Wheat crops frequently experience a combination of abiotic stresses in the field, but most quantitative trait loci (QTL) studies have focused on the identification of QTLs for traits under single stress field conditions. A recombinant inbred line (RIL) population derived from SeriM82 × Babax was used to map QTLs under well-irrigated, heat, drought, and a combination of heat and drought stress conditions in two years. A total of 477 DNA markers were used to construct linkage groups that covered 1619.6 cM of the genome, with an average distance of 3.39 cM between adjacent markers. Moderate to relatively high heritability estimates (0.60–0.70) were observed for plant height (PHE), grain yield (YLD), and grain per square meter (GM2). The most important QTLs for days to heading (DHE), thousand grain weight (TGW), and YLD were detected on chromosomes 1B, 1D-a, and 7D-b. The prominent QTLs related to canopy temperature were on 3B. Results showed that common QTLs for DHE, YLD, and TGW on 7D-b were validated in heat and drought trials. Three QTLs for chlorophyll content in SPAD unit (on 1A/6B), leaf rolling (ROL) (on 3B/4A), and GM2 (on 1B/7D-b) showed significant epistasis × environment interaction. Six heat- or drought-specific QTLs (linked to 7D-acc/cat-10, 1B-agc/cta-9, 1A-aag/cta-8, 4A-acg/cta-3, 1B-aca/caa-3, and 1B-agc/cta-9 for day to maturity (DMA), SPAD, spikelet compactness (SCOM), TGW, GM2, and GM2, respectively) were stable and validated over two years. The major DHE QTL linked to 7D-acc/cat-10, with no QTL × environment (QE) interaction increased TGW and YLD. This QTL (5.68 ≤ LOD ≤ 10.5) explained up to 19.6% variation in YLD in drought, heat, and combined stress trials. This marker as a candidate could be used for verification in other populations and identifying superior allelic variations in wheat cultivars or its wild progenitors to increase the efficiency of selection of high yielding lines adapted to end-season heat and drought stress conditions.

A doubled haploid (DH) population derived from a cross between the Japanese cultivar ‘Fukuho-kumogi’ and the Israeli wheat line ‘Oligoculm’ was used to map genome regions involved in the expression of grain yield, yield components, and spike features in wheat (Triticum aestivum L). A total of 371 markers (RAPD, SSR, RFLP, AFLP, and two morphological traits) were used to construct the linkage map that covered 4190 cM of wheat genome including 28 linkage groups. The results of composite interval mapping for all studied traits showed that some of the quantitative trait loci (QTL) were stable over experiments conducted in 2004 and 2005. The major QTL located in the Hair–Xpsp2999 interval on chromosome 1A controlled the expression of grains/spike (R²= 12.9% in 2004 and 22.4% in 2005), grain weight/spike (R²= 21.4% in 2004 and 15.8% in 2005), and spike number (R²= 15.6% in 2004 and 5.4% in 2005). The QTL for grain yield located on chromosomes 6A, 6B, and 6D totally accounted for 27.2% and 31.7% of total variation in this trait in 2004 and 2005, respectively. Alleles inherited from ‘Oligoculm’ increased the length of spikes and had decreasing effects on spike number. According to the data obtained in 2005, locus Xgwm261 was associated with a highly significant spike length QTL (R²= 42.33%) and also the major QTL for spikelet compactness (R²= 26.1%).

Coronavirus disease 2019 (COVID-19) represents a major pandemic threat that has spread to more than 212 countries with more than 432,902 recorded deaths and 7,898,442 confirmed cases worldwide so far (on June 14, 2020). It is crucial to investigate the spatial drivers to prevent and control the epidemic of COVID-19. This is the first comprehensive study of COVID-19 in Iran; and it carries out spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread. Four main steps were taken: comparison of Iranian coronavirus data with the global trends, prediction of mortality trends using regression modeling, spatial modeling, risk mapping, and change detection using the random forest (RF) machine learning technique (MLT), and validation of the modeled risk map. The results show that from February 19 to June 14, 2020, the average growth rates (GR) of COVID-19 deaths and the total number of COVID-19 cases in Iran were 1.08 and 1.10, respectively. Based on the World Health Organisation (WHO) data, Iran's fatality rate (deaths/0.1M pop) is 10.53. Other countries' fatality rates were, for comparison, Belgium - 83.32, UK - 61.39, Spain - 58.04, Italy - 56.73, Sweden - 48.28, France - 45.04, USA - 35.52, Canada - 21.49, Brazil - 20.10, Peru - 19.70, Chile - 16.20, Mexico- 12.80, and Germany - 10.58. The fatality rate for China is 0.32 (deaths/0.1M pop). Over time, the heatmap of the infected areas identified two critical time intervals for the COVID-19 outbreak in Iran. The provinces were classified in terms of disease and death rates into a large primary group and three provinces that had critical outbreaks were separate from the others. The heatmap of countries of the world shows that China and Italy were distinguished from other countries in terms of nine viral infection-related parameters. The regression models for death cases showed an increasing trend but with some evidence of turning. A polynomial relationship was identified between the coronavirus infection rate and the province population density. Also, a third-degree polynomial regression model for deaths showed an increasing trend recently, indicating that subsequent measures taken to cope with the outbreak have been insufficient and ineffective. The general trend of deaths in Iran is similar to the world's, but Iran's shows lower volatility. Change detection of COVID-19 risk maps with a random forest model for the period from March 11 to March 18 showed an increasing trend of COVID-19 in Iran's provinces. It is worth noting that using the LASSO MLT to evaluate variables' importance, indicated that the most important variables were the distance from bus stations, bakeries, hospitals, mosques, ATMs (automated teller machines), banks, and the minimum temperature of the coldest month. We believe that this study's risk maps are the primary, fundamental step to take for managing and controlling COVID-19 in Iran and its provinces.

