Abstract

Separation of single gene and polygenic effects would be useful in crop improvement. In this study, additive-dominance model with a single qualitative gene based on diallel crosses of parents and progeny F1s (or F2s) was examined. The mixed linear model approach, minimum norm quadratic unbiased estimation (MINQUE), was used to estimate the variance and covariance components and single gene effects. Monte Carlo simulation was used to evaluate the efficiency of each parameter estimated from the MINQUE approach for this genetic model. The results of 200 simulations indicated that estimates of variance components and single gene effects were unbiased when setting different single gene effects for parents and F1s (or F2s). Results also indicated that estimates of variances and single gene effects were very similar for both genetic populations. Therefore, single gene effects could be effectively separated and estimated by this approach. This research should aid the extension of this model to cases that involve multiple linked or unlinked genes (or genetic markers) and other complex ploygenic models. For illustration, a real data set comprised of eight parents of upland cotton (Gossypium hirsutum L.) with normal leaf and one parent with okra leaf, and their 44 F2s were used to estimate the variance components and the genetic effects of the okra leaf gene on fiber traits.

Keywords

mixed linear model, qualitative gene effects, polygenic effects, Monte Carlo simulation, variance components

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Apr 30th, 3:45 PM

SEPARATION OF SINGLE GENE EFFECTS FROM ADDITIVE-DOMINANCE GENETIC MODELS

Separation of single gene and polygenic effects would be useful in crop improvement. In this study, additive-dominance model with a single qualitative gene based on diallel crosses of parents and progeny F1s (or F2s) was examined. The mixed linear model approach, minimum norm quadratic unbiased estimation (MINQUE), was used to estimate the variance and covariance components and single gene effects. Monte Carlo simulation was used to evaluate the efficiency of each parameter estimated from the MINQUE approach for this genetic model. The results of 200 simulations indicated that estimates of variance components and single gene effects were unbiased when setting different single gene effects for parents and F1s (or F2s). Results also indicated that estimates of variances and single gene effects were very similar for both genetic populations. Therefore, single gene effects could be effectively separated and estimated by this approach. This research should aid the extension of this model to cases that involve multiple linked or unlinked genes (or genetic markers) and other complex ploygenic models. For illustration, a real data set comprised of eight parents of upland cotton (Gossypium hirsutum L.) with normal leaf and one parent with okra leaf, and their 44 F2s were used to estimate the variance components and the genetic effects of the okra leaf gene on fiber traits.