Literature DB >> 27372587

Invited review: Opportunities for genetic improvement of metabolic diseases.

J E Pryce1, K L Parker Gaddis2, A Koeck3, C Bastin4, M Abdelsayed5, N Gengler4, F Miglior6, B Heringstad7, C Egger-Danner8, K F Stock9, A J Bradley10, J B Cole11.   

Abstract

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.
Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  displaced abomasum; ketosis; metabolic disease; milk fever

Mesh:

Year:  2016        PMID: 27372587     DOI: 10.3168/jds.2016-10854

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  6 in total

1.  Genome-wide association study identifies QTLs for displacement of abomasum in Chinese Holstein cattle1.

Authors:  Hetian Huang; Jie Cao; Gang Guo; Xizhi Li; Yachun Wang; Ying Yu; Shengli Zhang; Qin Zhang; Yi Zhang
Journal:  J Anim Sci       Date:  2019-03-01       Impact factor: 3.159

2.  Estimation of Genetic Parameters for Female Fertility Traits in the Polish Holstein-Friesian Population.

Authors:  Agnieszka Otwinowska-Mindur; Ewa Ptak; Wojciech Jagusiak; Andrzej Zarnecki
Journal:  Animals (Basel)       Date:  2022-06-08       Impact factor: 3.231

Review 3.  β-Defensins: Farming the Microbiome for Homeostasis and Health.

Authors:  Kieran G Meade; Cliona O'Farrelly
Journal:  Front Immunol       Date:  2019-01-25       Impact factor: 7.561

4.  Genome-wide association analysis for β-hydroxybutyrate concentration in Milk in Holstein dairy cattle.

Authors:  S Nayeri; F Schenkel; A Fleming; V Kroezen; M Sargolzaei; C Baes; A Cánovas; J Squires; F Miglior
Journal:  BMC Genet       Date:  2019-07-16       Impact factor: 2.797

5.  Gene Mapping and Gene-Set Analysis for Milk Fever Incidence in Holstein Dairy Cattle.

Authors:  Hendyel A Pacheco; Simone da Silva; Anil Sigdel; Chun Kuen Mak; Klibs N Galvão; Rodrigo A Texeira; Laila T Dias; Francisco Peñagaricano
Journal:  Front Genet       Date:  2018-10-10       Impact factor: 4.599

Review 6.  General Health Benefits and Pharmacological Activities of Triticum aestivum L.

Authors:  Said Moshawih; Rabi'atul Nur Amalia Abdullah Juperi; Ganesh Sritheran Paneerselvam; Long Chiau Ming; Kai Bin Liew; Bey Hing Goh; Yaser Mohammed Al-Worafi; Chee-Yan Choo; Shobna Thuraisingam; Hui Poh Goh; Nurolaini Kifli
Journal:  Molecules       Date:  2022-03-17       Impact factor: 4.411

  6 in total

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