Predicting survival in dairy cattle by combining genomic breeding values and phenotypic information

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

Genomic and Phenotypic Udder Evaluation for Dairy Cattle Selection: A Review

The traditional point of view regarding dairy cattle selection has been challenged by recent genomic studies indicating that livestock productivity prediction can be redefined based on the evaluation of genomic and phenotypic data. Several studies that included different genomic-derived traits only indicated that interactions among them or even with conventional phenotypic evaluation criteria require further elucidation. Unfortunately, certain genomic and phenotypic-derived traits have been shown to be secondary factors influencing dairy production. Thus, these factors, as well as evaluation criteria, need to be defined. Owing to the variety of genomic and phenotypic udder-derived traits which may affect the modern dairy cow functionality and conformation, a definition of currently important traits in the broad sense is indicated. This is essential for cattle productivity and dairy sustainability. The main objective of the present review is to elucidate the possible relationships among genomic and phenotypic udder evaluation characteristics to define the most relevant traits related to selection for function and conformation in dairy cattle. This review aims to examine the potential impact of various udder-related evaluation criteria on dairy cattle productivity and explore how to mitigate the adverse effects of compromised udder conformation and functionality. Specifically, we will consider the implications for udder health, welfare, longevity, and production-derived traits. Subsequently, we will address several concerns covering the application of genomic and phenotypic evaluation criteria with emphasis on udder-related traits in dairy cattle selection as well as its evolution from origins to the present and future prospects.

Estimation of Dairy Cow Survival in the First Three Lactations for Different Culling Reasons Using the Kaplan-Meier Method

The aims of the study were: (i) to compare survival curves for cows culled for different reasons over three successive lactations using the Kaplan-Meier estimator; (ii) to determine the effects of breeding documentation parameters on cow survival; (iii) to investigate the similarity between culling categories. The survival times for a subset of 347,939 Holstein-Friesian cows culled between 2017 and 2018 in Poland were expressed in months from calving to culling or the end of lactation. The survival tables were constructed for each culling category and lactation number. The survival curves were also compared. The main culling categories were reproductive disorders-40%, udder diseases-13 to 15%, and locomotor system diseases-above 10%. The survival curves for cows from individual culling categories had similar shapes. A low probability of survival curves for metabolic and digestive system diseases and respiratory diseases was observed in each of the three lactations. The contagious disease category was almost non-existent in the first lactation. The greatest influence on the relative culling risk was exerted by age at first calving, lactation length, calving interval, production subindex, breeding value for longevity, temperament, and average daily milk yield. A more accurate method of determining culling reasons would be required.

Reliabilities of Genomic Prediction for Young Stock Survival Traits Using 54K SNP Chip Augmented With Additional Single-Nucleotide Polymorphisms Selected From Imputed Whole-Genome Sequencing Data

This study investigated effects of integrating single-nucleotide polymorphisms (SNPs) selected based on previous genome-wide association studies (GWASs), from imputed whole-genome sequencing (WGS) data, in the conventional 54K chip on genomic prediction reliability of young stock survival (YSS) traits in dairy cattle. The WGS SNPs included two groups of SNP sets that were selected based on GWAS in the Danish Holstein for YSS index (YSS_SNPs, n = 98) and SNPs chosen as peaks of quantitative trait loci for the traits of Nordic total merit index in Denmark–Finland–Sweden dairy cattle populations (DFS_SNPs, n = 1,541). Additionally, the study also investigated the possibility of improving genomic prediction reliability for survival traits by modeling the SNPs within recessive lethal haplotypes (LET_SNP, n = 130) detected from the 54K chip in the Nordic Holstein. De-regressed proofs (DRPs) were obtained from 6,558 Danish Holstein bulls genotyped with either 54K chip or customized LD chip that includes SNPs in the standard LD chip and some of the selected WGS SNPs. The chip data were subsequently imputed to 54K SNP together with the selected WGS SNPs. Genomic best linear unbiased prediction (GBLUP) models were implemented to predict breeding values through either pooling the 54K and selected WGS SNPs together as one genetic component (a one-component model) or considering 54K SNPs and selected WGS SNPs as two separate genetic components (a two-component model). Across all the traits, inclusion of each of the selected WGS SNP sets led to negligible improvements in prediction accuracies (0.17 percentage points on average) compared to prediction using only 54K. Similarly, marginal improvement in prediction reliability was obtained when all the selected WGS SNPs were included (0.22 percentage points). No further improvement in prediction reliability was observed when considering random regression on genotype code of recessive lethal alleles in the model including both groups of the WGS SNPs. Additionally, there was no difference in prediction reliability from integrating the selected WGS SNP sets through the two-component model compared to the one-component GBLUP.

