The Resilient Dairy Genome Project - a general overview of methods and objectives related to feed efficiency and methane emissions.

van Staaveren, Nienke; Oliveira, Hinayah R; Houlahan, Kerry; Chud, Tatiane C S; Oliveira, Gerson A; Hailemariam, Dagnachew; Kistemaker, Gerrit; Miglior, Filippo; Plastow, Graham; Schenkel, Flavio S; Cerri, Ronaldo; Sirard, Marc-André; Stothard, Paul; Pryce, Jennie; Butty, Adrien; Stratz, Patrick; Abdalla, Emhimad A E; Segelke, Dierck; Stamer, Eckhard; Thaller, Georg; ... (2024). The Resilient Dairy Genome Project - a general overview of methods and objectives related to feed efficiency and methane emissions. Journal of dairy science, 107(3), pp. 1510-1522. Elsevier 10.3168/jds.2022-22951

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The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries, i.e., Australia [AUS], Canada [CAN], Denmark [DNK], Germany [DEU], Spain [ESP], Switzerland [CHE], and United States of America [USA] contribute with genotypes and phenotypes including DMI and CH4. However, combining data is challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

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