Literature DB >> 25823842

Expected influence of linkage disequilibrium on genetic variance caused by dominance and epistasis on quantitative traits.

W G Hill1, A Mäki-Tanila.   

Abstract

Linkage disequilibrium (LD) influences the genetic variation in a quantitative trait contributed by two or more loci, with positive LD increasing the variance. The magnitude of LD also affects the relative magnitude of dominance and epistatic variation. We quantify the extent of the non-additive variance expected within populations, deriving analytical expressions for simple models and using numerical simulation in finite population more generally. As LD generates non-independence among loci, a simple partition into additive, dominance and epistatic components is not possible, so we merely distinguish between additive and non-additive components based on comparing covariances among close relatives, such as full sibs, half sibs and offspring-parent. As tight linkage is needed to yield substantial LD in outbred populations, we ignore recombination in the generation used to estimate components and it is analogous to a multi-allelic model. The expected magnitude of the non-additive variance is generally increased but not greatly so by the LD in outbred populations. Thus, as found in previous studies for unlinked loci, independent of the type and strength of gene interaction, the epistatic variance contributes little to the total.
© 2015 Blackwell Verlag GmbH.

Keywords:  Covariance of relatives; genetic interaction; genotypic variance; linkage

Mesh:

Year:  2015        PMID: 25823842     DOI: 10.1111/jbg.12140

Source DB:  PubMed          Journal:  J Anim Breed Genet        ISSN: 0931-2668            Impact factor:   2.380


  13 in total

1.  Orthogonal Estimates of Variances for Additive, Dominance, and Epistatic Effects in Populations.

Authors:  Zulma G Vitezica; Andrés Legarra; Miguel A Toro; Luis Varona
Journal:  Genetics       Date:  2017-05-18       Impact factor: 4.562

2.  Weak Epistasis May Drive Adaptation in Recombining Bacteria.

Authors:  Brian J Arnold; Michael U Gutmann; Yonatan H Grad; Samuel K Sheppard; Jukka Corander; Marc Lipsitch; William P Hanage
Journal:  Genetics       Date:  2018-01-12       Impact factor: 4.562

3.  Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods.

Authors:  Anderson Antonio Carvalho Alves; Rebeka Magalhães da Costa; Tiago Bresolin; Gerardo Alves Fernandes Júnior; Rafael Espigolan; André Mauric Frossard Ribeiro; Roberto Carvalheiro; Lucia Galvão de Albuquerque
Journal:  J Anim Sci       Date:  2020-06-01       Impact factor: 3.159

4.  Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts.

Authors:  Morgane Roth; Aurélien Beugnot; Tristan Mary-Huard; Laurence Moreau; Alain Charcosset; Julie B Fiévet
Journal:  Genetics       Date:  2022-04-04       Impact factor: 4.402

5.  Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals.

Authors:  Valentin Hivert; Julia Sidorenko; Florian Rohart; Michael E Goddard; Jian Yang; Naomi R Wray; Loic Yengo; Peter M Visscher
Journal:  Am J Hum Genet       Date:  2021-04-02       Impact factor: 11.025

6.  Genomic prediction of hybrid crops allows disentangling dominance and epistasis.

Authors:  David González-Diéguez; Andrés Legarra; Alain Charcosset; Laurence Moreau; Christina Lehermeier; Simon Teyssèdre; Zulma G Vitezica
Journal:  Genetics       Date:  2021-05-17       Impact factor: 4.562

7.  Genome-wide scan for commons SNPs affecting bovine leukemia virus infection level in dairy cattle.

Authors:  Hugo A Carignano; Dana L Roldan; María J Beribe; María A Raschia; Ariel Amadio; Juan P Nani; Gerónimo Gutierrez; Irene Alvarez; Karina Trono; Mario A Poli; Marcos M Miretti
Journal:  BMC Genomics       Date:  2018-02-13       Impact factor: 3.969

8.  Extensions of BLUP Models for Genomic Prediction in Heterogeneous Populations: Application in a Diverse Switchgrass Sample.

Authors:  Guillaume P Ramstein; Michael D Casler
Journal:  G3 (Bethesda)       Date:  2019-03-07       Impact factor: 3.154

9.  Genomic variation predicts adaptive evolutionary responses better than population bottleneck history.

Authors:  Michael Ørsted; Ary Anthony Hoffmann; Elsa Sverrisdóttir; Kåre Lehmann Nielsen; Torsten Nygaard Kristensen
Journal:  PLoS Genet       Date:  2019-06-12       Impact factor: 5.917

Review 10.  Non-additive Effects in Genomic Selection.

Authors:  Luis Varona; Andres Legarra; Miguel A Toro; Zulma G Vitezica
Journal:  Front Genet       Date:  2018-03-06       Impact factor: 4.599

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