Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

基因组育种计划对于提高牲畜生产效率性状的遗传进展率至关重要。这种改进伴随着生产系统的集约化、在日常管理实践中使用更广泛的精密技术以及高通量表型分析。同时，公众对动物福利的认识不断提高，影响畜牧生产者更加重视与生产性状相关的福利。因此，近年来，人们开发了牲畜管理实践和饲养技术，以提高动物福利。特别是，基因组选择可用于改善牲畜的社会行为、对疾病和其他应激因素的抵抗力，并缓解对生产系统变化的习惯。将新的行为和福利性状纳入基因组育种计划的主要要求是：（1）识别代表行业育种目标的生物学机制的性状；(2) 在大量动物上测量的个体表型记录的可用性（最好有基因组信息）；(3) 衍生性状具有遗传性、生物学意义、可重复性，并且（理想情况下）与选择指标中已包含的其他性状不高度相关；(4)大量具有表型记录的个体（或遗传上相近的个体）的基因组信息是可用的。在这篇综述中，我们（1）描述了一种为遗传和基因组选择方案开发新的福利指标性状（使用理想表型）的潜在途径；(2) 总结牲畜行为和福利的关键指标变量，包括对牲畜热应激的详细评估；(3) 描述可用于动物福利大规模数据分析的主要统计和生物信息学方法；(4) 确定生成高通量和大规模数据集的重大进展、挑战和机遇，以实现遗传和基因组选择，从而改善牲畜福利。各种新颖的福利指标特征可以从现代技术捕获的信息中得出，例如传感器、自动喂食系统、挤奶机器人、活动监视器、摄像机以及细胞和生理水平的间接生物标志物。基于最近开发（或正在开发）的技术，新性状的开发与改善牲畜福利的基因组选择方案相结合是可行的和优化的。有效实施遗传和基因组选择以改善动物福利还需要整合多个科学领域，例如细胞和分子生物学、神经科学、免疫学、应激生理学、计算机科学、工程学、定量基因组学和生物信息学。