Gene action for cold tolerance in chickpea

Six crosses were investigated using combining ability and generation mean analyses for reaction to cold tolerance in chickpea (Cicer arietinum L.). The combining ability variances revealed the significance of both additive and nonadditive gene effects, with preponderance of additive gene effects. The generation mean analysis revealed the presence of genie interactions in addition to additive and dominance gene effects. Among the interactions, additive×additive and dominance×dominance with duplicate epistasis were present. Cold tolerance was dominant over susceptibility to cold. Selection for cold tolerance would be more effective if dominance and epistatic effects were reduced after a few generations of selfing

Effect of Midtreatment PET/CT-Adapted Radiation Therapy With Concurrent Chemotherapy in Patients With Locally Advanced Non–Small-Cell Lung Cancer

IMPORTANCE Our previous studies demonstrated that tumors significantly decrease in size and metabolic activity after delivery of 45 Gy of fractionated radiatiotherapy (RT), and that metabolic shrinkage is greater than anatomic shrinkage. This study aimed to determine whether 18F-fludeoxyglucose–positron emission tomography/computed tomography (FDG-PET/CT) acquired during the course of treatment provides an opportunity to deliver higher-dose radiation to the more aggressive areas of the tumor to improve local tumor control without increasing RT-induced lung toxicity (RILT), and possibly improve survival. OBJECTIVE To determine whether adaptive RT can target high-dose radiation to the FDG-avid tumor on midtreatment FDG-PET to improve local tumor control of locally advanced non–small-cell lung cancer (NSCLC). DESIGN, SETTING, AND PARTICIPANTS A phase 2 clinical trial conducted at 2 academic medical centers with 42 patients who had inoperable or unresectable stage II to stage III NSCLC enrolled from November 2008, to May 2012. Patients with poor performance, more than 10% weight loss, poor lung function, and/or oxygen dependence were included, providing that the patients could tolerate the procedures of PET scanning and RT. INTERVENTION Conformal RT was individualized to a fixed risk of RILT (grade >2) and adaptively escalated to the residual tumor defined on midtreatment FDG-PET up to a total dose of 86 Gy in 30 daily fractions. Medically fit patients received concurrent weekly carboplatin plus paclitaxel followed by 3 cycles of consolidation. MAIN OUTCOMES AND MEASURES The primary end point was local tumor control. The trial was designed to achieve a 20% improvement in 2-year control from 34% of our prior clinical trial experience with 63 to 69 Gy in a similar patient population. RESULTS The trial reached its accrual goal of 42 patients: median age, 63 years (range, 45–83 years); male, 28 (67%); smoker or former smoker, 39 (93%); stage III, 38 (90%). Median tumor dose delivered was 83 Gy (range, 63–86 Gy) in 30 daily fractions. Median follow-up for surviving patients was 47 months. The 2-year rates of infield and overall local regional tumor controls (ie, including isolated nodal failure) were 82% (95% CI, 62%–92%) and 62% (95% CI, 43%–77%), respectively. Median overall survival was 25 months (95% CI, 12–32 months). The 2-year and 5-year overall survival rates were 52% (95% CI, 36%–66%) and 30% (95% CI, 16%–45%), respectively. CONCLUSIONS AND RELEVANCE Adapting RT-escalated radiation dose to the FDG-avid tumor detected by midtreatment PET provided a favorable local-regional tumor control. The RTOG 1106 trial is an ongoing clinical trial to validate this finding in a randomized fashion. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT0119052

Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium

<p>Abstract</p> <p>Background</p> <p>Cockerham genetic models are commonly used in quantitative trait loci (QTL) analysis with a special feature of partitioning genotypic variances into various genetic variance components, while the F_∞genetic models are widely used in genetic association studies. Over years, there have been some confusion about the relationship between these two type of models. A link between the additive, dominance and epistatic effects in an F_∞model and the additive, dominance and epistatic variance components in a Cockerham model has not been well established, especially when there are multiple QTL in presence of epistasis and linkage disequilibrium (LD).</p> <p>Results</p> <p>In this paper, we further explore the differences and links between the F_∞and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the F_∞models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F_∞model. A new way of linking the Cockerham and F_∞model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F_∞models are applied.</p> <p>Conclusion</p> <p>The Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F_∞regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F_∞model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F_∞model can then be calculated by translating formulas derived for the Cockerham models.</p

