identified distinctively co-expressed gene clusters and four main regulatory pathways, including chlorophyll, lutein, nitrogen, and gibberellic acid (GA) metabolism pathways. Therefore, photosynthesis, carbohydrate synthesis, nitrogen efficient utilization, and phytohormone signaling pathways are closely and intrinsically linked at the transcriptional level. These findings enhance our understanding of tillering in orchardgrass and perennial grasses, providing a new breeding strategy for improving forage biomass yield.

DNA sequence-based mapping and comparative genomics of the St genome of Pseudoroegneria spicata (Pursh) Á. Löve versus wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.).

Bluebunch wheatgrass (referred to as BBWG) [Pseudoroegneria spicata (Pursh) Á. Löve] is an important rangeland Triticeae grass used for forage, conservation, and restoration. This diploid has the basic St genome that occurs also in many polyploid Triticeae species, which serve as a gene reservoir for wheat improvement. Until now, the St genome in diploid species of Pseudoroegneria has not been mapped. Using a double-cross mapping populations, we mapped 230 expressed sequence tag derived simple sequence repeat (EST-SSR) and 3468 genotyping-by-sequencing (GBS) markers to 14 linkage groups (LGs), two each for the seven homologous groups of the St genome. The 227 GBS markers of BBWG that matched those in a previous study helped identify the unclassified seven LGs of the St sub-genome among 21 LGs of Thinopyrum intermedium (Host) Barkworth & D.R. Dewey. Comparisons of GBS sequences in BBWG to whole-genome sequences in bread wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) revealed that the St genome shared a homology of 35% and 24%, a synteny of 86% and 84%, and a collinearity of 0.85 and 0.86, with ABD and H, respectively. This first-draft molecular map of the St genome will be useful in breeding cereal and forage crops.

Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models.

Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures.

Exploiting Linkage Disequilibrium for Ultrahigh-Dimensional Genome-Wide Data with an Integrated Statistical Approach.

Genome-wide data with millions of single-nucleotide polymorphisms (SNPs) can be highly correlated due to linkage disequilibrium (LD). The ultrahigh dimensionality of big data brings unprecedented challenges to statistical modeling such as noise accumulation, the curse of dimensionality, computational burden, spurious correlations, and a processing and storing bottleneck. The traditional statistical approaches lose their power due to [Formula: see text] (n is the number of observations and p is the number of SNPs) and the complex correlation structure among SNPs. In this article, we propose an integrated distance correlation ridge regression (DCRR) approach to accommodate the ultrahigh dimensionality, joint polygenic effects of multiple loci, and the complex LD structures. Initially, a distance correlation (DC) screening approach is used to extensively remove noise, after which LD structure is addressed using a ridge penalized multiple logistic regression (LRR) model. The false discovery rate, true positive discovery rate, and computational cost were simultaneously assessed through a large number of simulations. A binary trait of Arabidopsis thaliana, the hypersensitive response to the bacterial elicitor AvrRpm1, was analyzed in 84 inbred lines (28 susceptibilities and 56 resistances) with 216,130 SNPs. Compared to previous SNP discovery methods implemented on the same data set, the DCRR approach successfully detected the causative SNP while dramatically reducing spurious associations and computational time.

The maize unstable factor for orange1 is a dominant epigenetic modifier of a tissue specifically silent allele of pericarp color1.

We have characterized Unstable factor for orange1 (Ufo1), a dominant, allele-specific modifier of expression of the maize pericarp color1 (p1) gene. The p1 gene encodes an Myb-homologous transcriptional activator of genes required for biosynthesis of red phlobaphene pigments. The P1-wr allele specifies colorless kernel pericarp and red cobs, whereas Ufo1 modifies P1-wr expression to confer pigmentation in kernel pericarp, as well as vegetative tissues, which normally do not accumulate significant amounts of phlobaphene pigments. In the presence of Ufo1, P1-wr transcript levels and transcription rate are increased in kernel pericarp. The P1-wr allele contains approximately six p1 gene copies present in a hypermethylated and multicopy tandem array. In P1-wr Ufo1 plants, methylation of P1-wr DNA sequences is reduced, whereas the methylation state of other repetitive genomic sequences was not detectably affected. The phenotypes produced by the interaction of P1-wr and Ufo1 are unstable, exhibiting somatic mosaicism and variable penetrance. Moreover, the changes in P1-wr expression and methylation are not heritable: meiotic segregants that lack Ufo1 revert to the normal P1-wr expression and methylation patterns. These results demonstrate the existence of a class of modifiers of gene expression whose effects are associated with transient changes in DNA methylation of specific loci.