Barley (Hordeum Vulgare L.) is grown worldwide for food, feed, and malt production. Malt is a primary input to brewing and distilling processes. Expansion of barley acreage has increased the demand for pre harvest sprouting (PHS) resistant barley varieties. Molecular and genome-wide breeding techniques can meet this demand rapidly. PHS resistance requires grain dormancy, which may have effects on malting barley quality parameters however these relationships are relatively uncharacterized. Malting quality is expensive to measure making genomic selection and secondary correlated traits attractive means of selection. The goal of this work was to characterize the genetic relationships between PHS resistance and malting quality in malting barley breeding lines to enable the selection of lines with excellent PHS resistance and malting quality parameters. In the first study, germination traits related to PHS resistance and malting suitability are shown to be highly predictable over time using logistic regression and functional principal components analysis. In the second, fine scale questions about the prediction of germination traits within breeding populations segregating for large effect dormancy loci are examined and several methods of improving prediction accuracy are tested. In the third, the relationships between the PHS resistance and malting quality traits are characterized, correlated response to selection against PHS is evaluated, and genome wide association for malting quality traits is performed to find common associations with PHS resistance loci. In the last, multi-trait genomic prediction models including germination traits to predict malting quality parameters are evaluated under a number of scenarios. The most important findings are that: 1) there are strong genetic correlations of PHS resistance to malting quality traits alpha amylase activity, free amino nitrogen, soluble protein, and soluble protein/total malt protein ratio; these are associated with the very non dormant HvMKK3 allele also associated with PHS susceptibility, 2) adjunct malting quality profiles are typically not compatible with PHS resistance, 3) there are informed ways of structuring prediction models to increase genomic prediction accuracy or ability in circumstances where single loci explain large amounts of genetic variance, 4) initial dormancy and how quickly germination changes over time is highly predictable within spring malting barley, and 5) using multi-trait genomic prediction models incorporating germination traits under sparse sampling techniques can increase prediction ability of malting quality traits by 16 to 62% over single trait, replicated models, and 0-12% over single trait sparse sampling models