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Tobacco (Nicotiana tabacum L.) use is the leading cause of preventable death, due to deleterious chemical components and smoke from tobacco products, and therefore reducing harmful chemical components in tobacco is one of the crucial tobacco breeding targets. However, due to complexity of tobacco smoke and unavailability of high-density genetic maps, the genetic architecture of representative hazardous smoke has not been fully dissected. The present study aimed to explore the genetic architecture of nine hazardous component traits of mainstream smoke through QTL mapping using 271 recombinant inbred lines (RILs) derived from K326 and Y3 in multiple environments. The analysis of genotype and genotype by environment interaction (GE) revealed substantially greater heritability over 95% contributed mostly by GE interaction effects. We also observed strong genetic correlations among most studied hazardous smoke traits, with the highest correlation coefficient of 0.84 between carbon monoxide and crotonaldehyde. Based on a published high-density genetic map, a total of 19 novel QTLs were detected for eight traits using a full QTL model, of which 17 QTLs showed significant additive effects, six showed significant additive-by-environment interaction effects, and one pair showed significant epistasis-by-environment interaction effect. Bioinformatics analysis of sequence in QTL region predicted six genes as candidates for four traits, of which Nt21g04598.1, Nt21g04600.1, and Nt21g04601.1 had pleiotropic effects on PHE and TAR.

Gene therapy is a potential treatment for Friedreich ataxia, with multiple programs on the horizon. The purpose of this study was to collect opinions about gene therapy from individuals 14 years or older with Friedreich ataxia or parents/caregivers of Friedreich ataxia patients who were diagnosed as children 17 or younger. Participants were asked to complete a survey after reading brief educational materials regarding gene therapy. Most of the patients captured in this survey have an early-onset (classical) presentation of the disease. Participants expressed urgency in participating in gene therapy clinical trials despite the associated risks. About half of the respondents believed that gene therapy would cease progression or minimize symptoms, whereas nearly one-fourth expected to be cured. The survey also revealed how participants perceive their symptom burden, because a substantial majority reported that balance/walking issues most interfere with their quality of life and would be the symptom they would prioritize treating. Although not statistically significant, more caregivers prioritized treating cardiomyopathy than patients. This study provides valuable information on priorities, beliefs, and expectations regarding gene therapy and serves to guide future gene therapy opinion studies and gene therapy trial design.

Abstract Confidence interval for the difference of two proportions has been studied for decades. Many methods were developed to improve the approximation of the limiting distribution of test statistics, such as the profile likelihood method, the score method, and the Wilson method. For the Wilson interval developed by Beal (Biometrics 43:941, 1987), the approximation of the Z test statistic to the standard normal distribution may be further improved by utilizing the continuity correction, in the observation of anti-conservative intervals from the Wilson interval. We theoretically prove that the Wilson interval is nested in the continuity corrected Wilson interval under mild conditions. We compare the continuity corrected Wilson interval with the commonly used methods with regards to coverage probability, interval width, and mean squared error of coverage probability. The proposed interval has good performance in many configurations. An example from a Phase II cancer trial is used to illustrate the application of these methods.

Protein ubiquitination is common and crucial in cellular functions, however, little is known about how deubiquitinating enzymes (DUBs) reverse regulate the ubiquitination signaling process. PPPDE family proteins are a novel class of deubiquitinating peptidases with demonstrated deubiquitination/deSUMOylating activities. In this study, we identified 10 PPPDE genes from the rice (Oryza sativa L.) genome unevenly distributed on five chromosomes, where most of these members have not been reported to date. Based on the gene structure, the OsPPPDE family consists of three distinct subgroups within the phylogenetic tree. Cis-element analysis identified light/phytohormone-responsive, development, and abiotic stress-related elements in the promoters of OsPPPDE. Furthermore, we conducted and analyzed the transcript abundance of OsPPPDE under various tissues and stresses using the transcriptome data of 352 samples from the Rice Expression Database and GEO datasets. Moreover, OsPPPDE5 showed differential regulation of its transcript abundance during Cd and drought stress. Collinearity and syntenic analysis of 101 PPPDEs and PPPDE-like proteins in 10 plant genomes indicated that this family is evolutionarily conserved. Domestication analysis suggests that OsPPPDEs may contribute to indica–japonica divergence using the data from the 3K Rice Genome Project. Our study provides a foundation for further study on the function and molecular mechanism of the OsPPPDE gene family.

