Fruit and vegetable intake is inversely correlated with risks for several chronic diseases in humans. Phytochemicals, and in particular, phenolic compounds, present in plant foods may be partly responsible for these health benefits through a variety of mechanisms. Since environmental factors play a role in a plant's production of secondary metabolites, it was hypothesized that an organic agricultural production system would increase phenolic levels. Cultivars of leaf lettuce, collards, and pac choi were grown either on organically certified plots or on adjacent conventional plots. Nine prominent phenolic agents were quantified by HPLC, including phenolic acids (e. g. caffeic acid and gallic acid) and aglycone or glycoside flavonoids (e. g. apigenin, kaempferol, luteolin, and quercetin). Statistically, we did not find significant higher levels of phenolic agents in lettuce and collard samples grown organically. The total phenolic content of organic pac choi samples as measured by the Folin-Ciocalteu assay, however, was significantly higher than conventional samples (p < 0.01), and seemed to be associated with a greater attack the plants in organic plots by flea beetles. These results indicated that although organic production method alone did not enhance biosynthesis of phytochemicals in lettuce and collards, the organic system provided an increased opportunity for insect attack, resulting in a higher level of total phenolic agents in pac choi.

The innovative RTM-GWAS procedure provides a relatively thorough detection of QTL and their multiple alleles for germplasm population characterization, gene network identification, and genomic selection strategy innovation in plant breeding. The previous genome-wide association studies (GWAS) have been concentrated on finding a handful of major quantitative trait loci (QTL), but plant breeders are interested in revealing the whole-genome QTL-allele constitution in breeding materials/germplasm (in which tremendous historical allelic variation has been accumulated) for genome-wide improvement. To match this requirement, two innovations were suggested for GWAS: first grouping tightly linked sequential SNPs into linkage disequilibrium blocks (SNPLDBs) to form markers with multi-allelic haplotypes, and second utilizing two-stage association analysis for QTL identification, where the markers were preselected by single-locus model followed by multi-locus multi-allele model stepwise regression. Our proposed GWAS procedure is characterized as a novel restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS, https://github.com/njau-sri/rtm-gwas ). The Chinese soybean germplasm population (CSGP) composed of 1024 accessions with 36,952 SNPLDBs (generated from 145,558 SNPs, with reduced linkage disequilibrium decay distance) was used to demonstrate the power and efficiency of RTM-GWAS. Using the CSGP marker information, simulation studies demonstrated that RTM-GWAS achieved the highest QTL detection power and efficiency compared with the previous procedures, especially under large sample size and high trait heritability conditions. A relatively thorough detection of QTL with their multiple alleles was achieved by RTM-GWAS compared with the linear mixed model method on 100-seed weight in CSGP. A QTL-allele matrix (402 alleles of 139 QTL × 1024 accessions) was established as a compact form of the population genetic constitution. The 100-seed weight QTL-allele matrix was used for genetic characterization, candidate gene prediction, and genomic selection for optimal crosses in the germplasm population.

SummaryNatural antisense transcripts (NATs) are commonly observed in eukaryotic genomes, but only a limited number of such genes have been identified as being involved in gene regulation in plants. In this research, we investigated the function of small RNA derived from a NAT in fiber cell development.Using a map-based cloning strategy for the first time in tetraploid cotton, we cloned a naked seed mutant gene (N 1 ) encoding a MYBMIXTA-like transcription factor 3 (MML3)/GhMYB25-like in chromosome A12, GhMML3_A12, that is associated with fuzz fiber development.The extremely low expression of GhMML3_A12 in N 1 is associated with NAT production, driven by its 3 0 antisense promoter, as indicated by the promoter-driven histochemical staining assay. In addition, small RNA deep sequencing analysis suggested that the bidirectional transcriptions of GhMML3_A12 form double-stranded RNAs and generate 21-22 nt small RNAs. Therefore, in a fiber-specific manner, small RNA derived from the GhMML3_A12 locus can mediate GhMML3_A12 mRNA self-cleavage and result in the production of naked seeds followed by lint fiber inhibition in N 1 plants. The present research reports the first observation of gene-mediated NATs and siRNA directly controlling fiber development in cotton.

