QTL mapping and candidate gene analysis of ferrous iron and zinc toxicity tolerance at seedling stage in rice by genome-wide association study

Background:Ferrous iron (Fe) and zinc (Zn) at high concentration in the soil cause heavy metal toxicity andgreatly affect rice yield and quality. To improve rice production, understanding the genetic and molecularresistance mechanisms to excess Fe and Zn in rice is essential. Genome-wide association study (GWAS) is aneffective way to identify loci and favorable alleles governing Fe and Zn toxicty as well as dissect the geneticrelationship between them in a genetically diverse population.Results:A total of 29 and 31 putative QTL affecting shoot height (SH), root length (RL), shoot fresh weight (SFW),shoot dry weight (SDW), root dry weight (RDW), shoot water content (SWC) and shoot ion concentrations (SFe orSZn) were identified at seedling stage in Fe and Zn experiments, respectively. Five toxicity tolerance QTL (qSdw3a,qSdw3b,qSdw12andqSFe5/qSZn5) were detected in the same genomic regions under the two stress conditionsand 22 candidate genes for 10 important QTL regions were also determined by haplotype analyses.Conclusion:Rice plants share partial genetic overlaps of Fe and Zn toxicity tolerance at seedling stage. Candidategenes putatively affecting Fe and Zn toxicity tolerance identified in this study provide valuable information forfuture functional characterization and improvement of rice tolerance to Fe and Zn toxicity by marker-assistedselection or designed QTL pyramiding

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Genome-wide association mapping of aluminum toxicity tolerance and fine mapping of a candidate gene for Nrat1 in rice.

Aluminum (Al) stress is becoming the major limiting factor in crop production in acidic soils. Rice has been reported as the most Al-tolerant crop and the capacity of Al toxicity tolerance is generally evaluated by comparing root growth under Al stress. Here, we performed an association mapping of Al toxicity tolerance using a core collection of 211 indica rice accessions with 700 K high quality SNP data. A total of 21 putative QTL affecting shoot height (SH), root length (RL), shoot fresh weight (SFW), shoot dry weight (SDW), root dry weight (RDW) and shoot water content (SWC) were identified at seedling stage, including three QTL detected only under control condition, eight detected only under Al stress condition, ten simultaneously detected in both control and Al stress conditions, and seven were identified by stress tolerance index of their corresponding traits. Total of 21 candidate genes for 7 important QTL regions associated with Al toxicity tolerance were identified based on combined haplotype analysis and functional annotation, and the most likely candidate gene(s) for each important QTL were also discussed. Also a candidate gene Nrat1 on chromosome 2 was further fine-mapped using BSA-seq and linkage analysis in the F2 population derived from the cross of Al tolerant accession CC105 and super susceptible accession CC180. A new non-synonymous SNP variation was observed at Nrat1 between CC105 and CC180, which resulted in an amino-acid substitution from Ala (A) in CC105 to Asp (D) in CC180. Haplotype analysis of Nrat1 using 327 3K RGP accessions indicated that minor allele variations in aus and indica subpopulations decreased Al toxicity tolerance in rice. The candidate genes identified in this study provide valuable information for improvement of Al toxicity tolerance in rice. Our research indicated that minor alleles are important for QTL mapping and its application in rice breeding when natural gene resources are used

Identification of QTN and candidate genes for Salinity Tolerance at the Germination and Seedling Stages in Rice by Genome-Wide Association Analyses

Abstract To facilitate developing rice varieties tolerant to salt stress, a panel of 208 rice mini-core accessions collected from 25 countries were evaluated for 13 traits associated with salt tolerance (ST) at the germination and seedling stages. The rice panel showed tremendous variation for all measured ST traits and eight accessions showing high levels of ST at either and/or both the germination and seedling stages. Using 395,553 SNP markers covering ~372 Mb of the rice genome and multi-locus mixed linear models, 20 QTN associated with 11 ST traits were identified by GWAS, including 6 QTN affecting ST at the germination stage and 14 QTN for ST at the seedling stage. The integration of bioinformatic with haplotype analyses for the ST QTN lets us identify 22 candidate genes for nine important ST QTN (qGR3, qSNK1, qSNK12, qSNC1, qSNC6, qRNK2, qSDW9a, qSST5 and qSST9). These candidate genes included three known ST genes (SKC1, OsTZF1 and OsEATB) for QTN qSNK1 qSST5 and qSST9. Candidate genes showed significant phenotypic differences in ST traits were detected between or among 2–4 major haplotypes. Thus, our results provided useful materials and genetic information for improving rice ST in future breeding and for molecular dissection of ST in rice

