Genome‐enable prediction for health traits using high‐density SNP panel in US Holstein cattle

Abstract

Summary The objective of this study was to compare accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle. The dataset was composed of 2505 records reporting the occurrence of retained fetal membranes (RFM), metritis (MET), mastitis (MAST), displaced abomasum (DA), lameness (LS), clinical endometritis (CE), respiratory disease (RD), dystocia (DYST) and subclinical ketosis (SCK) in Holstein cows, collected between 2012 and 2014 in 16 dairies located across the US. Cows were genotyped with the Illumina BovineHD (HD, 777K). The quality controls for SNP genotypes were HWE P‐value of at least 1 × 10−10; MAF greater than 0.01 and call rate greater than 0.95. The FImpute program was used for imputation of missing SNP markers. The effect of each SNP was estimated using the Bayesian Ridge Regression (BRR), Bayes A, Bayes B and Bayes Cπ methods. The prediction quality was assessed by the area under the curve, the prediction mean square error and the correlation between genomic breeding value and the observed phenotype, using a leave‐one‐out cross‐validation technique that avoids iterative cross‐validation. The highest accuracies of predictions achieved were: RFM [Bayes B (0.34)], MET [BRR (0.36)], MAST [Bayes B (0.55), DA [Bayes Cπ (0.26)], LS [Bayes A (0.12)], CE [Bayes A (0.32)], RD [Bayes Cπ (0.23)], DYST [Bayes A (0.35)] and SCK [Bayes Cπ (0.38)] models. Except for DA, LS and RD, the predictive abilities were similar between the methods. A strong relationship between the predictive ability and the heritability of the trait was observed, where traits with higher heritability achieved higher accuracy and lower bias when compared with those with low heritability. Overall, it has been shown that a high‐density SNP panel can be used successfully to predict genomic breeding values of health traits in Holstein cattle and that the model of choice will depend mostly on the genetic architecture of the trait.

Early-lactation diseases and fertility in 2 seasons of calving across US dairy herds

“…This prospective study is part of a research effort investigating potential associations between genomic variation and fertility of Holstein cows (Lopes et al, 2020;Pinedo et al, 2020). A convenient sample of 16 herds were enrolled based on existing contacts provided by the researchers, availability of good-quality records, daily recording of milk yield or monthly milk recording service provided by DHI, and geographic distribution across the United States.…”

Recent advances and future directions for uterine diseases diagnosis, pathogenesis, and management in dairy cows

“…Authors suggested that the health traits of young calves and heifers can likely be incorporated effectively to predict differences in future health performance and can be a compelling opportunity to help reduce disease incidence and improve profitability in dairy operations (McNeel et al, 2017). A second study compared the accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle (Lopes et al, 2020). Genetic heritability for metritis (0.05) and clinical endometritis (0.04) were low.…”

“…The author concluded that high-density SNP panels could be successfully used to predict genomic breeding values of health traits in Holstein cattle. Still, the model choice will most likely depend on the genetic architecture of the attribute of interesting (Lopes et al, 2020).…”

“…Indeed, in the last decade, over a dozen of non-antimicrobial therapeutics to prevent and treat metritis and endometritis has been investigated with encouraging results ( Ahmadi et al., 2019 ; Ametaj et al., 2014 ; ATLAS Collaboration, 2017 ; Brick et al., 2012 ; Daetz et al., 2016 ; Escandon et al., 2020 ; Genis et al., 2018 ; Machado et al., 2012 ; Pinedo et al., 2015 ). Genome-enable prediction for health traits ( McNeel et al., 2017 ; Lopes et al., 2020 ), activity monitors ( Barragan et al., 2018 ; Stangaferro et al., 2016 ), biomarkers ( Barragan et al., 2018 , 2019 ; Dervishi et al., 2018 ; Wisnieski et al., 2019 ; Zhang et al., 2017 ), immune cells profile ( Pomeroy et al., 2017 ), machine learning predictive models ( Bogado Pascottini et al., 2020 ) are others innovative tools that have been explored in the recent years to help mitigate negative impacts of uterine diseases. The objective of this manuscript is to summarize recent advances and future directions for uterine disease diagnosis, pathogenesis, and management.…”

Serum Vitamin D Is Associated with Antioxidant Potential in Peri-Parturient Cows

“…Despite the body of literature on enhancing immune function and improving redox balance with supplemental vitamin E and selenium, dairy cows still experience oxidative stress in the periparturient period which further increases the risk for disease [ 17 ]. Indeed, approximately 45–75% of disease incidence occur during the first 30 d in milk when cows are at highest risk for oxidative stress [ 18 , 19 ]. Thus, alternative methods for reducing oxidative stress and disease risk in the periparturient period are needed.…”