Abstract: Various methods are provided for determining progeny plant genotypes from a pooled sample. The methods include genotyping marker alleles in two or more progeny plants, each resulting from a different parental cross. The methods utilize in-silico deconvolution implementing Hidden Markov Modeling to determine a probability of each possible genotype for one or more marker loci in at least one progeny plant from the genetic signal generated from the pooled samples. The present methods provide for increased efficiency in genotyping progeny plants.

Abstract: Various methods are provided for determining progeny plant genotypes from a pooled sample. The methods include genotyping marker alleles in two or more progeny plants, each resulting from a different parental cross. The methods utilize in-silico deconvolution implementing Hidden Markov Modeling to determine a probability of each possible genotype for one or more marker loci in at least one progeny plant from the genetic signal generated from the pooled samples. The present methods provide for increased efficiency in genotyping progeny plants.

Abstract: Various methods are provided for determining progeny plant genotypes from a pooled sample. The methods include genotyping marker alleles in two or more progeny plants, each resulting from a different parental cross. The methods utilize in-silico deconvolution implementing Hidden Markov Modeling to determine a probability of each possible genotype for one or more marker loci in at least one progeny plant from the genetic signal generated from the pooled samples. The present methods provide for increased efficiency in genotyping progeny plants.

Abstract: A Bayesian multilevel whole-genome regression model is disclosed and its prediction performance compared to that of the popular BayesA model applied to each population separately (no pooling) and to the joined data set (complete pooling). For small population sizes (e.g., <50), partial pooling increased prediction accuracy over no or complete pooling for populations represented in the estimation set. Partial pooling with multilevel models can make optimal use of information in multi-population estimation sets.

Abstract: Improved molecular breeding methods include a method in which an association data set is developed by associating the phenotypes of a broad population of individuals with the individual genotypes. The association data set is used in conjunction with a growth model in order to select breeding pairs likely to generate offspring with one or more desirable traits.