Statistical Analysis of Joint Effects of Major Genes and Polygenes in Quantitative Genetics

Abstract

In this paper we analyze a quantitative genetic character which is controlled by both major genes and polygenes. Assuming that thare a m no rpistatic effects. DO linkage and no geneticenvironmental interactiona, we follow TAN and Chiao (1072) to derive the probability distributions for segregating populations. The numbere of major genes and polygmea, and the additive and dominance effects of major genes and polygeoes are then estimated by using the procedures developed in TAN and C~N O (1972) and the POWZLL-FLETCHER search procedure for maximum values. In thin paper, we consider the case involving data from P,, P1, F,. B, (Backcroas to P , ) and B2 (Backcroae to Pz) ns this type of experiment is common in praotical applications. The analyses are applied to a simulated model generated by using binomial, multinomial and normal variables and to the data of a n erperiment on kernel weight of sorghum plant provided to the authors by ProfeasorGEonor H. L. LWO of K.anaae S t a k University. The analysis of these data indicate cle~rly that the method derived in this paper is useful and desirable.

Maximum Likelihood Estimation of Genetic Parameters for Quantitative traits from Autotetraploid Self‐Fertilized Populations

“…We assume that the data involve two parents Pi and P 2 , F 2 (PiX P 2 ) , n, (FiX PI) and B 2 (FiX P 2 ) which we will refer to as the first, the second, the third, the fourth and the fifth populations. In plant breeding and plant quantitative genetics experiments, this sort of data are very common for self-fertilized plants (see ALLARD and HARDING (1962), MATHER and JINKS (1971), TAN and CHANG (1972) and TAN and D'ANGELO (1979)). In this model the two parents are pure lines so that genetic segregations are expected only in F 2 , B, and B 2 populations.…”

A Study of Deleterious Gene Structure in Plants Using Markov Chain Monte Carlo

“…Therefore, even though the costs of a large number of our MCMC iterations are also high, the algorithm is readily implemented and works efficiently for large values of M where peeling becomes difficult. One may consider improving this grid-based peeling evaluation by using other peeling techniques, such as the classical steepest ascent method and Powell's algorithm (e.g., Tan and D'Angelo, 1979). We note that the former is not tractable since it involves partial derivatives that are difficult to evaluate and the latter may not be successful since its one-dimensional maximization may fail to capture the ridge shape of the likelihoods of M and q.…”

Multivariate segregation analysis for quantitative traits in line crosses

“…The most widely accepted is segregation analysis based on the mixture model (Tan and Chang, 1972;Elston and Stewart, 1973;Tan and D'Angelo, 1979;Elston, 1984;Loisel et al, 1994;Zhang et al, 2003). Various analytical strategies have been adopted in an attempt to make the method more reliable and robust.…”