Leonora M. James ¹ Mary S. Mayes ¹ Cori Cooper ¹ James E. Koltes ^1* Matthew W. Breitzman ² Michael J. Vandehaar ³

• ¹ Iowa State University, Ames, Iowa, United States
• ² W. M. Keck Metabolomics Research Laboratory, Office of Biotechnology, Iowa State University, Ames, Iowa, United States
• ³ Michigan State University, East Lansing, Michigan, United States

Genetic selection for feed efficiency is possible in Holstein dairy cattle. However, measuring individual cow feed intake is expensive, which limits available phenotypes, resulting in lower prediction accuracy of breeding values than desired. New indicator trait phenotypes for feed efficiency could help improve breeding value accuracies if they can be measured widely across dairy herds. The objective of this study was to identify milk metabolites associated with feed intake and efficiency traits that may serve as new indicator traits. Metabolites were obtained from three sources and two distinct groups of cows. Gas chromatography mass spectrometry (GC-MS), and liquid chromatography mass spectrometry (LC-MS) assays were conducted on a subset of 39 cows identified based on their extreme residual feed intake (RFI; top and bottom 15%). Routinely collected on-farm milk testing data were evaluated on a second, larger subset of 357 cows. Statistical models were created to evaluate if metabolites: 1) provided novel feed efficiency information; 2) served as proxies for body weight traits not routinely collected on farms; and 3) were associated with breeding values for feed efficiency traits, including: predicted transmitting abilities (PTA) for feed saved (FS), RFI and body weight composite (BWC). Ontology enrichment analysis was used to identify enriched pathways from the contrast of extreme RFI cows by GC-MS and LC-MS. The false discovery rate (FDR, reported as q-values) and Hommel corrections were used as multiple testing corrections. Partial least squares discriminate analysis confirmed animals could be classified as high or low feed efficiency groups. A total of 33 GC-MS metabolites, 10 LC-MS ontology pathways (both q<0.20) and 42 fatty acid or milk quality assays from on-farm tests (Hommel adj-p<0.05) were identified for analyses 1, 2, and 3 respectively.Ontology enrichment analysis of LC-MS data identified the sphingolipid metabolism (analysis 2), and beta-alanine metabolism (analysis 1) pathways (q<0.20). Heptanoic acid was identified as associated with PTA BWC (GC-MS, model 3; q<0.15). These metabolites represent new candidate indicator traits for feed efficiency that could be included in existing on-farm milk testing systems upon validation.