Using QTL results to discriminate among crosses based on their progeny mean and variance

Authors: ZHONG, SHENGQIANG - IOWA STATE UNIVERSITY; Jannink, Jean-Luc

Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/2/2007
Publication Date: 9/1/2007
Citation: Zhong, S., Jannink, J. 2007. Using QTL results to discriminate among crosses based on their progeny mean and variance. Genetics. 177:567-576.

Interpretive Summary: The value of a particular cross for breeding purposes depends not on the mean of all its progeny but only the best of its progeny. The value of the best progeny can be predicted as a function of the mean of all progeny and of their standard deviation. We developed theory to calculate this standard deviation using results from QTL analysis. The value of accounting for this standard deviation increased with increasing heritability and marker density used in the QTL analysis, but decreasing genome size and QTL number. This value was also higher if, as would be typical in breeding programs, only crosses among the best parents were performed. We also showed that the method of QTL analysis affects how well superior progeny can be predicted. Including all rather than only significant markers in the calculation improved prediction as did accounting for the uncertainty of QTL analysis. Nevertheless, we generally found little difference among crosses for the standard deviation among their progeny such that the value of estimating it was restricted to relatively few cases.

Technical Abstract: In order to develop inbred lines, parents are crossed to generate segregating populations from which superior inbred progeny are selected. The value of a particular cross thus depends on the expected performance of its best progeny, which we call the superior progeny value. Superior progeny value is a linear combination of the mean of the cross’s progeny and their standard deviation. In this study we specify theory to predict a cross’s progeny standard deviation from QTL results and explore analytically and by simulation the variance of that standard deviation under different genetic models. We then study the impact of different QTL analysis methods on the prediction accuracy of a cross’s superior progeny value. We show that including all markers, rather than only markers with significant effects, improves the prediction. Methods that account for the uncertainty of the QTL analysis by integrating over the posterior distributions of effect estimates also produce better predictions than methods that retain only point estimates from the QTL analysis. The utility of including estimates of a cross’s among-progeny standard deviation in the prediction increases with increasing heritability and marker density but decreasing genome size and QTL number. This utility is also higher if crosses are only envisioned among the best parents rather than among all parents. Nevertheless, we show that among crosses the variance of progeny means is generally much greater than the variance of progeny standard deviations, restricting the utility of estimates of progeny standard deviations to a relatively small parameter space.