Literature DB >> 24128704

Genomic selection for feed efficiency in dairy cattle.

J E Pryce1, W J Wales2, Y de Haas3, R F Veerkamp3, B J Hayes1.   

Abstract

Feed is a major component of variable costs associated with dairy systems and is therefore an important consideration for breeding objectives. As a result, measures of feed efficiency are becoming popular traits for genetic analyses. Already, several countries account for feed efficiency in their breeding objectives by approximating the amount of energy required for milk production, maintenance, etc. However, variation in actual feed intake is currently not captured in dairy selection objectives, although this could be possible by evaluating traits such as residual feed intake (RFI), defined as the difference between actual and predicted feed (or energy) intake. As feed intake is expensive to accurately measure on large numbers of cows, phenotypes derived from it are obvious candidates for genomic selection provided that: (1) the trait is heritable; (2) the reliability of genomic predictions are acceptable to those using the breeding values; and (3) if breeding values are estimated for heifers, rather than cows then the heifer and cow traits need to be correlated. The accuracy of genomic prediction of dry matter intake (DMI) and RFI has been estimated to be around 0.4 in beef and dairy cattle studies. There are opportunities to increase the accuracy of prediction, for example, pooling data from three research herds (in Australia and Europe) has been shown to increase the accuracy of genomic prediction of DMI from 0.33 within country to 0.35 using a three-country reference population. Before including RFI as a selection objective, genetic correlations with other traits need to be estimated. Weak unfavourable genetic correlations between RFI and fertility have been published. This could be because RFI is mathematically similar to the calculation of energy balance and failure to account for mobilisation of body reserves correctly may result in selection for a trait that is similar to selecting for reduced (or negative) energy balance. So, if RFI is to become a selection objective, then including it in an overall multi-trait selection index where the breeding objective is net profit is sensible, as this would allow genetic correlations with other traits to be properly accounted for. If genetic parameters are accurately estimated then RFI is a logical breeding objective. If there is uncertainty in these, then DMI may be preferable.

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Year:  2013        PMID: 24128704     DOI: 10.1017/S1751731113001687

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


  8 in total

1.  Impact of QTL properties on the accuracy of multi-breed genomic prediction.

Authors:  Yvonne C J Wientjes; Mario P L Calus; Michael E Goddard; Ben J Hayes
Journal:  Genet Sel Evol       Date:  2015-05-08       Impact factor: 4.297

Review 2.  Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits.

Authors:  C Egger-Danner; J B Cole; J E Pryce; N Gengler; B Heringstad; A Bradley; K F Stock
Journal:  Animal       Date:  2014-11-12       Impact factor: 3.240

3.  Genome-Wide Association Study for Carcass Traits in an Experimental Nelore Cattle Population.

Authors:  Rafael Medeiros de Oliveira Silva; Nedenia Bonvino Stafuzza; Breno de Oliveira Fragomeni; Gregório Miguel Ferreira de Camargo; Thaís Matos Ceacero; Joslaine Noely Dos Santos Gonçalves Cyrillo; Fernando Baldi; Arione Augusti Boligon; Maria Eugênia Zerlotti Mercadante; Daniela Lino Lourenco; Ignacy Misztal; Lucia Galvão de Albuquerque
Journal:  PLoS One       Date:  2017-01-24       Impact factor: 3.240

4.  Effects of Incorporating Dry Matter Intake and Residual Feed Intake into a Selection Index for Dairy Cattle Using Deterministic Modeling.

Authors:  Kerry Houlahan; Flavio S Schenkel; Dagnachew Hailemariam; Jan Lassen; Morten Kargo; John B Cole; Erin E Connor; Silvia Wegmann; Oliveira Junior; Filippo Miglior; Allison Fleming; Tatiane C S Chud; Christine F Baes
Journal:  Animals (Basel)       Date:  2021-04-17       Impact factor: 2.752

Review 5.  Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle.

Authors:  Cori J Siberski-Cooper; James E Koltes
Journal:  Animals (Basel)       Date:  2021-12-22       Impact factor: 2.752

6.  Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency.

Authors:  Sunduimijid Bolormaa; Iona M MacLeod; Majid Khansefid; Leah C Marett; William J Wales; Filippo Miglior; Christine F Baes; Flavio S Schenkel; Erin E Connor; Coralia I V Manzanilla-Pech; Paul Stothard; Emily Herman; Gert J Nieuwhof; Michael E Goddard; Jennie E Pryce
Journal:  Genet Sel Evol       Date:  2022-09-06       Impact factor: 5.100

7.  Assessing accuracy of imputation using different SNP panel densities in a multi-breed sheep population.

Authors:  Ricardo V Ventura; Stephen P Miller; Ken G Dodds; Benoit Auvray; Michael Lee; Matthew Bixley; Shannon M Clarke; John C McEwan
Journal:  Genet Sel Evol       Date:  2016-09-23       Impact factor: 4.297

Review 8.  Integration of Multiplied Omics, a Step Forward in Systematic Dairy Research.

Authors:  Yingkun Zhu; Dengpan Bu; Lu Ma
Journal:  Metabolites       Date:  2022-03-04
  8 in total

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