Literature DB >> 15536483

A quantitative genetics model for viability selection.

L Luo1, Y-M Zhang, S Xu.   

Abstract

Viability selection will change gene frequencies of loci controlling fitness. Consequently, the frequencies of marker loci linked to the viability loci will also change. In genetic mapping, the change of marker allelic frequencies is reflected by the departure from Mendelian segregation ratio. The non-Mendelian segregation of markers has been used to map viability loci along the genome. However, current methods have not been able to detect the amount of selection (s) and the degree of dominance (h) simultaneously. We developed a method to detect both s and h using an F2 mating design under the classical fitness model. We also developed a quantitative genetics model for viability selection by proposing a continuous liability controlling the viability of individuals. With the liability model, mapping viability loci has been formulated as mapping quantitative trait loci. As a result, nongenetic systematic environmental effects can be easily incorporated into the model and subsequently separated from the genetic effects of the viability loci. The quantitative genetic model has been verified with a series of Monte Carlo simulation experiments.

Mesh:

Year:  2005        PMID: 15536483     DOI: 10.1038/sj.hdy.6800615

Source DB:  PubMed          Journal:  Heredity (Edinb)        ISSN: 0018-067X            Impact factor:   3.821


  28 in total

1.  A Mixed Model Approach to Genome-Wide Association Studies for Selection Signatures, with Application to Mice Bred for Voluntary Exercise Behavior.

Authors:  Shizhong Xu; Theodore Garland
Journal:  Genetics       Date:  2017-08-03       Impact factor: 4.562

2.  Modeling segregation distortion for viability selection. I. Reconstruction of linkage maps with distorted markers.

Authors:  Chengsong Zhu; Chunming Wang; Yuan-Ming Zhang
Journal:  Theor Appl Genet       Date:  2006-11-22       Impact factor: 5.699

3.  Mapping quantitative trait loci from a single-tail sample of the phenotype distribution including survival data.

Authors:  Mikko J Sillanpää; Fabian Hoti
Journal:  Genetics       Date:  2007-12       Impact factor: 4.562

Review 4.  Methodologies for segregation analysis and QTL mapping in plants.

Authors:  Yuan-Ming Zhang; Junyi Gai
Journal:  Genetica       Date:  2008-08-23       Impact factor: 1.082

5.  Patterns of quantitative genetic variation in multiple dimensions.

Authors:  Mark Kirkpatrick
Journal:  Genetica       Date:  2008-08-10       Impact factor: 1.082

6.  Mapping quantitative trait loci for binary trait in the F2:3 design.

Authors:  Chengsong Zhu; Yuan-Ming Zhang; Zhigang Guo
Journal:  J Genet       Date:  2008-12       Impact factor: 1.166

7.  Quantitative trait locus mapping can benefit from segregation distortion.

Authors:  Shizhong Xu
Journal:  Genetics       Date:  2008-10-28       Impact factor: 4.562

8.  Method for the mapping of a female partial-sterile locus on a molecular marker linkage map.

Authors:  Jianguo Chen; Bruce Walsh
Journal:  Theor Appl Genet       Date:  2009-07-21       Impact factor: 5.699

9.  Prevalence of segregation distortion in diploid alfalfa and its implications for genetics and breeding applications.

Authors:  Xuehui Li; Xiaojuan Wang; Yanling Wei; E Charles Brummer
Journal:  Theor Appl Genet       Date:  2011-05-31       Impact factor: 5.699

10.  Construction of the First High-Density Genetic Linkage Map and Analysis of Quantitative Trait Loci for Growth-Related Traits in Sinonovacula constricta.

Authors:  Donghong Niu; Yunchao Du; Ze Wang; Shumei Xie; Haideng Nguyen; Zhiguo Dong; Heding Shen; Jiale Li
Journal:  Mar Biotechnol (NY)       Date:  2017-07-19       Impact factor: 3.619

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