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The development of genomics-based tools has resulted in a significant shift in our understanding of macrosystems biology, particularly for humans and livestock. Our ability to conduct high-throughput DNA sequencing has not only allowed for sequencing of the bovine genome, but has also facilitated the characterization of the millions of microbes that live in and on dairy cows. Our ability to better understand the microbial communities within the rumen and gastrointestinal tract has opened the door for making improvements in milk production efficiency (MPE) by manipulating these microbiota. This is of particular importance, given that ruminal microbes are the essential agents of feed conversion to usable nutrients and are further responsible for generating the building blocks for nearly all milk nutrients during lactation. Therefore, the ability to select for an MPE-enhancing ruminal microbiota is of great interest, but is hampered by our inability to effectively characterize this microbial trait in a high-throughput and non-invasive manner. Here, we propose to address this by applying a recently developed high-throughput, noninvasive approach for sampling the ruminal microbiota to 800 genotyped dairy cows across two USDA farms. We will further perform the first genome-wide association study (GWAS) in dairy cows that considers the ruminal microbiota in relation to MPE, and will make this data publicly available through the BovineMine database. In sum, the overall goal of this project is to generate the first large-scale ruminal microbiota dataset correlated to host genotype as a publicly available resource to expedite the discovery of microbial traits useful for improving MPE in dairy cows. To accomplish this, we propose the following three aims:Aim 1. Characterize the ruminal microbiota of 800 genotyped cows across two dairy farms over time.Aim 2. Identify correlations between host genetic markers and ruminal microbial taxa as it relates to MPE.Aim 3. Integrate host genotype, ruminal microbiota, and milk production data into BovineMine.

Suen, G.
University of Wisconsin - Madison
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