Application of machine learning to genomic selection of dairy cattle through improved feed efficiency complex prediction

奶畜行业的可持续性和盈利能力取决于满足消费者期望的产品生产、动物健康和福祉的维护以及资源和投入的有效管理之间的平衡相互作用。动物的饲料效率（FE）综合体，定义为使用更少的饲料资源生产相同数量的乳制品，是一个关键的生产力指标，估计乳制品生产总成本的60%与喂养有关。鉴于目前国际上对温室气体排放、营养损失和水质的关注，促进FE复合物的改善是乳制品生产系统的一个重要重点。FE复合物受许多因素影响，包括谱系、生理、环境和基因组学变化，因此基于线性方法的传统方法量化FE复合物具有有限的统计功效和准确性。替代的更全面的方法，如基于机器学习（ML）的方法，提供了整合异构变量和更有效地利用高维数据的潜力。ML技术可以突出影响FE复合物的最重要因素，这些选定的变量可以用于开发改进的模型，可以更准确地预测FE复合物，并提供有关FE复合物背后的生物过程的新知识。该项目将寻求利用数据驱动的方法，通过结合谱系，生理，环境和基因组学信息，使用ML来提高FE复杂性的预测准确性。在农业食品和生物科学研究所（AFBI）过去20年整理的专门构建的数据库中捕获的高精度数据集将通过北方爱尔兰农场动物生物库（NIFAB）进行访问。这些数据集包含系谱、每周采食量和饲料分析、管理和其他环境数据、日常生产记录和基因组学信息，为更好地了解奶牛FE复合体提供了独特的机会，并通过识别关键基因和相关生物学途径来提高我们对FE复合体基因组影响的认识。这些信息最终将使英国的奶农能够在牛群中选择最高效的奶牛来繁殖替代品，以及在这些奶牛上使用最高效的公猪。因此，饲料成本将降低，盈利能力将提高，减少生产系统的效率低下和营养损失。作为一个案例学生，该项目将提供一个独特的机会，直接与AFBI合作，了解农业食品部门的需求和研究差距。

The sustainability and profitability of the dairy livestock sector is dependent on a balanced interplay between the production of produce that can meet consumer expectations, the maintenance of animal health and wellbeing, and the efficient management of resources and inputs. Feed efficiency (FE) complex in animals, as defined by the production of the same quantity of dairy product using fewer feeding resources, is a key productivity indicator with an estimated 60% of the total costs of dairy production being associated with feeding. Given current international concerns regarding greenhouse gas emissions, nutrient losses, and water quality, facilitating improvements to FE complex is an important focus of dairy-based production systems. FE complex is affected by many factors, including pedigree, physiological, environmental, and genomics variations, and therefore traditional methods based on linear approaches that quantify FE complex have limited statistical power and accuracy. Alternative more comprehensive approaches such as machine learning (ML)-based methods offer the potential to integrate heterogeneous variables and utilise high dimensional data more efficiently. ML techniques can highlight the most important factors influencing FE complex and these selected variables can be used to develop improved models that can more accurately predict FE complex and provide new knowledge on the biological processes underlying the FE complex. This project will seek to utilise a data-driven approach using ML by combining pedigree, physiological, environmental, and genomics information to provide improved prediction accuracy in the FE complex. High precision datasets, captured within a specially constructed database, collated at the Agri-Food and Bioscience Institute (AFBI) over the last 20 years will be made accessible to this work through the Northern Ireland Farm Animal Biobank (NIFAB). These datasets, containing pedigree, weekly feed intake and feed analysis, management and other environmental data, daily production records, and genomics information, present a unique opportunity to better understand the FE complex in dairy cattle, and improve our knowledge on genomic influence on FE complex by identifying key genes and associated biological pathways. This information will ultimately allow dairy farmers in the UK to select the most efficient cows within the herd from which to breed replacements, and the most food efficient sires to use on these cows. As a consequence, feed costs will be reduced and profitability improved, mitigating inefficiencies in production systems and nutrient losses. As a CASE studentship, this project will provide a unique opportunity to work directly with AFBI and learn about the needs and research gaps of the agri-food sector.