Improving the accuracy of predictions for cow survival by multivariate evaluation model

Context Cow survival measures the ability of cows to survive from the current to subsequent lactation. In addition to economic gain, genetic selection for survival could improve animal welfare by increasing the adaptability and resilience of the cows to both environmental and health challenges. However, survival is a complex trait because it results from a diverse range of reasons for culling of cows from the herd. Consequently, the accuracy of genetic predictions of direct survival are often low. Aims Our aim was to increase the accuracy of predictions of survival in Holstein and Jersey sires by including important predictor traits in multi-trait evaluation models. Methods Phenotypic and genetic correlations between survival trait deviations (TDs) and 35 routinely measured traits (including milk yield, fertility and type traits) were estimated using bivariate sire models. Survival TDs for 538 394 Holstein and 63 839 Jersey cows were used in our study; these cows or their close relatives also had milk, fertility and type traits records between 2002 and 2019. These genetic parameters were required to assess the potential usefulness of predictor traits for the prediction of survival. Key results Survival was genetically correlated with milk, fat and protein yields, overall type, composite mammary system and fertility TDs in both Holstein and Jersey. Further, most of the type traits related to feet and legs, and rump, were also correlated with survival TDs in Jersey. For sires, the accuracy of predictions for survival increased by 0.05 for Holsteins (from 0.54 to 0.59) and for Jerseys (from 0.48 to 0.53) through the use of multivariate models compared with univariate models. Conclusions Survival was genetically associated with traits affecting voluntary and involuntary culling and when included in multi-trait genetic evaluation models, they moderately improved the accuracy of genetic prediction of survival. Implications Predictor traits can be used to increase the accuracy of predictions of survival through the use of multi-trait models. The inclusion of breed-specific predictor traits should be considered, especially for Jerseys in genetic evaluations of survival.

Analysis of culling reasons and age at culling in Australian dairy cattle

Context A thorough analysis of the reasons for culling was made to understand the phenotypic trend in herd life. In addition, identification of culling reasons could enable to develop a strategy for further evaluation of longevity in Australian dairy cows. Aims The aim of this study was to investigate the main causes of culling in Australian dairy herds and thereby to assess the trend of reason-specific culling over time. Methods Culling reasons in Australian dairy cattle were studied based on culling records from 1995 through 2016. A total of 2452124 individual cow culling observations were obtained from Datagene, Australia, of which 2140337 were Holstein and 311787 were from Jersey cows. A binary logistic regression model was used to estimate effects of breed and age and the trend of a particular culling reason over time. Key results The most important culling reasons identified over the 21-year period were infertility (17.0%), mastitis (12.9%), low production (9.3%), sold for dairy purpose (6.4%) and old age (6.2%), whereas 37.4% were ‘other reasons not reported’. The average age at culling was nearly the same for Holstein (6.75 years) and Jersey (6.73 years) cows. The estimated age at culling was slightly increased for Holstein cows (by 3.7 days) and somewhat decreased for Jersey cows (by 11 days) over the last two decades. The probability of culling cows for infertility and low production was high in early parities and consistently declined as age advanced, and culling due to mastitis was higher in older cows. The trend of main culling reasons over time was evaluated, indicating that the probability of culling due to infertility has progressively increased over the years in both breeds, and culling for mastitis in Jersey cows has also increased. Culling of cows due to low production sharply decreased from 2.5 to –8% for Holstein and from 73 to 60% for Jersey cows over the 21-year period. Conclusions Culling age has changed only little in both breeds whereas culling reasons have changed over the last two decades, with low production becoming a less important reason for culling and infertility becoming more important for Holstein and Jersey breeds. Implications Due to changes of culling reasons, there could be a change in the meaning of survival over time as well. As a result, genetic correlation with survival and other traits might be changed and accuracy and bias of genetic evaluations could be affected.