Genetic Effects Of Quantitative Trait Loci Research Articles

Genetic linkage map construction and QTL identification of juvenile growth traits in Torreya grandis.

Torreya grandis Fort. ex Lindl, a conifer species widely distributed in Southeastern China, is of high economic value by producing edible, nutrient seeds. However, knowledge about the genome structure and organization of this species is poorly understood, thereby limiting the effective use of its gene resources. Here, we report on a first genetic linkage map for Torreya grandis using 96 progeny randomly chosen from a half-sib family of a commercially cultivated variety of this species, Torreya grandis Fort. ex Lindl cv. Merrillii. The map contains 262 molecular markers, i.e., 75 random amplified polymorphic DNAs (RAPD), 119 inter-simple sequence repeats (ISSR) and 62 amplified fragments length polymorphisms (AFLP), and spans a total of 7,139.9 cM, separated by 10 linkage groups. The linkage map was used to map quantitative trait loci (QTLs) associated with juvenile growth traits by functional mapping. We identified four basal diameter-related QTLs on linkage groups 1, 5 and 9; four height-related QTLs on linkage groups 1, 2, 5 and 8. It was observed that the genetic effects of QTLs on growth traits vary with age, suggesting the dynamic behavior of growth QTLs. Part of the QTLs was found to display a pleiotropic effect on basal diameter growth and height growth.

Validation of QTLs for Eating Quality of Japonica Rice ‘Koshihikari’ Using Backcross Inbred Lines

Using backcross inbred lines (BILs) derived from a cross between temperate japonica rice cultivars, Moritawase and Koshihikari, we validated the major quantitative trait loci (QTL) for eating quality and textural characteristics on chromosomes (Chr) 1, 2, 3, 6, 7, 10, and 12. Significant genetic differences in eating quality among BILs were detected at the QTL on Chr 3 and corresponded to the differences between the parents. Although differences in eating quality on the other chromosomes were not significant by t-test, cluster analysis and principal component analysis clearly showed that the genetic effects of the QTLs on Chr 6, 7, and 10 were similar to that on Chr 3, but the genetic effects of QTLs on Chr 1, 2, and 12 were entirely different from that on Chr 3. We previously identified that textural characteristics were highly correlated with eating quality. In this study, genetic differences in textural characteristics were similar to the genetic differences in eating quality among BILs. These results reveal major QTLs for eating quality of Koshihikari on Chr 3, 6, 7, and 10. The QTL on Chr 3 contributed most to the improvement of eating quality and textural characteristics.

Statistical method for mapping QTLs for complex traits based on two backcross populations

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.

A mathematical framework for functional mapping of complex phenotypes using delay differential equations

All biological phenomena occurring at different levels of organization from cells to organisms can be modeled as a dynamic system, in which the underlying components interact dynamically to comprehend its biological function. Such a systems modeling approach facilitates the use of biochemically and biophysically detailed mathematical models to describe and quantify “living cells,” leading to an in-depth and precise understanding of the behavior, development and function of a biological system. Here, we illustrate how this approach can be used to map genes or quantitative trait loci (QTLs) that control a complex trait using the example of the circadian rhythm system which has been at the forefront of analytical mathematical modeling for many years. We integrate a system of biologically meaningful delay differential equations (DDEs) into functional mapping, a statistical model designed to map dynamic QTLs involved in biological processes. The DDEs model the ability of circadian rhythm to generate autonomously sustained oscillations with a period close to 24 h, in terms of time-varying mRNA and protein abundances. By incorporating the Runge–Kutta fourth order algorithm within the likelihood-based context of functional mapping, we estimated the genetic parameters that define the periodic pattern of QTL effects on time-varying mRNA and protein abundances and their dynamic association as well as the linkage disequilibrium of the QTL and a marker. We prove theorems about how to choose appropriate parameters to guarantee periodic oscillations. We further used simulation studies to investigate how a QTL influences the period and the amplitude of circadian oscillations through changing model parameters. The model provides a quantitative framework for assessing the interplay between genetic effects of QTLs and rhythmic responses.

A unifying statistical model for QTL mapping of genotype x sex interaction for developmental trajectories.

Most organisms display remarkable differences in morphological, anatomical, and developmental features between the two sexes. It has been recognized that these sex-dependent differences are controlled by an array of specific genetic factors, mediated through various environmental stimuli. In this paper, we present a unifying statistical model for mapping quantitative trait loci (QTL) that are responsible for sexual differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, incorporated by sex-stimulated differentiation in growth form that is described by mathematical functions. A typical structural model is implemented to approximate time-dependent covariance matrices for longitudinal traits. This model allows for a number of biologically meaningful hypothesis tests regarding the effects of QTL on overall growth trajectories or particular stages of development. It is particularly powerful to test whether and how the genetic effects of QTL are expressed differently in different sexual backgrounds. Our model has been employed to map QTL affecting body mass growth trajectories in both male and female mice of an F2 population derived from the large (LG/J) and small (SM/J) mouse strains. We detected four growth QTL on chromosomes 6, 7, 11, and 15, two of which trigger different effects on growth curves between the two sexes. All the four QTL display significant genotype-sex interaction effects on the timing of maximal growth rate in the ontogenetic growth of mice. The implications of our model for studying the genetic architecture of growth trajectories and its extensions to some more general situations are discussed.

Bayesian model choice and search strategies for mapping interacting quantitative trait Loci.

Most complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.