Abstract Background Maize (Zea Mays) is one of the world’s most important crops. Hybrid maize lines resulted a major improvement in corn production in the previous and current centuries. Understanding the genetic mechanisms of the corn production associated traits greatly facilitate the development of superior hybrid varieties. Result In this study, four ear traits associated with corn production of Nested Association Mapping (NAM) population were analyzed using a full genetic model, and further, optimal genotype combinations and total genetic effects of current best lines, superior lines, and superior hybrids were predicted for each of the traits at four different locations. The analysis identified 21–34 highly significant SNPs (−log 10 P > 5), with an estimated total heritability of 37.31–62.34%, while large contributions to variations was due to dominance, dominance-related epistasis, and environmental interaction effects ( $${h}_{D+}^2\hat{=}$$ h D + 2 = ^ 14.06% ~ 49.28%), indicating these factors contributed significantly to phenotypic variations of the ear traits. Environment-specific genetic effects were also discovered to be crucial for maize ear traits. There were four SNPs found for three ear traits: two for ear length and weight, and two for ear row number and length. Using the Enumeration method and the stepwise tuning technique, optimum multi-locus genotype combinations for superior lines were identified based on the information obtained from GWAS. Conclusions Predictions of genetic breeding values showed that different genotype combinations in different geographical regions may be better, and hybrid-line variety breeding with homozygote and heterozygote genotype combinations may have a greater potential to improve ear traits.

Introduction The management of low-risk febrile infants presents a model population for exploring how implicit racial bias promotes inequitable emergency care for children who belong to racial, ethnic and language minority groups. Although widely used clinical standards guide the clinical care of febrile infants, there remains substantial variability in management strategies. Deviations from recommended care may be informed by the physician’s assessment of the family’s values, risk tolerance and access to supportive resources. However, in the fast-paced emergency setting, such assessments may be influenced by implicit racial bias. Despite significant research to inform the clinical care of febrile infants, there is a dearth of knowledge regarding health disparities and clinical guideline implementation. The proposed mixed methods approach will (1) quantify the extent of disparities by race, ethnicity and language proficiency and (2) explore the role of implicit bias in physician–patient communication when caring for this population.Methods and analysis With 42 participating sites from the Pediatric Emergency Medicine Collaborative Research Committee, we will conduct a multicenter, cross-sectional study of low-risk febrile infants treated in the emergency department (ED) and apply multivariable logistic regression to assess the association between (1) race and ethnicity and (2) limited English proficiency with the primary outcome, discharge to home without lumbar puncture or antibiotics. We will concurrently perform an interpretive study using purposive sampling to conduct individual semistructured interviews with (1) minority parents of febrile infants and (2) paediatric ED physicians. We will triangulate or compare perspectives to better elucidate disparities and bias in communication and medical decision-making.Ethics and dissemination This study has been approved by the University of Florida Institutional Review Board. All participating sites in the multicenter analysis will obtain local institutional review board approval. The results of this study will be presented at academic conferences and in peer-reviewed publications.

Interleukin-1 (IL-1) plays a key role in carcinogenesis and several IL-1-targeted therapeutics are under investigation for the treatment of pancreatic ductal adenocarcinoma (PDAC). We sought to broaden our understanding of how the family of IL-1 ligands and receptors impact the tumor immune landscape and patient survival in PDAC. Gene expression data and DNA methylation data for IL1A, IL1B, IL1RN, IL1R1, IL1R2, and IL1RAP was attained from The Cancer Genome Atlas (TCGA) database and cross validated using the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Immune cell-type abundance was estimated using CIBERSORTx. Further confirmatory soluble protein analysis and peripheral blood immunophenotyping were performed on available tissue samples from our institution. 169 PDAC patients and 50 benign pancreatic TCGA-based samples were analyzed. IL1A (p

