Antagonistic bioactivity of an endophytic bacterium H-6

An endophytic bacterium, H-6, was isolated from leaves of Huperzia serrata grown in the Lushan Mountain, China. The strain was identified as Burkholderia sp. H-6 based on morphological, physiological and biochemical methods as well as on 16S rDNA analysis. This strain inhibited mycelium growth in vitro of 6 plant pathogenic fungi, especially of Phytophthora capsici, Fusarium graminearumt and Sclerotinia libertiana. In greenhouse pot experiments, soil drenches with cell densities of 106, 108 and 1010 CFU ml-1 H-6 reduced significantly P. capsici, in pepper seedling by 51.7, 58.7 and 60.2%, respectively, compared to the inoculated control, 3 weeks after sowing. Growth parameters such as lengths and fresh weights of roots and shoots of P. capsici-inoculated control plants were significantly lower compared to P. capsici-inoculated and H-6-treated plants, which is an added advantage of the strain used as potential biocontrol agent in future.Key words: Endophytic bacterium, 16S rDNA gene, antagonistic activity, Huperzia serrata

How To Perform Meaningful Estimates of Genetic Effects

Although the genotype-phenotype map plays a central role both in Quantitative and Evolutionary Genetics, the formalization of a completely general and satisfactory model of genetic effects, particularly accounting for epistasis, remains a theoretical challenge. Here, we use a two-locus genetic system in simulated populations with epistasis to show the convenience of using a recently developed model, NOIA, to perform estimates of genetic effects and the decomposition of the genetic variance that are orthogonal even under deviations from the Hardy-Weinberg proportions. We develop the theory for how to use this model in interval mapping of quantitative trait loci using Halley-Knott regressions, and we analyze a real data set to illustrate the advantage of using this approach in practice. In this example, we show that departures from the Hardy-Weinberg proportions that are expected by sampling alone substantially alter the orthogonal estimates of genetic effects when other statistical models, like F2 or G2A, are used instead of NOIA. Finally, for the first time from real data, we provide estimates of functional genetic effects as sets of effects of natural allele substitutions in a particular genotype, which enriches the debate on the interpretation of genetic effects as implemented both in functional and in statistical models. We also discuss further implementations leading to a completely general genotype-phenotype map

Relationship between epistasis and aggressiveness in resistance of pepper (Capsicum annuum L.) to Phytophthora nicotianae

This study evaluated the types of gene action governing the inheritance of resistance to Phytophthora nicotianae necrosis in populations derived from two crosses involving two susceptible (Beldi and Nabeul II) and one resistant (CM334) cultivars of pepper (Capsicum annuum L.). Populations, composed of Pr, Ps, F1 , F 2 , BC 1 Pr, and BC 1 Ps generations, were inoculated with six P. nicotianae isolates. Generation means analysis indicated that an additive-dominance model was appropriate for P. nicotianae isolates Pn Ko1 , Pn Ko2 and Pn Kr1 , which showed low aggressiveness in the two crosses. For the more aggressive isolates Pn Bz1 , Pn Bz2 and Pn Kr2 , epistasis was an integral component of resistance in the two crosses. The presence of epistasis in the resistance of pepper to P. nicotianae was dependent on the level of aggressiveness of the isolates. Selection in pepper with less aggressive isolates was efficient, but not with more aggressive isolates; on the other hand, selection with more aggressive isolates was more stable. The minimum number of genes controlling resistance was estimated at up to 2.71. In the majority of cases, the additive variance was significant and greater than the environmental and dominance variance

Detection of two QTL on chicken chromosome 14 for keyhole lymphet heamocyanin

A keyhole lymphet heamocyanin is an antigen which triggers Th1 type of immune response. A QTL for a primary immune response towards keyhole lymphet heamocyanin has been detected on chicken chromosome 14 in three populations. The results from the most recent population were inconsistent and varied depending on the applied QTL detection model. The major goal of the current study was the reanalysis of this data using a 2 QTL model. Additionally, in order to provide more accurate estimates of QTL effects and positions, epistasis between the QTL was considered as a potential important contributor to quantitative traits. Four statistical models were assumed: M1: A model assuming marginal additive effects of two QTL; M2: A model assuming marginal and epistatic additive effects of two QTL; M3: A model assuming marginal additive and dominance effects of two QTL; M4: A model assuming marginal additive and dominance effects of two QTL and all possible pairwise epistases. Two QTL with significant additive and dominance effects were detected on chicken chromosome 14 using model M3. One QTL was detected at 63 cM between MCW0123 and ROS0005, another at 76 cM between ROS0005 and MCW0225/NTN2Lsts1 (FDR = 0.0051). Modelling only additive effects resulted in a significantly worse fit. On the other hand, including epistatic effects did not improve fit significantly. The current study confirms previous reports of the QTL location on GGA14. A notable finding of this study is recognition of two closely related QTL for a keyhole lymphet heamocyanin response at the distal part of chicken chromosome 14