Response of Natural Enemies toward Selective Chemical Insecticides; Used for the Integrated Management of Insect Pests in Cotton Field Plots

Sucking pests of cotton (Gossypium hirsutum L.), such as thrips, or Thrips tabaci Lindeman, and jassid, or Amrasca biguttula Ishida, are among the most threatening insect pests to young cotton plants in Pakistan. New chemical insecticides have been trialed to control their damage in commercial fields. Formulations that show good suppression of these pest’s populations, while sparing bio-controlling agents, are always preferred for obtaining better crop yield. Six different commercially available insecticides, namely Fountain® (fipronil and imidacloprid), Movento Energy® (spirotetramat and imidacloprid), Oshin® (dinotefuran), Concept Plus® (pyriproxyfen, fenpyroximate, and acephate), Maximal® (nitenpyram), and Radiant® (spinetoram) were evaluated in the present study to shortlist the best available insecticide against targeted pests. Harmful impacts of selected insecticides were also evaluated against naturally occurring predators, such as spiders and green lacewings (Chrysoperla carnea). Radiant® (spinetoram) and Movento Energy®, respectively, were best at controlling thrips (with 61% and 56% mortality, respectively) and jassid (62% and 57% mortality, respectively) populations during 2018 and 2019. Radiant® proved itself as the best option and showed minimal harmful effects on both major arthropod predators of cotton fields i.e., spiders (with 8–9% mortality) and green lacewings (with 12–16% mortality). Movento Energy® also showed comparatively less harmful effects (with 15–18% mortality) towards natural predatory fauna of cotton crops, as compared to other selective insecticides used in the study. The findings of current study suggest that the judicious use of target-oriented insecticides can be an efficient and predator-friendly management module in cotton fields. However, the impact of these chemicals is also depended on their timely application, keeping in consideration the ETL of pests and the population of beneficial arthropods

Validation of the major QTL for Al toxicity tolerance by linkage analysis with 11 KASP SNP markers (a), genotype of 93 extremely Al toxicity tolerance (b) and sensitive (c) individuals by SNP1661173, the last three sites were NTC, PR and PS.

<p>Validation of the major QTL for Al toxicity tolerance by linkage analysis with 11 KASP SNP markers (a), genotype of 93 extremely Al toxicity tolerance (b) and sensitive (c) individuals by SNP1661173, the last three sites were NTC, PR and PS.</p

Manhattan plots of QTL for aluminum toxicity tolerance in the whole genome.

<p>Significant SNPs from different conditions are displayed in different colors: control is green, aluminum stress is grey, the ratio of stress to control is red. The associated traits are represented by different symbols: shoot height = triangle up, root length = triangle down, shoot fresh weight = ×, shoot dry weight = square, root dry weight = circle, shoot water content = star.</p

Haplotype analysis in the region of <i>Nrat1</i>.

<p>(a) Haplotypes of <i>Nrat1</i> observed in 327 accessions using 32M SNP data. (b) The performance of root ratio between Al stress to control condition in seven haplotypes.</p

Manhattan plot of important QTL and haplotype analysis of candidate genes related to QTL including <i>qSh1</i> (a), <i>qSh7</i> (b), <i>qSdw2</i> (c), <i>qSdw3a</i> (d), <i>qSdw5</i> (e), <i>qSwc3</i> (f) and <i>qSwc8</i> (g).

<p>Each point was a gene in the region of the QTL. Line and histogram in different colors indicated different conditions: green is control condition, grey is Al stress condition and red is the ratio of the stress to control conditions. Dash line showed the threshold to determine candidate genes. The ** and *** suggested significance of ANOVA at p < 0.01and p < 0.001, respectively. The letter on histogram (a and b) indicated multiple comparisons result at the significant level 0.01. The value in brackets was the number of individuals for each haplotype.</p