Dairy cattle breeding systems have a common goal of improved milk production. Extensive research has been carried out to increase our understanding of the biology behind milk production. This biological information has then been used to improve the prediction of a cow's milk production. This thesis explores two ways in which milk production can be better understood and predicted. The first study investigates whether the sex of the calf that initiates a lactation has an impact on milk yield during that lactation as well as whether the sex of calves in different parities affects milk yield during that lactation. The second study investigates the additive and dominance effects of four quantitative trait nucleotides (QTN) for milk fat yield and evaluates whether including the QTN in genomic prediction analyses as either a random covariate, fixed covariate or fixed class improves estimated breeding values (EBV).

Calf sex was shown to have an effect on milk yield in each of the first three lactations, with heifer calves resulting in a higher milk yield than bull calves. Some of the effect of calf sex is explained by a greater number of days-in-milk for a cow when a female calf was born, as evidenced by a decrease in the effect of calf sex when days-in-milk was fitted in the model. While days-in-milk explained some of the effect, there was still some effect of calf sex within and across lactations.

Dominance was observed at the majority of QTN, suggesting that milk fat yield EBV could be improved by the correct modeling of dominance in prediction models. Including QTN in genomic prediction models did not significantly change prediction accuracy, however including QTN significantly decreased bias in many cases, such as when AGPAT6, a QTN with observed over-dominance, was included as a fixed class effect. There was evidence of epistatic interactions between the QTN PLAG1 and AGPAT6, PLAG1 and GHR, and DGAT1 and GHR.

This thesis shows there is the potential to improve modeling of economically important traits. As data sets get bigger and data collection methods improve, more of the underlying biology will be revealed.