Gene by environment QTL mapping through multiple trait analyses in blood pressure salt-sensitivity: identification of a novel QTL in rat chromosome 5

BACKGROUND: The genetic mechanisms underlying interindividual blood pressure variation reflect the complex interplay of both genetic and environmental variables. The current standard statistical methods for detecting genes involved in the regulation mechanisms of complex traits are based on univariate analysis. Few studies have focused on the search for and understanding of quantitative trait loci responsible for gene × environmental interactions or multiple trait analysis. Composite interval mapping has been extended to multiple traits and may be an interesting approach to such a problem. METHODS: We used multiple-trait analysis for quantitative trait locus mapping of loci having different effects on systolic blood pressure with NaCl exposure. Animals studied were 188 rats, the progenies of an F2 rat intercross between the hypertensive and normotensive strain, genotyped in 179 polymorphic markers across the rat genome. To accommodate the correlational structure from measurements taken in the same animals, we applied univariate and multivariate strategies for analyzing the data. RESULTS: We detected a new quantitative train locus on a region close to marker R589 in chromosome 5 of the rat genome, not previously identified through serial analysis of individual traits. In addition, we were able to justify analytically the parametric restrictions in terms of regression coefficients responsible for the gain in precision with the adopted analytical approach. CONCLUSION: Future work should focus on fine mapping and the identification of the causative variant responsible for this quantitative trait locus signal. The multivariable strategy might be valuable in the study of genetic determinants of interindividual variation of antihypertensive drug effectiveness

Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases

<p>Abstract</p> <p>Background</p> <p>Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates using a small number of observations. Missing trait and/or marker values prevent one from directly applying the classical model selection criteria such as Akaike's information criterion (AIC) and Bayesian information criterion (BIC).</p> <p>Results</p> <p>We propose a two-step Bayesian variable selection method which deals with the sparse parameter space and the small sample size issues. The regression coefficient priors are flexible enough to incorporate the characteristic of "large <it>p </it>small <it>n</it>" data. Specifically, sparseness and possible asymmetry of the significant coefficients are dealt with by developing a Gibbs sampling algorithm to stochastically search through low-dimensional subspaces for significant variables. The superior performance of the approach is demonstrated via simulation study. We also applied it to real QTL mapping datasets.</p> <p>Conclusion</p> <p>The two-step procedure coupled with Bayesian classification offers flexibility in modeling "large p small n" data, especially for the sparse and asymmetric parameter space. This approach can be extended to other settings characterized by high dimension and low sample size.</p

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

Mapping quantitative trait loci in line cross with repeat records

<p>Abstract</p> <p>Background</p> <p>Phenotypes with repeat records from one individual or multiple individuals were often encountered in practices of mapping QTL in linecross. The current genetic mapping method for a trait with repeat records is adopted by simply replacing the phenotype by the average value of the repeat records. This simple treatment has not sufficiently utilized the information from the replication and ignored the impacts of the permanent environmental effects on the accuracy of the estimated QTL.</p> <p>Results</p> <p>We propose to map QTL by using the repeatability model to directly analyze the repeat records rather than simply analyze the mean phenotype, improving the efficiency of QTL detecting because of adequately utilizing the information from data and allowing for the permanent environmental effects. A maximum likelihood method implemented via the expectation-maximization (EM) algorithm is applied to perform the parameter estimation of the repeatability model. The superiority of the mapping method based on the repeatability model over simple analysis using the mean phenotype was demonstrated by a series of simulations.</p> <p>Conclusion</p> <p>Our results suggest that the proposed method can serve as a powerful alternative to existing methods. By mean of the repeatability model, utilizing the repeat records on individual may improve the efficiency of QTL detecting in line cross.</p