Integrating genomics and transcriptomics to empower dairy breeding for feed efficient animals Improving feed efficiency (FE) of dairy cattle has been a major interest for animal scientists and dairy farmers over decades. Feed efficiency is a complex trait in dairy cattle and is highly linked to milk yield and methane emission in dairy production. Improving FE of dairy cattle will increase profits of dairy farmers and reduce methane emissions of UK dairy population. Including FE into genetic improvement scheme is one of the top priorities in the UK dairy breeding now. The overall aim of the proposal is to understand the genetic basis of dairy feed efficiency through integrating population-level phenotypic, genomic, and transcriptomic data, which will be exploited to empower dairy breeding for feed efficient and environmentally-friendly animals. The project builds on SRUC's award-wining Dairy Research Centre (Queen's Anniversary Prize) with 50-year data recording on dairy feed efficiency on a population level. We will apply new methods we recently published on Nature Genetics to this data, to fully utilize and integrate animals' genomic and transcriptomic profiles into understanding genetics of feed efficiency. To achieve the overall aim of the project, we will: (i) Identify cattle genes and genomic regions that are associated with FE using population-level phenotypic and genomic data; (ii) Characterize the genes and variants of the cattle genome whose expression is important to FE using genomic and RNA-sequencing information; (iii) Develop methods and apply the newly acquired genomic and regulatory variants to enhance genomic selection for FE in dairy breeding. The proposed project builds on SRUC's award-winning Dairy Research Centre with 50-year dairy recording on feed intake, milk production and composition, body weight (BW) and condition, health status, and reproductive events per animal. Three work packages (WP) will be conducted: (i) WP1: We will generate whole genome-sequencing (WGS) data for 40 representative animals in the study population, plus WGS data we previously sequenced or obtained from publicly-available database. We will use these sequence data to facilitate genotype imputation to obtain sequence-level genotypes in the study population. We will conduct association analyses to identify genes and regions associated with FE using sequence-level genotypes. (ii) WP2: We will obtain RNA-sequencing for 200 animals in the study population from blood samples. Animals' transcriptomic profiles will be integrated with their genomic data and FE phenotypes to identify regulatory variants associated with FE. (iii) WP3: We will apply the newly acquired genomic and regulatory variants from WP1 and WP2 into genomic prediction for FE, to develop and assess methods of genomic prediction for FE using the functional variants. The present project will facilitate improved FE and reduced methane emission in the dairy population, highly relevant to BBSRC's strategic priorities in bioscience for sustainable agriculture and priority areas in data-driven biology. This project has extremely important scientific, economic, and social impact on understanding complex trait of feed efficiency, increasing profits of farmers and dairy industry, and mitigating methane emissions of UK dairy population. The project uses high-quality data and innovative methods to provide the scientific community a role model of integrating population-level omics data into genetic research for complex traits in animals and plants. The research outcome will have great potential to be applied to the UK national genetic improvement programs of dairy cattle. The research outcome will facilitate at least 1% extra genetic progress for UK dairy breeding, worth millions of pounds per year for the UK dairy industry.