Using genomic data to improve the estimation of general combining ability based on sparse partial diallel cross designs in maize
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Evaluation of general combining ability (GCA) is crucial to hybrid breeding in maize. Although the complete diallel cross design can provide an efficient estimation, sparse partial diallel cross (SPDC) is more flexible in breeding practice. Using real and simulated data sets of partial diallel crosses between 266 maize inbred lines, this study investigated the performance of SPDC designs for estimating the GCA. With different distributions of parental lines involved in crossing (called random, balanced and unbalanced samplings), different numbers of hybrids were sampled as the training sets to estimate the GCA of the 266 inbred lines. In this process, three statistical approaches were applied. One obtained estimations through the ordinary least square (OLS) method, and the other two utilized genomic prediction (GP) to estimate the GCA. It was found that the coefficient of determination of each approach was always higher than the heritability of a target trait, showing that the GCA for maize inbred lines could be accurately predicted with SPDC designs. Both the GP approaches were more accurate than the OLS, particularly in the scenario for a low-heritability trait with a small sample size. Additionally, prediction results demonstrated that a big sample of hybrids could greatly help improve the accuracy. The random sampling of parental lines had little influence on the average accuracy. However, the prediction for lines that never or seldom involved in crossing might suffer from much lower accuracy. (C) 2020 Crop Science Society of China and Institute of Crop Science, CAAS. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
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