Literature DB >> 21481291

The contribution of dominance to the understanding of quantitative genetic variation.

Robin Wellmann1, Jörn Bennewitz1.   

Abstract

Knowledge of the genetic architecture of a quantitative trait is useful to adjust methods for the prediction of genomic breeding values and to discover the extent to which common assumptions in quantitative trait locus (QTL) mapping experiments and breeding value estimation are violated. It also affects our ability to predict the long-term response of selection. In this paper, we focus on additive and dominance effects of QTL. We derive formulae that can be used to estimate the number of QTLs that affect a quantitative trait and parameters of the distribution of their additive and dominance effects from variance components, inbreeding depression and results from QTL mapping experiments. It is shown that a lower bound for the number of QTLs depends on the ratio of squared inbreeding depression to dominance variance. That is, high inbreeding depression must be due to a sufficient number of QTLs because otherwise the dominance variance would exceed the true value. Moreover, the second moment of the dominance coefficient depends only on the ratio of dominance variance to additive variance and on the dependency between additive effects and dominance coefficients. This has implications on the relative frequency of overdominant alleles. It is also demonstrated how the expected number of large QTLs determines the shape of the distribution of additive effects. The formulae are applied to milk yield and productive life in Holstein cattle. Possible sources for a potential bias of the results are discussed.

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Year:  2011        PMID: 21481291     DOI: 10.1017/S0016672310000649

Source DB:  PubMed          Journal:  Genet Res (Camb)        ISSN: 0016-6723            Impact factor:   1.588


  19 in total

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Journal:  J Genet       Date:  2019-03       Impact factor: 1.166

3.  Genomic Model with Correlation Between Additive and Dominance Effects.

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Journal:  Genetics       Date:  2018-05-09       Impact factor: 4.562

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Authors:  Hadi Esfandyari; Anders C Sørensen; Piter Bijma
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5.  A crossbred reference population can improve the response to genomic selection for crossbred performance.

Authors:  Hadi Esfandyari; Anders Christian Sørensen; Piter Bijma
Journal:  Genet Sel Evol       Date:  2015-09-29       Impact factor: 4.297

6.  Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.

Authors:  Anita Ehret; David Hochstuhl; Daniel Gianola; Georg Thaller
Journal:  Genet Sel Evol       Date:  2015-03-31       Impact factor: 4.297

7.  Application of a Bayesian dominance model improves power in quantitative trait genome-wide association analysis.

Authors:  Jörn Bennewitz; Christian Edel; Ruedi Fries; Theo H E Meuwissen; Robin Wellmann
Journal:  Genet Sel Evol       Date:  2017-01-14       Impact factor: 4.297

8.  Using genome-wide association analysis to characterize environmental sensitivity of milk traits in dairy cattle.

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Journal:  G3 (Bethesda)       Date:  2013-07-08       Impact factor: 3.154

9.  Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle.

Authors:  Johann Ertl; Andrés Legarra; Zulma G Vitezica; Luis Varona; Christian Edel; Reiner Emmerling; Kay-Uwe Götz
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10.  Genomic selection models for directional dominance: an example for litter size in pigs.

Authors:  Luis Varona; Andrés Legarra; William Herring; Zulma G Vitezica
Journal:  Genet Sel Evol       Date:  2018-01-26       Impact factor: 4.297

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