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Research suggests that in North America, soybean Relative Maturity (RM) is controlled by a minimum of eight genetic loci labeled E loci. The amount of variation explained by these genes would suggest that accurate predictions for RM could be obtained using prediction models that only include allele effects for markers located near the major E genes. Having the ability to accurately predict the RM of a segregating breeding line using genetic information has the potential to positively impact both the rate of genetic gain and cost per unit of genetic gain within a breeding program by enabling; 1) prediction of RM in segregating progeny from crosses between parents with large differences in RM; 2) selection of segregating lines with appropriate RMs in non-adapted off season nurseries; 3) increased selection intensities of segregating lines assigned to field trials; and 4) cost reduction of replicated field trials. The objectives of this research then was to; 1) compare the accuracy of RM prediction using genome wide markers versus using prediction models containing only molecular markers significantly associated with RM; 2) validate that prediction accuracies were maintained when predictions were made for segregating lines not only having distant relationships to those in the original training dataset, but also developed and grown outside of the years of the segregating lines in the original training dataset; and 3) evaluate if the prediction accuracies and associated genotyping costs support wide scale RM prediction within a soybean cultivar development program. In effort to determine if the RM of a segregating soybean breeding line could be predicted using genetic information, we developed a training dataset that consisted of

The use of genetic information to predict the relative maturity of soybeans