Literature DB >> 22444079

Genotype by environment interaction for litter size in pigs as quantified by reaction norms analysis.

P W Knap1, G Su.   

Abstract

A Bayesian procedure was used to estimate linear reaction norms (i.e. individual G × E plots) on 297 518 litter size records of 121 104 sows, daughters of 2040 sires, recorded on 144 farms in North and Latin America, Europe, Asia and Australia. The method allowed for simultaneous estimation of all parameters involved. The analysis was carried out on three subsets, comprising (i) parity 1 records of 33 641 sows of line B, (ii) all parity records of 52 120 sows of line B and (iii) all parity records of 121 104 sows of lines A, B and A × B. Estimated heritabilities ranged from 0.09 to 0.10 (smallest to largest subset) for the intercept of the reaction norms, and were 0.15, 0.08 and 0.02 (ditto) for the slope. Estimated genetic correlations between intercept and slope were -0.09, +0.26 and +0.69 (ditto). The three subsets therefore showed a progressively lower genetic component to environmental sensitivity, and progressively less re-ranking of genotypes across the environmental (herd-year-season) range. In a genetic evaluation that does not include reaction norms in the statistical model, part of the G × E effect remains confounded with the additive genetic effect, which may lead to errors in the estimates of the additive genetic effect; the reaction norms model removes this confounding. The intercept estimates from the largest data subset show correlations with litter size estimated breeding values (EBV) from routine genetic evaluation (without reaction norms included) of 0.78 to 0.85 for sows with one to seven litter records, and 0.75 for sires. Hence, including reaction norms in genetic evaluation would increase the reliability of the EBV of young selection candidates without own performance or progeny data by considerably more than 100 × (1/0.75-1) = 33%. Reaction norm slope estimates turn out to be very demanding statistics; environmental sensitivity must therefore be classified as a 'hard-to-measure' trait.

Entities:  

Year:  2008        PMID: 22444079     DOI: 10.1017/S1751731108003145

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


  20 in total

1.  Gene networks for total number born in pigs across divergent environments.

Authors:  Lucas L Verardo; Marcos S Lopes; Pramod Mathur; Ole Madsen; Fabyano F Silva; Martien A M Groenen; Egbert F Knol; Paulo S Lopes; Simone E F Guimarães
Journal:  Mamm Genome       Date:  2017-06-02       Impact factor: 2.957

2.  Genotype by environment interaction for stayability of Red Angus in the United States.

Authors:  Dennis J Fennewald; Robert L Weaber; William R Lamberson
Journal:  J Anim Sci       Date:  2018-03-06       Impact factor: 3.159

3.  Genotype by environment interaction for tick resistance of Hereford and Braford beef cattle using reaction norm models.

Authors:  Rodrigo R Mota; Robert J Tempelman; Paulo S Lopes; Ignacio Aguilar; Fabyano F Silva; Fernando F Cardoso
Journal:  Genet Sel Evol       Date:  2016-01-14       Impact factor: 4.297

4.  1HNMR-Based metabolomic profiling method to develop plasma biomarkers for sensitivity to chronic heat stress in growing pigs.

Authors:  Samir Dou; Nathalie Villa-Vialaneix; Laurence Liaubet; Yvon Billon; Mario Giorgi; Hélène Gilbert; Jean-Luc Gourdine; Juliette Riquet; David Renaudeau
Journal:  PLoS One       Date:  2017-11-27       Impact factor: 3.240

5.  Effects of Diet and Genetics on Growth Performance of Pigs in Response to Repeated Exposure to Heat Stress.

Authors:  Wendy M Rauw; E Johana Mayorga; Soi Meng Lei; Jack C M Dekkers; John F Patience; Nicholas K Gabler; Steven M Lonergan; Lance H Baumgard
Journal:  Front Genet       Date:  2017-10-26       Impact factor: 4.599

6.  Genomic selection for crossbred performance accounting for breed-specific effects.

Authors:  Marcos S Lopes; Henk Bovenhuis; André M Hidalgo; Johan A M van Arendonk; Egbert F Knol; John W M Bastiaansen
Journal:  Genet Sel Evol       Date:  2017-06-26       Impact factor: 4.297

7.  Use of multi-trait and random regression models to identify genetic variation in tolerance to porcine reproductive and respiratory syndrome virus.

Authors:  Graham Lough; Hamed Rashidi; Ilias Kyriazakis; Jack C M Dekkers; Andrew Hess; Melanie Hess; Nader Deeb; Antti Kause; Joan K Lunney; Raymond R R Rowland; Han A Mulder; Andrea Doeschl-Wilson
Journal:  Genet Sel Evol       Date:  2017-04-19       Impact factor: 4.297

8.  Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms.

Authors:  Shi-Yi Chen; Pedro H F Freitas; Hinayah R Oliveira; Sirlene F Lázaro; Yi Jian Huang; Jeremy T Howard; Youping Gu; Allan P Schinckel; Luiz F Brito
Journal:  Genet Sel Evol       Date:  2021-06-17       Impact factor: 4.297

9.  Selection of pigs for improved coping with health and environmental challenges: breeding for resistance or tolerance?

Authors:  Sarita Z Y Guy; Peter C Thomson; Susanne Hermesch
Journal:  Front Genet       Date:  2012-12-14       Impact factor: 4.599

Review 10.  Genotype by environment interaction and breeding for robustness in livestock.

Authors:  Wendy M Rauw; Luis Gomez-Raya
Journal:  Front Genet       Date:  2015-10-20       Impact factor: 4.599

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