Studies to map quantitative trait loci (QTL) in plants have used designed populations such as F2 or backcross populations. In silico mapping circumvents the need for designed populations by exploiting existing phenotypic, pedigree, and genomic information in plant breeding programs. The objective of this study was to evaluate the usefulness of in silico mapping to detect QTL in a breeding program for a model self-pollinated crop (soybean, Glycine max L. Merr), and to validate the methodology by detecting QTL for kernel hardness and dough strength in a wheat (Triticum aestivum L.) breeding program. I simulated a soybean breeding program and applied in silico QTL mapping via a mixed-model approach. Average power to detect QTL ranged from <1% to 47% depending on the significance level, number of QTL, heritability of the trait, population size, and number of markers. The false discovery rate ranged from 2% to 43%. Larger populations, higher heritability, and fewer QTL controlling the trait increased power and reduced the false discovery rate and bias. There was greater power to detect major QTL than minor QTL. For validation, I studied 80 parental and 373 experimental wheat inbreds genotyped for 65 simple sequence repeat markers and three candidate loci. Two QTLs for kernel hardness were detected on chromosomes 1A and SD. Four QTLs for dough strength were detected on chromosomes 1A, 1B, 1D, and 5B. Results were consistent with previously reported markers, QTLs, and candidate loci associated with kernel hardness and dough strength. Unlike previous studies, my assumption of fixed marker effects identified the favorable marker alleles to select for. In conclusion, in silico QTL mapping via a mixed-model approach allows gene discovery in the context of a plant breeding program.