Invited review: Advances and applications of random regression models: From quantitative genetics to genomics

Abstract

An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

“…Firstly, continuous monitoring of the animal welfare state from birth to slaughter (or involuntary culling) is needed because animals can be more or less prone to certain welfare issues at specific life stages [e.g., food allergies and gut inflammation after weaning in piglets (Jayaraman and Nyachoti, 2017;Radcliffe et al, 2019), tail biting and aggressive behaviors after mixing pigs in larger groups (Camerlink et al, 2013;Shen et al, 2019), feather pecking in laying hens (Ellen et al, 2019), and age-specific disease occurrences such as mastitis in dairy species (Barkema et al, 2015)]. Therefore, longitudinal phenotypes need to be collected and analyzed (Rauw and Gomez-Raya, 2015;Berghof et al, 2019;Oliveira et al, 2019a). Resilience, defined as the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state attained before exposure to a disturbance (Berghof et al, 2019), can also indicate welfare.…”

“…Big data handling and manipulation requires good computational infrastructure and efficient programming methods (Nayeri et al, 2019). Furthermore, most PLF devices generate repeated records for each individual [i.e., longitudinal traits (Oliveira et al, 2019a)], which are highly desirable for monitoring livestock welfare. However, the covariance structure among records needs to be considered in the statistical models (Oliveira et al, 2019a).…”

“…Therefore, this wide range of environments facilitate the investigation of changes in the expression of traits through a continuous descriptor of environments (Rauw and Gomez-Raya, 2015). In this context, Ravagnolo and Misztal (2002) estimated the genetic component of heat tolerance for non-return rate in Holstein cattle using a random regression animal model (Oliveira et al, 2019a) and temperature humidity index (THI). THI was calculated using temperature and humidity data provided by public weather stations, which can be obtained from on-line sources in various countries.…”

Transition Period of the Dairy Cow Revisited: II. Homeorhetic Stimulus and Ketosis With Implication for Fertility

“…A significant shift in animal breeding occurred with the implementation of genomic selection in the United States, Great Britain, Ireland, New Zealand, Australia, the Netherlands, France, the Scandinavian countries, and Germany (Silva et al, 2014). Huge numbers of genomics markers of the livestock genome (single nucleotide polymorphisms-SNP) are available and, for instance, have been demonstrated for production traits such as lactation curve, milk, fat, and protein (Oliveira et al, 2019) and various health traits such as displaced abomasum (Zerbin et al, 2015;Lehner et al, 2018), cystic ovaries, displaced abomasum, ketosis, lameness, mastitis, metritis, retained placenta (Parker Gaddis et al, 2014), mastitis, metritis, retained placenta, displaced abomasum, ketosis, and lameness, (Vukasinovic et al, 2017), association between reproductive performance and serum IGF-1 (Gobikrushanth et al, 2018), ketosis (Parker Gaddis et al, 2018;Kroezen et al, 2018), and somatic cell score (Oliveira et al, 2019). As a consequence, Zoetis Genetic offered, for the first time, an evaluation for wellness traits of Holsteins.…”

Autoregressive and random regression test‐day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

“…The most commonly used test‐day (TD) model for genetic evaluation of multiple lactations is probably the multi‐trait random regression (RR) model (Oliveira, Brito, et al, ). Several members of Interbull, such as Canada (Schaeffer, Jamrozik, Kistemaker, & Doormaal, ), Germany, Austria, Luxembourg (Liu, Reinhardt, & Reents, ), Italy (Muir, Kistemaker, Jamrozik, & Canavesi, ), Netherlands (Roos, Harbers, & Jong, ), New Zealand (Harris, Winkelman, Johnson, & Montgomerie, ) and others, have implemented RR models in their national dairy cattle genetic evaluations (Interbull, ).…”