Genotype × environment interaction (GEI) is an important aspect of both plant breeding and the successful introduction of new cultivars. In the present study, additive main effects and multiplicative interactions (AMMI) and genotype (G) main effects and genotype (G) × environment (E) interaction (GGE) biplot analyses were used to identify stable genotypes and to dissect GEI in Plantago. In total, 10 managed field trials were considered as environments to analyze GEI in thirty genotypes belonging to eight Plantago species. Genotypes were evaluated in a drought stress treatment and in normal irrigation conditions at two locations in Shiraz (Bajgah) for three years (2013-2014- 2015) and Kooshkak (Marvdasht, Fars, Iran) for two years (2014–2015). Three traits, seed yield and mucilage yield and content, were measured at each experimental site and in natural Plantago habitats. AMMI2 biplot analyses identified genotypes from several species with higher stability for seed yield and other genotypes with stable mucilage content and yield. P. lanceolata (G26), P. officinalis (G10), P. ovata (G14), P. ampleexcaulis (G11) and P. major (G4) had higher stability for seed yield. For mucilage yield, G21, G18 and G20 (P. psyllium), G1, G2 and G4 (P. major), G9 and G10 (P. officinalis) and P. lanceolata were identified as stable. G13 (P. ovata), G5 and G6 (P. major) and G30 (P. lagopus) had higher stability for mucilage content. No one genotype was found to have high levels of stability for more than one trait but some species had more than one genotype exhibiting stable trait performance. Based on trait variation, GGE biplot analysis identified two representative environments, one for seed yield and one for mucilage yield and content, with good discriminating ability. The identification of stable genotypes and representative environments should assist the breeding of new Plantago cultivars.

Introduction: Biochemical and metabolic processes help plants tolerate the adverse effects of drought. In plants accumulating bioactive compounds, understanding the genetic control of the biosynthesis of biochemical pathways helps the discovery of candidate gene (CG)–metabolite relationships.Methods: The metabolic profile of flowers in 119 rapeseed (Brassica napus) accessions was assessed over two irrigation treatments, one a well-watered (WW) condition and the other a drought stress (DS) regime. We integrated information gained from 52,157 single-nucleotide polymorphism (SNP) markers, metabolites, and transcriptomes to identify linked SNPs and CGs responsible for the genetic control of flower phenolic compounds and regulatory elements.Results: In a genome-wide association study (GWAS), of the SNPs tested, 29,310 SNPs were qualified to assess the population structure and linkage disequilibrium (LD), of which several SNPs for radical scavenging activity (RSA) and total flavanol content (TFLC) were common between the two irrigation conditions and pleiotropic SNPs were found for chlorogenic and coumaric acids content. The principal component analysis (PCA) and stepwise regression showed that chlorogenic acid and epicatechin in WW and myricetin in DS conditions were the most important components for RSA. The hierarchical cluster analysis (HCA) showed that vanillic acid, myricetin, gallic acid, and catechin were closely associated in both irrigation conditions. Analysis of GWAS showed that 60 CGs were identified, of which 18 were involved in stress-induced pathways, phenylpropanoid pathway, and flavonoid modifications. Of the CGs, PAL1, CHI, UGT89B1, FLS3, CCR1, and CYP75B137 contributed to flavonoid biosynthetic pathways. The results of RNA sequencing (RNA-seq) revealed that the transcript levels of PAL, CHI, and CYP75B137 known as early flavonoid biosynthesis-related genes and FLS3, CCR1, and UGT89B1 related to the later stages were increased during drought conditions. The transcription factors (TFs) NAC035 and ERF119 related to flavonoids and phenolic acids were upregulated under drought conditions.Discussion: These findings expand our knowledge on the response mechanisms to DS, particularly regarding the regulation of key phenolic biosynthetic genes in rapeseed. Our data also provided specific linked SNPs for marker-assisted selection (MAS) programs and CGs as resources toward realizing metabolomics-associated breeding of rapeseed.