Genome-wide association study (GWAS) has been a standard approach to discover the genetic determinants underlying complex traits. It is a major challenge in GWAS how to improve analysis power, uncover complex genetic correlation, and reveal gene-gene and gene-environment interactions through integrated analysis of multiple genetically related traits. To combat these challenges, we proposed a mixed linear model-based joint association analysis method for multiple traits, which include epistasis and gene-environment interaction in the mapping model and utilize within-trait variance and between-trait covariance simultaneously; A F-statistics based on Wilks statistics is used to test the significance of each SNP and paired interacted SNPs, each genetic effects of QTS are estimated and tested by the MCMC method based on a QTS full model. Simulations showed that the multi-trait GWAS method could provide increased power in detecting pleiotropic loci affecting more than one trait, and can unbiasedly estimate effects of QTS. To demonstrate the performance of the proposed method, we analyzed four blood lipid traits in Multi-Ethnic Study of Atherosclerosis (MESA) Cohort and two yield-related traits in a rice immortalized F2 dataset. A software package was developed for the proposed method.

Soil organic matter contains more carbon than global vegetation and the atmosphere combined. Gaining access to this source of organic carbon is challenging and requires at least partial removal of polyphenolic and/or soil mineral protections, followed by subsequent enzymatic or chemical cleavage of diverse plant polysaccharides. Soil-feeding animals make significant contributions to the recycling of terrestrial organic matter. Some humivorous earthworms, beetles, and termites, among others, have evolved the ability to mineralize recalcitrant soil organic matter, thereby leading to their tremendous ecological success in the (sub)tropical areas. This ability largely relies on their symbiotic associations with a diverse community of gut microbes. Recent integrative omics studies, including genomics, metagenomics, and proteomics, provide deeper insights into the functions of gut symbionts. In reviewing this literature, we emphasized that understanding how these soil-feeding fauna catabolize soil organic substrates not only reveals the key microbes in the intestinal processes but also uncovers the potential novel enzymes with considerable biotechnological interests.

Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene–gene interaction, gene–environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.

Abstract Background Cottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement. Results A genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids (palmitic, linoleic, oleic, myristic, stearic), respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines. Conclusions This study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy.

Abstract With the development and application of super rice breeding, elite rice hybrids with super high-yielding potential have been widely developed in last decades in China. Xieyou9308 is one of the most famous super hybrid rice varieties. To uncover the genetic mechanism of Xieyou9308’s high yield potential, a recombinant inbred line (RIL) population derived from cross of XieqingzaoB and Zhonghui9308 was re-sequenced and investigated on the grain yield (GYD) and its three component traits, number of panicles per plant (NP), number of filled grains per panicle (NFGP), and grain weight (GW). Unconditional and conditional genome-wide association analysis, based on a linear mixed model with epistasis and gene-environment interaction effects, were conducted, using ~0.7 million identified SNPs. There were six, four, seven, and seven QTSs identified for GYD, NP, NFGP, and GW, respectively, with accumulated explanatory heritability varying from 43.06% to 48.36%; additive by environment interactions were detected for GYD, some minor epistases were detected for NP and NFGP. Further, conditional genetic mapping analysis for GYD given its three components revealed several novel QTSs associated with yield than that were suppressed in our unconditional mapping analysis.

Mitogen-activated protein kinase (MAPK) cascades play central roles in response to biotic and abiotic stresses. However, the mechanisms by which various MAPK members regulate the plant immune response in rice remain elusive. In this article, to characterize the mechanisms, the knock-out and overexpression mutants of OsMPK15 were constructed and the disease resistance was investigated under the various fungal and bacterial inoculations. The knock-out mutant of OsMPK15 resulted in the constitutive expression of pathogenesis-related (PR) genes, increased accumulation of reactive oxygen species (ROS) triggered by the pathogen-associated molecular pattern (PAMP) elicitor chitin, and significantly enhanced the disease resistance to different races of Magnaporthe oryzae and Xanthomonas oryzae pv. oryzae (Xoo), which cause the rice blast and bacterial blight diseases, respectively. On contrary, the expression of PR genes and ROS were down-regulated in the OsMPK15-overexpressing (OsMPK15-OE) lines. Meanwhile, phytohormones such as salicylic acid (SA) and jasmonic acid (JA) were accumulated in the mpk15 mutant lines but decreased in the OsMPK15-OE lines. The expression of SA- and JA-pathway associated genes were significantly upregulated in the mpk15 mutant, whereas it was down regulated in the OsMPK15-OE lines. We conclude that OsMPK15 may negatively regulate the disease resistance through modulating SA- and JA-mediated signaling pathway.

