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Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/55196
Title: Genomic Scan as a Tool for Assessing the Genetic Component of Phenotypic Variance in Wild Populations
Authors: Herrera, Carlos M.
Keywords: Amplified fragment length polymorphism
Genetic determination
Genomic scan
Multiple linear regression
Phenotypic variance components
Issue Date: 2012
Publisher: Springer
Citation: François Pompanon and Aurélie Bonin (eds.), Data Production and Analysis in Population Genomics: Methods and Protocols, Methods in Molecular Biology, vol. 888. pp: 315-329 (2012)
Abstract: Methods for estimating quantitative trait heritability in wild populations have been developed in recent years which take advantage of the increased availability of genetic markers to reconstruct pedigrees or estimate relatedness between individuals, but their application to real-world data is not exempt from difficulties. This chapter describes a recent marker-based technique which, by adopting a genomic scan approach and focusing on the relationship between phenotypes and genotypes at the individual level, avoids the problems inherent to marker-based estimators of relatedness. This method allows the quantification of the genetic component of phenotypic variance (“degree of genetic determination” or “heritability in the broad sense”) in wild populations and is applicable whenever phenotypic trait values and multilocus data for a large number of genetic markers (e.g., amplified fragment length polymorphisms, AFLPs) are simultaneously available for a sample of individuals from the same population. The method proceeds by first identifying those markers whose variation across individuals is significantly correlated with individual phenotypic differences (“adaptive loci”). The proportion of phenotypic variance in the sample that is statistically accounted for by individual differences in adaptive loci is then estimated by fitting a linear model to the data, with trait value as the dependent variable and scores of adaptive loci as indepen- dent ones. The method can be easily extended to accommodate quantitative or qualitative information on biologically relevant features of the environment experienced by each sampled individual, in which case estimates of the environmental and genotype × environment components of phenotypic variance can also be obtained
Publisher version (URL): http://dx.doi.org/10.1007/978-1-61779-870-2_18
URI: http://hdl.handle.net/10261/55196
DOI: 10.1007/978-1-61779-870-2_18
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