Genetic and management solutions for lameness-associated endemic diseases in dairy cattle

Cattle lameness is a debilitating and painful condition, and is described as one of the clearest indicators of compromised welfare in dairy cattle and one of the most important causes of involuntary removal and replacement of animals. No other common condition is associated with such visible signs of pain and, as such, in addition to inflicting serious grief to the animal, lameness also damages the public's perception of the livestock industry and poses a huge reputational threat. The project we propose here will collate large amounts of cattle lameness data from different sources, including UK foot trimmers, milk recording agencies, previous BBSRC funded work, and video analytics empowered by artificial intelligence. This effort will build upon work conducted by the University of Liverpool and funded by AHDB and BBSRC and will be supported by a number of key industry partners.
Our specific objectives are:
1. To collate lameness associated data from various complementary sources and develop a unique, comprehensive database for cattle lameness.
2. To perform genetic and genomic analyses of current and novel lameness-related phenotypes.
3. Develop novel strategies to incorporate the studied lameness phenotypes in biology-driven breeding programmes.
4. Use the generated database to offer additional management insights to UK dairy farmers.
5. Achieve widespread and rapid impact via an extensive knowledge exchange (KE) programme underpinned by implementation science research.
The unique database generated in Objective 1 will be used for the genetic and genomic analyses of multiple animal traits related to animal resistance to endemic diseases associated with cattle lameness, aiming to generate new and enrich existing tools underpinning selective breeding and genetic improvement. Different breeding strategies will then be evaluated, based on these results, and their efficacy in controlling lameness will be determined. The database will be also used to generate management insights and develop relevant practices for on-farm use. More specifically, we will: 1) develop a lameness epidemiological pattern analysis tool, 2) develop models that will predict future lameness status of an individual animal, and 3) quantify the impact of lameness on future farm performance. These novel analytical tools will allow farmers to better manage lameness in their herds. Together, the industry partners collaborating for this proposal, provide an impressive suite of well-established knowledge exchange (KE) networks, opportunities and farmer training platforms. In combination with the research team, this proposal also unites a wealth of experience in delivering a diverse range of KE activities to farmers and vets. As the outputs from the project become available for use, this highly influential coalition of research and industry partners will be able to provide harmonized communications and widespread, high quality training and support for farmers, foot trimmers and vets.

We will continue our effort to create a national database of UK foot trimmers' records. This will build upon work conducted by the University of Liverpool (UoL). The UoL will spend the first year of this project helping a large number of foot trimmers and farmers to share their data. Daily mobility scores performed automatically on approximately 15,000 cows by CattleEye will also be included in this dataset. Genotypes for a subset of approximately 8,000 animals are also available. Rapid qualitative approaches will be used to accelerate widespread engagement within the first year. Individual phenotypes, pedigree and genotypes in the project database will then be jointly analysed to estimate genetic parameters and determine genetic variants associated with the various lameness-related phenotypes. Phenotypic and genomic outcomes will be integrated in a series of simulation studies to determine ways to inform future breeding programmes aiming to control cattle lameness while continuing the improvement of other important animal traits associated with cow production, health, fertility, and longevity. Foot trimming lesion data and mobility scores (human or CattleEye generated) will be used to develop novel KPIs for lesion incidence or lameness, temporal patterns of prevalence of lameness, and a herd level diagnosis of the predominant epidemiological pattern of diseases associated with lameness within the herd. We will then utilise a range of traditional statistical methods and machine learning methods to predict several key animal events relevant to lameness control, namely the probability and time to; a new (first) case of lameness; of curing once lame; and of recurrence of lameness once recovered. We will also quantify the association of lameness prediction scores with future milk production and reproductive performance. Widespread and rapid impact will be achieved via an extensive knowledge exchange programme underpinned by implementation science research.