Plant U-box (PUB) E3 ubiquitin ligases play important roles in hormone signaling pathways and response to abiotic stresses, but little is known about them in soybean, Glycine max. Here, we identified and characterized 125 PUB genes from the soybean genome, which were classified into eight groups according to their protein domains. Soybean PUB genes (GmPUB genes) are broadly expressed in many tissues and are a little more abundant in the roots than in the other tissues. Nine GmPUB genes, GmPUB1-GmPUB9, showed induced expression patterns by drought, and the expression of GmPUB8 was also induced by exogenous ABA and NaCl. GmPUB8 was localized to post-Golgi compartments, interacting with GmE2 protein as demonstrated by yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) experiments, and showed E3 ubiquitin ligase activity by in vitro ubiquitination assay. Heterogeneous overexpression of GmPUB8 in Arabidopsis showed decreased drought tolerance, enhanced sensitivity with respect to osmotic and salt stress inhibition of seed germination and seedling growth, and inhibited ABA- and mannitol-mediated stomatal closure. Eight drought stress-related genes were less induced in GmPUB8-overexpressing Arabidopsis after drought treatment compared with the wild type and the pub23 mutant. Taken together, our results suggested that GmPUB8 might negatively regulate plant response to drought stress. In addition, Y2H and BiFC showed that GmPUB8 interacted with soybean COL (CONSTANS LIKE) protein. GmPUB8-overexpressing Arabidopsis flowered earlier under middle- and short-day conditions but later under long-day conditions, indicating that GmPUB8 might regulate flowering time in the photoperiod pathway. This study helps us to understand the functions of PUB E3 ubiquitin ligases in soybean.

Genomic mapping of complex traits across species demands integrating genetics and statistics. In particular, because it is easily interpreted, the R 2 statistic is commonly used in quantitative trait locus (QTL) mapping studies to measure the proportion of phenotypic variation explained by molecular markers. Mixed models with random polygenic effects have been used in complex trait dissection in different species. However, unlike fixed linear regression models, linear mixed models have no well-established R 2 statistic for assessing goodness-of-fit and prediction power. Our objectives were to assess the performance of several R 2 -like statistics for a linear mixed model in association mapping and to identify any such statistic that measures model-data agreement and provides an intuitive indication of QTL effect. Our results showed that the likelihood-ratio-based R 2 (R LR 2 ) satisfies several critical requirements proposed for the R 2 -like statistic. As R LR 2 reduces to the regular R 2 for fixed models without random effects other than residual, it provides a general measure for the effect of QTL in mixed-model association mapping. Moreover, we found that R LR 2 can help explain the overlap between overall population structure modeled as fixed effects and relative kinship modeled though random effects. As both approaches are derived from molecular marker information and are not mutually exclusive, comparing R LR Researchers in many disciplines use linear regression models widely. The R 2 statistic, the coefficient of determination, is one of the most frequently used measures of prediction power and goodness-of-fit for simple linear regression models (Draper and Smith, 1981;Everitt, 2002). In the literature on genetics, researchers often report R 2 values of newly identified genetic loci in addition to effect sizes and P-values (Lettre et al., 2008;Weedon et al., 2008). For nonstandard linear regression models, however, several competing R 2 -like statistics have been proposed to measure prediction power and goodness-of-fit (Buse, 1973;Magee, 1990;Xu, 2003;Kramer, 2005) but have not been used in genetics. Indeed, it is desirable to have a measure for general linear mixed models analogous in some ways to the R 2 of the linear regression model, which has a 'variation explained' interpretation.Association mapping searches the association between genetic markers and complex traits (for example disease susceptibility) based on populations (Hirschhorn and Daly, 2005). It complements linkage analysis in mapping the genetic basis of complex traits. Mixed models have long been used in genetic research (Henderson, 1984;Lynch and Walsh, 1998), and the mixed-model association mapping methods were developed to account for complex population structure (Meuwissen et al., 2002;Yu et al., 2006;Malosetti et al., 2007). Although statistics like deviance and the Bayesian Information Criterion (BIC) (Schwarz, 1978) can be used to select models (Broman and Speed, 2002;Littell et al., 2006), many researchers desire a R 2 -like stat...