Genetic improvement of feed intake and methane emissions of cattle

Feed costs represent half of the total costs of dairy production. One way to increase profitability of dairy production is to reduce feed costs by improving feed efficiency. As DMI is a trait that varies significantly during and across lactations, it is imperative to understand the underlying genetic architecture of DMI across lactation. Moreover, phenotypes of DMI are scarce, due to the difficulty of recording them (expensive and labor-intensive). Some predictor traits have been suggested to predict DMI. Examples of these predictor traits are those related to production (milk yield (MY) or milk content) or to the maintenance of the cow (body weight (BW) or conformation traits). The ability to determine when predictor traits ideally should be measured in order to achieve an accurate prediction of DMI throughout the whole lactation period is thus important. Recently, with the use of information of single nucleotide polymorphism (SNP) markers, together with phenotypic data and pedigree, genomically estimated breeding values (GEBV) of scarcely recorded traits, such as DMI, have become easier to accurately predict. This approach, combined with predictor traits, could contribute to an increased accuracy of predictions of GEBV of DMI. Methane (CH4) is the second important greenhouse gas, and enteric CH4 is the largest source of anthropogenic CH4, representing 17% of global CH4 emissions. Furthermore, methane emissions represent 2-12% of feed energy losses. Selecting for lower CH4 emitting animals and more feed-efficient animals would aid in mitigating global CH4 emissions. To identify the impact on CH4 emissions of selecting for lower DMI animals, it is important to determine the correlations between DMI and CH4 and to identify whether the same genes that control DMI affect CH4. Therefore, the general objectives of this thesis were to (1) explore the genetic architecture of DMI during lactation, (2) study the relationship of DMI to conformation, production and other related traits, (3) investigate the correlations between DMI and methane traits, and determine the SNP in common between DMI and CH4 through a genome-wide association study (GWAS), and (4) investigate the accuracy of predictions of DMI using predictor traits combined with genomic data.