Statistical Approaches to Mapping QTL of Dynamic Traits

Quantitative traits whose phenotypic values change with time or other quantitative factor are called dynamic quantitative traits. Genetic analyses of dynamic traits are usually conducted in one of two ways. One is to treat phenotypic values collected at different time points as repeated measurements of the same trait, which are analyzed in the framework of multivariate theory. Alternatively, a growth curve may be fit to the phenotypes at multiple time points and inference can be made through the parameters of the growth trajectories. The latter has been used in QTL mapping for developmental traits and resulted in an appearance of the functional mapping strategy. Aiming at the disadvantages of functional mapping strategy, we propose to replace the nonlinear and non-additive model biological meaningful by the orthogonal polynomial or B-Spline model to fit dynamic curves with arbitrary shape and analyze arbitrary complicated data, and the constant residual covariance matrix by the alterable one calculated by using auto-correlation function to deal with discrepancies in measurement schedule of phenotype among progenies. A novel RRM mapping strategy was developed for mapping QTL of dynamic traits, which performs higher detecting efficiency than functional mapping, especially for detection of multiple QTL, has been proved by our simulations and data analysis. Finally, a simplified and effective mapping strategy was further discussed by integrating functional mapping and RRM mapping strategies.

Effects of Streptomyces microflavus Extract on Heat Tolerance of Tall Fescue

Heat stress in summer is a major factor limiting use of cool-season grasses in Shanghai area, China. The objectives of this study were to investigate effects of a crude extract product of a new strain of Streptomyces microflavus (TSS) on turf performance and physiological activities of tall fescue (Festuca arundinacea) in response to heat stress. Plants of tall fescue cultivar ‘Barlexus’ were exposed to 38/33 (°C) (day/night) high temperature in growth chamber after TSS applications. High temperature induced about 3.5 fold increases of initial shoot extension rate and clipping yield (6 d) and led to quick decline of plant growth after 18 d. TSS inhibited the extent of initial increases of shoot extension rate and clipping yield and maintained a longer period of steady growth under the heat stress. TSS also decreased the decline of leaf chlorophyll content, TNC, shoot density, and turf quality induced by heat stress. The results suggested that TSS application improved turf performance under heat stress, and the greater improvement of heat tolerance was associated with higher dose of application. This positive effect on heat tolerance could be related to the decreased carbohydrate consumption and loss, and reduced decomposition of leaf chlorophyll.

Mapping QTL for Categorical Traits with Multivariate Regression

Simple linear regression analysis has been used to map QTL for quantitative traits. Many traits of biological interest and/or economical importance in various species show binary phenotypic distributions (e.g., presence or absence). It has been shown that such a binary trait also can be analyzed with the simple linear regression, subject to virtually no loss in power compared to the generalized linear model analysis. Binary trait is a special case of a multiple categorical trait (e.g., low, medium or high). We propose a mechanism to decompose a multiple categorical trait into an array of correlated binary variables. The categorical trait turned multiple binary traits are analyzed with a multivariate linear regression method. Turning the problem of categorical trait mapping into that of multivariate mapping allows the exploration of pleiotropic effects of QTL for different categories. Efficiency of the method is verified through a series of simulation experiments.