Major advances in genetic evaluation techniques

Phenotypic and genomic modeling of lactation curves: A longitudinal perspective

Lactation curves, which describe the production pattern of milk-related traits over time, provide insightful information about individual cow health, resilience, and milk production efficiency. Key functional traits can be derived through lactation curve modeling, such as lactation peak and persistency. Furthermore, novel traits such as resilience indicators can be derived based on the variability of the deviations of observed milk yield from the expected lactation curve fitted for each animal. Lactation curve parameters are heritable, indicating that one can modify the average lactation curve of a population through selective breeding. Various statistical methods can be used for modeling longitudinal traits. Among them, the use of random regression models enables a more flexible and robust modeling of lactation curves compared with traditional models used to evaluate accumulated milk 305-d yield, as they enable the estimation of both genetic and environmental effects affecting milk production traits over time. In this symposium review, we discuss the importance of evaluating lactation curves from a longitudinal perspective and various statistical and mathematical models used to analyze longitudinal data. We also highlighted the key factors that influence milk production over time, and the potential applications of longitudinal analyses of lactation curves in improving animal health, resilience, and milk production efficiency. Overall, analyzing the longitudinal nature of milk yield will continue to play a crucial role in improving the production efficiency and sustainability of the dairy industry, and the methods and models developed can be easily translated to other longitudinal traits.

Body conformation traits in early-lactation associated with clinical mastitis and lameness in lactating Chinese holstein cows

BACKGROUND

Comprehending the correlation between body conformation traits of cows at the early stages of lactation and prevalent lactation diseases might facilitate the execution of selection and feeding strategies that prioritize cow health. This study aimed to evaluate the impact of body conformation traits on the incidence of clinical mastitis and lameness in Chinese Holstein cows. From a pasture herd of 1472 early lactating Chinese Holstein cows, we evaluated 20 body conformation traits. During lactation, this pasture herd was visited weekly to gather clinical mastitis and lameness data. A nine-point scale was used to determine the conformation traits of cows to clarify their linear characters, including frame capacity, rump (RU), feet and leg (FL), mammary system (MS), and dairy character. A longitudinal binary disease (0 = healthy; 1 = diseased) data structure was created by allocating disease records to adjacent official test dates. The impact of body conformation traits on the risk of developing diseases (clinical mastitis and lameness) was analyzed using the logistic regression models.

RESULTS

Compared to cows with low total scores (75-79 points), those with high total scores (80-85 points) of body conformation traits had a significantly lower risk of mastitis (P < 0.001). The disease status (0 or 1: binary variable) of clinical mastitis in lactating cows was significantly impacted negatively by age (P < 0.05). The fore udder attachment (FUA), angularity, rear attachment height (RAH), and rear teat placement (RTP) were all significantly associated with clinical mastitis during lactation (P < 0.05). The rear leg-rear view (RLRV) was significantly correlated with correlated considerably (P < 0.05) with lameness during lactation. An ideal score of four points on the lameness risk dimension of the RLRV may indicate a low risk of lameness. Since the risk of mastitis decreased as this trait score increased, the RTP may be an ideal marker for mastitis risk.

CONCLUSIONS

According to the study, clinical mastitis and lameness risks in cows can be estimated using their body conformation traits. Cows with more centrally located rear teats have a lower risk of mastitis. These results may help dairy farmers identify cows at high risk of disease early in lactation and aid in breeding for disease resistance in cows.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

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

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.

Evolutionary rates for multivariate traits: the role of selection and genetic variation

A fundamental question in evolutionary biology is the relative importance of selection and genetic architecture in determining evolutionary rates. Adaptive evolution can be described by the multivariate breeders' equation (Δz(-)=Gβ), which predicts evolutionary change for a suite of phenotypic traits (Δz(-)) as a product of directional selection acting on them (β) and the genetic variance-covariance matrix for those traits (G ). Despite being empirically challenging to estimate, there are enough published estimates of G and β to allow for synthesis of general patterns across species. We use published estimates to test the hypotheses that there are systematic differences in the rate of evolution among trait types, and that these differences are, in part, due to genetic architecture. We find some evidence that sexually selected traits exhibit faster rates of evolution compared with life-history or morphological traits. This difference does not appear to be related to stronger selection on sexually selected traits. Using numerous proposed approaches to quantifying the shape, size and structure of G, we examine how these parameters relate to one another, and how they vary among taxonomic and trait groupings. Despite considerable variation, they do not explain the observed differences in evolutionary rates.

Patterns of quantitative genetic variation in multiple dimensions

A fundamental question for both evolutionary biologists and breeders is the extent to which genetic correlations limit the ability of populations to respond to selection. Here I view this topic from three perspectives. First, I propose several nondimensional statistics to quantify the genetic variation present in a suite of traits and to describe the extent to which correlations limit their selection response. A review of five data sets suggests that the total variation differs substantially between populations. In all cases analyzed, however, the "effective number of dimensions" is less than two: more than half of the total genetic variation is explained by a single combination of traits. Second, I consider how patterns of variation affect the average evolutionary response to selection in a random direction. When genetic variation lies in a small number of dimensions but there are a large number of traits under selection, then the average selection response will be reduced substantially from its potential maximum. Third, I discuss how a low genetic correlation between male fitness and female fitness limits the ability of populations to adapt. Data from two recent studies of natural populations suggest this correlation can diminish or even erase any genetic benefit to mate choice. Together these results suggest that adaptation (in natural populations) and genetic improvement (in domesticated populations) may often be as much constrained by patterns of genetic correlation as by the overall amount of genetic variation.

Across-family marker-assisted selection using selective genotyping strategies in dairy cattle breeding schemes

This study investigated the potential loss expected from marker-assisted selection (MAS) when only a proportion of animals are genotyped using several selective genotyping strategies. A population resembling a commercial dairy cattle population over 25 yr was simulated, and the most informative individuals for genotyping were identified among the potential breeding candidates (young bulls and bull-dams). Two strategies were used to identify the most informative animals. The first genotyping strategy was based on selecting individuals for genotyping with predicted total genetic effect [sum of the predicted quantitative trait locus (QTL) and polygenic effects] close to the truncation point for selection. The second strategy used an index that extended the previous strategy to include the variance due to segregation of the QTL in the parents. The 2 strategies for selective genotyping were applied at the 2 different genotyping levels and compared with random selection of candidates for genotyping and complete genotyping of the potential candidates. All selective genotyping strategies at the same proportion of genotyping showed similar cumulative genetic level. The frequency of the favorable QTL allele increased faster with more animals genotyped. Extra response in total genetic effect (polygenic and QTL) was not significantly different between genotyping all candidates (100%), 20%, and 50% genotyping (except for yr 13), but all MAS strategies resulted in significantly higher response than BLUP until yr 18. With 50% (20%) genotyping of candidates for selection within a population, 95% (89%) of maximum cumulative QTL response was achieved in yr 13. All MAS schemes resulted in a 19% decrease in the rate of inbreeding compared with the BLUP scheme. Therefore, it is possible to use selective genotyping in practical dairy cattle breeding and decrease the genotyping costs with a minimal loss of response compared with complete genotyping of the potential candidates.