Literature DB >> 22436268

New phenotypes for new breeding goals in dairy cattle.

D Boichard1, M Brochard.   

Abstract

Cattle production faces new challenges regarding sustainability with its three pillars - economic, societal and environmental. The following three main factors will drive dairy cattle selection in the future: (1) During a long period, intensive selection for enhanced productivity has deteriorated most functional traits, some reaching a critical point and needing to be restored. This is especially the case for the Holstein breed and for female fertility, mastitis resistance, longevity and metabolic diseases. (2) Genomic selection offers two new opportunities: as the potential genetic gain can be almost doubled, more traits can be efficiently selected; phenotype recording can be decoupled from selection and limited to several thousand animals. (3) Additional information from other traits can be used, either from existing traditional recording systems at the farm level or from the recent and rapid development of new technologies and precision farming. Milk composition (i.e. mainly fatty acids) should be adapted to better meet human nutritional requirements. Fatty acids can be measured through a new interpretation of the usual medium infrared spectra. Milk composition can also provide additional information about reproduction and health. Modern milk recorders also provide new information, that is, on milking speed or on the shape of milking curves. Electronic devices measuring physiological or activity parameters can predict physiological status like estrus or diseases, and can record behavioral traits. Slaughterhouse data may permit effective selection on carcass traits. Efficient observatories should be set up for early detection of new emerging genetic defects. In the near future, social acceptance of cattle production could depend on its capacity to decrease its ecological footprint. The first solution consists in increasing survival and longevity to reduce replacement needs and the number of nonproductive animals. At the individual level, selection on rumen activity may lead to decreased methane production and concomitantly to improved feed efficiency. A major effort should be dedicated to this new field of research and particularly to rumen flora metagenomics. Low input in cattle production is very important and tomorrow's cow will need to adapt to a less intensive production environment, particularly lower feed quality and limited care. Finally, global climate change will increase pathogen pressure, thus more accurate predictors for disease resistance will be required.

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Year:  2012        PMID: 22436268     DOI: 10.1017/S1751731112000018

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  13 in total

1.  Genome-wide genotyping uncovers genetic profiles and history of the Russian cattle breeds.

Authors:  Andrey Yurchenko; Nikolay Yudin; Ruslan Aitnazarov; Alexandra Plyusnina; Vladimir Brukhin; Vladimir Soloshenko; Bulat Lhasaranov; Ruslan Popov; Ivan A Paronyan; Kirill V Plemyashov; Denis M Larkin
Journal:  Heredity (Edinb)       Date:  2017-12-08       Impact factor: 3.821

2.  Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle.

Authors:  Hans D Daetwyler; Aurélien Capitan; Hubert Pausch; Paul Stothard; Rianne van Binsbergen; Rasmus F Brøndum; Xiaoping Liao; Anis Djari; Sabrina C Rodriguez; Cécile Grohs; Diane Esquerré; Olivier Bouchez; Marie-Noëlle Rossignol; Christophe Klopp; Dominique Rocha; Sébastien Fritz; André Eggen; Phil J Bowman; David Coote; Amanda J Chamberlain; Charlotte Anderson; Curt P VanTassell; Ina Hulsegge; Mike E Goddard; Bernt Guldbrandtsen; Mogens S Lund; Roel F Veerkamp; Didier A Boichard; Ruedi Fries; Ben J Hayes
Journal:  Nat Genet       Date:  2014-07-13       Impact factor: 38.330

3.  Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.).

Authors:  R Rincent; D Laloë; S Nicolas; T Altmann; D Brunel; P Revilla; V M Rodríguez; J Moreno-Gonzalez; A Melchinger; E Bauer; C-C Schoen; N Meyer; C Giauffret; C Bauland; P Jamin; J Laborde; H Monod; P Flament; A Charcosset; L Moreau
Journal:  Genetics       Date:  2012-08-03       Impact factor: 4.562

4.  Incidence rates of clinical mastitis among Canadian Holsteins classified as high, average, or low immune responders.

Authors:  Kathleen A Thompson-Crispi; Filippo Miglior; Bonnie A Mallard
Journal:  Clin Vaccine Immunol       Date:  2012-11-21

Review 5.  Go with the flow-biology and genetics of the lactation cycle.

Authors:  Eva M Strucken; Yan C S M Laurenson; Gudrun A Brockmann
Journal:  Front Genet       Date:  2015-03-26       Impact factor: 4.599

Review 6.  Bovine mastitis: frontiers in immunogenetics.

Authors:  Kathleen Thompson-Crispi; Heba Atalla; Filippo Miglior; Bonnie A Mallard
Journal:  Front Immunol       Date:  2014-10-07       Impact factor: 7.561

7.  Genome-wide comparative analyses of correlated and uncorrelated phenotypes identify major pleiotropic variants in dairy cattle.

Authors:  Ruidong Xiang; Iona M MacLeod; Sunduimijid Bolormaa; Michael E Goddard
Journal:  Sci Rep       Date:  2017-08-23       Impact factor: 4.379

8.  Use of Large and Diverse Datasets for 1H NMR Serum Metabolic Profiling of Early Lactation Dairy Cows.

Authors:  Timothy D W Luke; Jennie E Pryce; Aaron C Elkins; William J Wales; Simone J Rochfort
Journal:  Metabolites       Date:  2020-04-30

9.  A genome-wide association study of immune response traits in Canadian Holstein cattle.

Authors:  Kathleen A Thompson-Crispi; Mehdi Sargolzaei; Ricardo Ventura; Mohammed Abo-Ismail; Filippo Miglior; Flavio Schenkel; Bonnie A Mallard
Journal:  BMC Genomics       Date:  2014-07-04       Impact factor: 3.969

Review 10.  Integrating Genomic Data Sets for Knowledge Discovery: An Informed Approach to Management of Captive Endangered Species.

Authors:  Kristopher J L Irizarry; Doug Bryant; Jordan Kalish; Curtis Eng; Peggy L Schmidt; Gini Barrett; Margaret C Barr
Journal:  Int J Genomics       Date:  2016-06-08       Impact factor: 2.326

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