Data modelling for animal phenomics

动物表型组学的数据建模

• 批准号: RGPIN-2022-03452
• 负责人: Tulpan, Dan
• 金额: $ 2.11万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743566
• 关键词: Data; modelling; animal; phenomics

One of the major limiting factors impacting multiple areas of animal science such as livestock breeding, production and genomics is related to collecting, managing and effectively modeling abundant, real-time, high-throughput and high-quality phenotypic data. Significant advancements in closing the gap between phenotypes and genotypes and developing a better understanding of complex interactions between phenotypes and production systems can be achieved by standardizing and automating phenotype collection protocols, improving the underlining collection technologies and augmenting the collected phenotypic information with environmental, geographic, genotypic, physical and physiological data 1,2. Significant efforts are being made by both, researchers and private businesses, to develop systems and technologies that harvest, store, model and analyze animal-related information synchronously within periodical and pre-established time frames relevant to specific phenotypic features and various steps of multiple production value chains. Given the large number of components of an animal phenotype such as morphological, developmental, biochemical, physiological and behavioural, this research proposal will focus on the study of phenotypes related to animal growth. The short-term goals of my research project include: (S1) developing efficient and accurate semi-automatic and automatic image acquisition and processing protocols for pigs, dairy and beef cattle using affordable sensors and reference objects, (S2) extracting and analyzing morphometric information (e.g. body dimensions, posture, body condition score and motion patterns) from images using data mining, computer vision and machine/deep learning methods, (S3) investigating the level of correlation among various morphometric measurements, animal physiological states and animal development/growth and their putative use in models capable to estimate or predict growth, and (S4) developing species-specific regression and classification phenotypic models for estimation of growth represented by live body weights and body condition scores (and other relevant existing and potentially new traits) based on digital images that include reference objects with known dimensions. The long-term goals include: (L1) researching methods that increase the accuracy and usability of semi-automatic and automatic phenotypic data collection, which can be further integrated with complementary livestock information to produce animals more efficiently and more sustainably, and to improve economic outcomes on the farm, (L2) collecting and integrating phenotypic results into models that can be used to develop predictive decision support systems for the Canadian industry, and (L3) recruiting and training highly qualified personnel in livestock phenomics.

影响动物科学多个领域（如畜牧养殖、生产和基因组学）的主要限制因素之一与收集、管理和有效建模丰富、实时、高通量和高质量的表型数据有关。通过标准化和自动化表型收集方案，改善底层收集技术，并利用环境、地理、基因型、物理和生理数据增加收集的表型信息，可以在缩小表型和基因型之间的差距以及更好地理解表型和生产系统之间的复杂相互作用方面取得重大进展1，2。研究人员和私营企业都在作出重大努力，开发系统和技术，以便在与特定表型特征和多个生产价值链的各个步骤有关的定期和预先确定的时间框架内同步采集、储存、建模和分析与动物有关的信息。鉴于动物表型的大量组成部分，例如形态、发育、生化、生理和行为，本研究提案将重点研究与动物生长相关的表型。我的研究项目的短期目标包括：（S1）使用可负担得起的传感器和参考对象开发用于猪、奶牛和肉牛的高效且准确的半自动和自动图像采集和处理协议，（S2）提取和分析形态测量信息（例如，身体尺寸、姿势、身体状况评分和运动模式），（S3）研究各种形态测量、动物生理状态和动物发育/生长之间的相关性水平及其在能够估计或预测生长的模型中的推定用途，以及（S4）开发物种特异性回归和分类表型模型，用于估计由活体重量和身体状况评分表示的生长（以及其他相关的现有的和潜在的新的特征），其基于包括具有已知尺寸的参考对象的数字图像。长期目标包括：（L1）研究提高半自动和自动表型数据收集的准确性和可用性的方法，所述表型数据收集可以进一步与补充牲畜信息整合，以更有效和更可持续地生产动物，并改善农场的经济成果，（L2）收集表型结果并将其整合到可以用于开发加拿大产业的预测决策支持系统的模型中，以及（L3）招募和培训家畜表型组学方面的高素质人员。