Studies on inheritance of fertility are of great importance in wheat breeding. Although substantial progress has been achieved in molecular characterization of male sterility and fertility restoration recently, little effort has been devoted to female sterility. To identify the gene(s) controlling female sterility in wheat efficiently, an investigation was conducted for the seed setting ratio using a set of F2 populations derived from the cross between a female sterile line XND126 and an elite cultivar Gaocheng 8901. Bulked segregation analysis (BSA) method and recessive class approach were adopted to screen for SSR markers potentially linked to female fertility gene loci in 2005. Out of 1080 SSRs in wheat genome, eight markers on chromosome 2D showed a clear difference between two disparate bulks and small recombination frequency values, suggesting a strong linkage signal to the sterility gene. Based on the candidate linked markers, partial linkage maps were constructed with Mapmaker 3.0 (EXP) instead of whole genome maps, and quantitative trait locus (QTL) mapping was implemented with software QTLNetwork 2.0. A major gene locus designated as taf1, was located on chromosome 2DS. The above result was confirmed by the analysis for 2007 data, and taf1 was identified on the same chromosome 2DS with a confidence interval of 2.4 cM, which could explain 44.99% of phenotypic variation. These results provided fundamental information for fine mapping studies and laid the groundwork for wheat fertility genetic studies. [ABSTRACT FROM AUTHOR]

Two Genetic models (an embryo model and an endosperm model) were proposed for analyzing genetic effects of nuclear genes, cytoplasmic genes, maternal genes, and nuclear–cytoplasmic interaction (NCI) as well as their genotype by environment interaction for quantitative traits of plant seed. In these models, the NCI effects were partitioned into direct additive and dominance NCI components. Mixed linear model approaches were employed for statistical analysis. For both balanced and unbalanced diallel cross designs, Monte Carlo simulations were conducted to evaluate unbiasedness and precision of estimated variance components of these models. The results showed that the proposed methods work well. Random genetic effects were predicted with an adjusted unbiased prediction method. Seed traits (protein content and oil content) of Upland cotton ( Gossypium hirsutum L.) were analyzed as worked examples to demonstrate the use of the models. [ABSTRACT FROM AUTHOR]

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits. [ABSTRACT FROM AUTHOR]

Currently, developing genetic linkage map mostly use the derived-populations from crossing of two homogenous parents, which only covers limited genetic diversity and is inappropriate for some species, such as tobacco with lower diversity in genome. It is very general that there are no sufficient polymorphic markers to construct linkage map and ineffective to conduct marker-assisted selection (MAS) and quantitative trait locus (QTL) mapping based on lower density linkage map. This study proposed a method for developing genetic linkage map based on a four-way cross population. Computer simulation was conducted to investigate the feasibility and effectiveness of the method and a supporting program was designed. The main procedures and features of the proposed method were summarized as follows: 1) estimating genetic distance of any paired markers based on maximum likelihood method; 2) splitting all markers into different groups (linkage group) by cluster analysis based on genetic distance of markers; 3) for each linkage group, two end markers were first determined, then the marker order could be determined by inserting other markers in appropriate position by distance analysis of any three neighboring markers. Monte Carlo simulation showed that the proposed method is feasible, effective, and applicable in other derived populations from crossing of two homogenous parents.

Most important agronomic and quality traits of crops are quantitative in nature. The genetic variations in such traits are usually controlled by sets of genes called quantitative trait loci (QTLs), and the interactions between QTLs and the environment. It is crucial to understand the genetic architecture of complex traits to design efficient strategies for plant breeding. In the present study, a new experimental design and the corresponding statistical method are presented for QTL mapping. The proposed mapping population is composed of double backcross populations derived from backcrossing both homozygous parents to DH (double haploid) or RI (recombinant inbreeding) lines separately. Such an immortal mapping population allows for across-environment replications, and can be used to estimate dominance effects, epistatic effects, and QTL-environment interactions, remedying the drawbacks of a single backcross population. In this method, the mixed linear model approach is used to estimate the positions of QTLs and their various effects including the QTL additive, dominance, and epistatic effects, and QTL-environment interaction effects (QE). Monte Carlo simulations were conducted to investigate the performance of the proposed method and to assess the accuracy and efficiency of its estimations. The results showed that the proposed method could estimate the positions and the genetic effects of QTLs with high efficiency.