Heterosis as Investigated in Terms of Polyploidy and Genetic Diversity Using Designed Brassica juncea Amphiploid and Its Progenitor Diploid Species

Fixed heterosis resulting from favorable interactions between the genes on their homoeologous genomes in an allopolyploid is considered analogous to classical heterosis accruing from interactions between homologous chromosomes in heterozygous plants of a diploid species. It has been hypothesized that fixed heterosis may be one of the causes of low classical heterosis in allopolyploids. We used Indian mustard (Brassica juncea, 2n = 36; AABB) as a model system to analyze this hypothesis due to ease of its resynthesis from its diploid progenitors, B. rapa (2n = 20; AA) and B. nigra (2n = 16; BB). Both forms of heterosis were investigated in terms of ploidy level, gene action and genetic diversity. To facilitate this, eleven B. juncea genotypes were resynthesized by hybridizing ten near inbred lines of B. rapa and nine of B. nigra. Three half diallel combinations involving resynthesized B. juncea (11×11) and the corresponding progenitor genotypes of B. rapa (10×10) and B. nigra (9×9) were evaluated. Genetic diversity was estimated based on DNA polymorphism generated by SSR primers. Heterosis and genetic diversity in parental diploid species appeared not to predict heterosis and genetic diversity at alloploid level. There was also no association between combining ability, genetic diversity and heterosis across ploidy. Though a large proportion (0.47) of combinations showed positive values, the average fixed heterosis was low for seed yield but high for biomass yield. The genetic diversity was a significant contributor to fixed heterosis for biomass yield, due possibly to adaptive advantage it may confer on de novo alloploids during evolution. Good general/specific combiners at diploid level did not necessarily produce good general/specific combiners at amphiploid level. It was also concluded that polyploidy impacts classical heterosis indirectly due to the negative association between fixed heterosis and classical heterosis

Systemic properties of metabolic networks lead to an epistasis-based model for heterosis

The genetic and molecular approaches to heterosis usually do not rely on any model of the genotype–phenotype relationship. From the generalization of Kacser and Burns’ biochemical model for dominance and epistasis to networks with several variable enzymes, we hypothesized that metabolic heterosis could be observed because the response of the flux towards enzyme activities and/or concentrations follows a multi-dimensional hyperbolic-like relationship. To corroborate this, we used the values of systemic parameters accounting for the kinetic behaviour of four enzymes of the upstream part of glycolysis, and simulated genetic variability by varying in silico enzyme concentrations. Then we “crossed” virtual parents to get 1,000 hybrids, and showed that best-parent heterosis was frequently observed. The decomposition of the flux value into genetic effects, with the help of a novel multilocus epistasis index, revealed that antagonistic additive-by-additive epistasis effects play the major role in this framework of the genotype–phenotype relationship. This result is consistent with various observations in quantitative and evolutionary genetics, and provides a model unifying the genetic effects underlying heterosis

Reservoirs and vectors of emerging viruses

Wildlife, especially mammals and birds, are hosts to an enormous number of viruses, most of which we have absolutely no knowledge about even though we know these viruses circulate readily in their specific niches. More often than not, these viruses are silent or asymptomatic in their natural hosts. In some instances, they can infect other species, and in rare cases, this cross-species transmission might lead to human infection. There are also instances where we know the reservoir hosts of zoonotic viruses that can and do infect humans. Studies of these animal hosts, the reservoirs of the viruses, provide us with the knowledge of the types of virus circulating in wildlife species, their incidence, pathogenicity for their host, and in some instances, the potential for transmission to other hosts. This paper describes examples of some of the viruses that have been detected in wildlife, and the reservoir hosts from which they have been detected. It also briefly explores the spread of arthropod-borne viruses and their diseases through the movement and establishment of vectors in new habitats

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease