Estimating effects of a single gene and polygenes on quantitative traits from a diallel design

• Published in: Genetica 2006-09, Vol.128 (1-3), p.471-484
• Main Authors: Lou, Xiang-Yang, Yang, Mark C K
• Format: Article
• Language: English
• Subjects: Alleles; Animals; Biometry; Carboxylesterase - genetics; Computer programs; Design; Drosophila melanogaster; Drosophila melanogaster - enzymology; Drosophila melanogaster - genetics; Drosophila Proteins - genetics; Economic models; Female; Gene mapping; Genes, Insect; Genetics; Likelihood Functions; Male; Models, Genetic; Monte Carlo Method; Monte Carlo simulation; Quantitative Trait, Heritable; Statistical analysis; Statistical methods; Studies; Variance analysis
• ISSN: 0016-6707; 1573-6857
• DOI: 10.1007/s10709-006-7853-y

A genetic model is developed with additive and dominance effects of a single gene and polygenes as well as general and specific reciprocal effects for the progeny from a diallel mating design. The methods of ANOVA, minimum norm quadratic unbiased estimation (MINQUE), restricted maximum likelihood estimation (REML), and maximum likelihood estimation (ML) are suggested for estimating variance components, and the methods of generalized least squares (GLS) and ordinary least squares (OLS) for fixed effects, while best linear unbiased prediction, linear unbiased prediction (LUP), and adjusted unbiased prediction are suggested for analyzing random effects. Monte Carlo simulations were conducted to evaluate the unbiasedness and efficiency of statistical methods involving two diallel designs with commonly used sample sizes, 6 and 8 parents, with no and missing crosses, respectively. Simulation results show that GLS and OLS are almost equally efficient for estimation of fixed effects, while MINQUE (1) and REML are better estimators of the variance components and LUP is most practical method for prediction of random effects. Data from a Drosophila melanogaster experiment (Gilbert 1985a, Theor appl Genet 69:625-629) were used as a working example to demonstrate the statistical analysis. The new methodology is also applicable to screening candidate gene(s) and to other mating designs with multiple parents, such as nested (NC Design I) and factorial (NC Design II) designs. Moreover, this methodology can serve as a guide to develop new methods for detecting indiscernible major genes and mapping quantitative trait loci based on mixture distribution theory. The computer program for the methods suggested in this article is freely available from the authors.