Principle And Method Of Composite Interval Mapping Qtl For Dynamic Trait

Growth development of tissues or organs and productive performances changing with life time or certain quantitative factors are called with one voice as dynamic traits. From theory and simulation experiment, the principle and method of composite interval mapping QTL are systemically investgated for dynamic traits in F2 population. The study results are as follows:1 Construction of mathematical models of composite interval mapping QTL for dynamic traits Based on the orthogonal, additive and universal-practicable properties of Legendre polynomials, Legendre polynomials with appropriate order are nested within genetic effects in genetic model for mapping QTL to describe the effects of QTL and cofactors on changing process of dynamic trait The powers of detecting multiple QTL controlling dynamic traits will therefore be increased by means of the advantages of composite interval mapping.2 Deduction and verification of maximum likelihood estimation and regression estimation of parameters in mathematical models of composite interval mapping QTL for dynamic traits It is included that general parameter estimation method for disequilibrium sampling data and simplified one for disequilibrium sampling data. The corresponding solving processes are provided at the same time.3 Simulation study by using Monte Carlo method The powers of detecting multiple QTL controlling dynamic traits between interval mapping and composite interval mapping are compared in the same mapping environments; Feasibility of replacing maximum likelihood estimation (ML) with regression estimation (RG) is discussed to estimate the parameters in mathematical models of composite interval mapping QTL for dynamic traits, and the effects of cofactors on detecting powers are emphatically analyzed in composite interval mapping QTL for dynamic traits. The results from simulations show that composite interval mapping is not only more than interval mapping in the number of QTL detected, but also is better in estimation accuracy and precision for QTLpositions, additive and dominant regression effects; Under assuming that variances of residual errors are the same in different test days, The statistical efficiencies of detecting multiple QTL are almost same between ML and RG, but the calculating speeds of RG are greatly higher than ML; When multiple QTL are placed densely on specific chromosome fraction, the number of QTL is much more detected and the estimation accuracy of parameters is higher as increased number of cofactors in composite interval mapping model; When multiple QTL are placed distantly, the differences of detecting efficiencies among models with a few cofactors are similar to results from above dense QTL, but The detecting efficiencies tend to be stable until number of cofactors attain to be six because of limitation of residual QTL effects needed to be absorbed.Compared with present methods, composite interval mapping QTL for dynamic trait presented here perform obvious advantages as: 1) it is greatly convenient to sample as well as increase sampling costs because of the ability to deal with the disequilibrium sampling data; 2) it is suitable for any dynamic trait Changing law of dynamic trait can be fitted with the best precision as long as the Legendre polynomial with appropriate order is chosen; 3) Ability to simultaneously analyze effects of genetic and various systemic environment factors, and 4) it is easy to extent it to other resource populations and complicate mating populations.In view of the economical importance of dynamic traits and its extensive existence in features, the study on mapping QTL for these traits would be important in theoretical and practical for revealing its genetic law and increasing its improvement efficiency.