Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

**VISION** Creating 624 jobs and £64M p.a. additional GVA, the Digital Dairy Value-Chain will deliver an uplift in the rural economies of Cumbria and South West Scotland. It will develop a world-class research, innovation, business and skills platform to establish the region as a leader in advanced, sustainable and high-value dairy manufacturing. Inclusive, innovation-led growth will also deliver wider societal and environmental gains including reductions in carbon footprints. The project will develop innovative technologies (e.g. sensors, IoT, 5G-communications, blockchain), infrastructure and advanced manufacturing processes to create a fully-integrated and traceable supply chain. It will generate new opportunities to optimise manufacturing, develop new products and markets, and valorise consumer concerns around dairy production. **GEOGRAPHY** Spanning national boundaries, Cumbria and SW Scotland produce 1.9bn litres of milk annually. As the UK's second largest milk producer, dairy manufacturing is an important source of economic activity and employment. Our geography is remote and rural. Regional GVA is well below the national average, high-value employment opportunities are limited and business innovation activity is patchy. The impacts of BREXIT and changes to agricultural support will be significant. This project focuses on AgriFood manufacturing, sustainable exploitation of natural capital, and digitalisation; all are priorities for regional growth. With 63% of our local authority areas having a negative prosperity gap there is significant untapped human capital. Social inclusivity goals cut across our activities particularly focusing on advancing opportunity for young people and women who are under-represented in dairying, STEM and business innovation. **NETWORK** We draw on an impressive network of world-class research capability (SRUC; University of Strathclyde; University of the West of Scotland); technology innovation centre (CENSIS); regional and multi-national dairy-processing companies (First Milk, Lactalis, Arla, Kendal Nutricare, Appleby Creamery); SmartSTEMs; and technology businesses (Lely, Seric, North). The project is backed by strong support from civic leadership. **OBJECTIVES** The project will: * **Develop digital connectivity.** Establish the region as a beacon for digitally-connected, value-added milk processing. * **Stimulate R&D.** Facilitate access to state-of-the-art infrastructure and expertise, catalyse collaborative R&D and commercialisation of innovative products and processes. * **Facilitate business growth.** Provide business support and facilities for early ventures, scale-up businesses and established companies to undertake New Product Development (NPD) and process improvement. * **Attract talent and skills.** Foster a talent pipeline that advances opportunity and enables industry to exploit new technologies and market opportunities. This project leverages investments in the Borderlands Inclusive Growth Deal, Ayrshire Growth Deal, and Cumbria's Food Enterprise Zone.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

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.