Literature DB >> 33541267

Predicting the purebred-crossbred genetic correlation from the genetic variance components in the parental lines.

Pascal Duenk1, Piter Bijma2, Yvonne C J Wientjes2, Mario P L Calus2.   

Abstract

BACKGROUND: The genetic correlation between purebred and crossbred performance ([Formula: see text]) is an important parameter in pig and poultry breeding, because response to selection in crossbred performance depends on the value of [Formula: see text] when selection is based on purebred (PB) performance. The value of [Formula: see text] can be substantially lower than 1, which is partly due to differences in allele frequencies between parental lines when non-additive genetic effects are present. This relationship between [Formula: see text] and parental allele frequencies suggests that [Formula: see text] can be expressed as a function of genetic parameters for the trait in the parental lines. In this study, we derived expressions for [Formula: see text] based on genetic variances within, and the genetic covariance between parental lines. It is important to note that the variance components used in our expressions are not the components that are typically estimated in empirical data. The expressions were derived for a genetic model with additive and dominance effects (D), and additive and epistatic additive-by-additive effects (EAA). We validated our expressions using simulations of purebred parental lines and their crosses, where the parental lines were either selected or not. Finally, using these simulations, we investigated the value of [Formula: see text] for genetic models with both dominance and epistasis or with other types of epistasis, for which expressions could not be derived.
RESULTS: Our simulations show that when non-additive effects are present, [Formula: see text] decreases with increasing differences in allele frequencies between the parental lines. Genetic models that involve dominance result in lower values of [Formula: see text] than genetic models that involve epistasis only. Using information of parental lines only, our expressions provide exact estimates of [Formula: see text] for models D and EAA, and accurate upper and lower bounds of [Formula: see text] for two other genetic models.
CONCLUSION: This work lays the foundation to enable estimation of [Formula: see text] from information collected in PB parental lines only.

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Mesh:

Year:  2021        PMID: 33541267      PMCID: PMC7860586          DOI: 10.1186/s12711-021-00601-w

Source DB:  PubMed          Journal:  Genet Sel Evol        ISSN: 0999-193X            Impact factor:   4.297


  28 in total

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2.  The distribution of QTL additive and dominance effects in porcine F2 crosses.

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Journal:  J Anim Breed Genet       Date:  2010-06       Impact factor: 2.380

3.  A unified model for functional and statistical epistasis and its application in quantitative trait Loci analysis.

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Journal:  Genetics       Date:  2007-04-03       Impact factor: 4.562

4.  QMSim: a large-scale genome simulator for livestock.

Authors:  Mehdi Sargolzaei; Flavio S Schenkel
Journal:  Bioinformatics       Date:  2009-01-28       Impact factor: 6.937

5.  Correlations between purebred and crossbred body weight traits in Limousin and Limousin-Angus populations.

Authors:  M Lukaszewicz; R Davis; J K Bertrand; I Misztal; S Tsuruta
Journal:  J Anim Sci       Date:  2015-04       Impact factor: 3.159

Review 6.  BOARD INVITED REVIEW: The purebred-crossbred correlation in pigs: A review of theory, estimates, and implications.

Authors:  Y C J Wientjes; M P L Calus
Journal:  J Anim Sci       Date:  2017-08       Impact factor: 3.159

7.  A note on Fisher's 'average effect' and 'average excess'.

Authors:  D S Falconer
Journal:  Genet Res       Date:  1985-12       Impact factor: 1.588

8.  Directionality of epistasis in a murine intercross population.

Authors:  Mihaela Pavlicev; Arnaud Le Rouzic; James M Cheverud; Günter P Wagner; Thomas F Hansen
Journal:  Genetics       Date:  2010-06-01       Impact factor: 4.562

9.  Method to represent the distribution of QTL additive and dominance effects associated with quantitative traits in computer simulation.

Authors:  Xiaochun Sun; Rita H Mumm
Journal:  BMC Bioinformatics       Date:  2016-02-06       Impact factor: 3.169

10.  Joint genomic evaluation of French dairy cattle breeds using multiple-trait models.

Authors:  Sofiene Karoui; María Jesús Carabaño; Clara Díaz; Andrés Legarra
Journal:  Genet Sel Evol       Date:  2012-12-07       Impact factor: 4.297

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