Estimating Effects and Variance Components in Models of Quantitative Genetics in an Era Sequenced Genomes

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R0149

Estimating Effects and Variance Components in Models of Quantitative Genetics in an Era Sequenced Genomes

Charles J. Mode
Charles J. Mode Drexel University
DOI

Abstract

As in many other areas of research in genetics, the availability sequenced genomes in samples of individuals has revolutionized the study of quantitative traits, because researches have developed statistical evidence regarding the locations of genomic regions, loci, that have been implicated with the expression of a quantitative trait or traits. Therefore, in cases in which it is possible to develop operational definitions of at least two alleles at each locus, genomic regions, it becomes possible to identify the genotype of each individual with respect to a set of loci that have been shown in other experiments to influence the expression of a quantitative trait. As will be shown in this paper, by knowing the genotype of each individual in a sample with respect to a set of identified loci, it is now possible to directly estimate effects that are measures of not only intra-allelic interactions at each locus under consideration but also various types of epistatic effects that are measures of interactions among alleles at different loci, governing the inheritance of a quantitative trait. These straight forward methods of estimation differ from those used in classical quantitative genetics, because such effects and corresponding variance components could be estimated indirectly, using analysis of variance procedures or some version of general linear models that have been and are widely in statistical genetics. The direct methods of estimation described in this paper, show promise towards shifting the working paradigm that has been used in classical models of the genetics of quantitative traits involving the estimation of variance components to a simpler and more direct approach.

Estimating Effects and Variance Components in Models of Quantitative Genetics in an Era Sequenced Genomes

As in many other areas of research in genetics, the availability sequenced genomes in samples of individuals has revolutionized the study of quantitative traits, because researches have developed statistical evidence regarding the locations of genomic regions, loci, that have been implicated with the expression of a quantitative trait or traits. Therefore, in cases in which it is possible to develop operational definitions of at least two alleles at each locus, genomic regions, it becomes possible to identify the genotype of each individual with respect to a set of loci that have been shown in other experiments to influence the expression of a quantitative trait. As will be shown in this paper, by knowing the genotype of each individual in a sample with respect to a set of identified loci, it is now possible to directly estimate effects that are measures of not only intra-allelic interactions at each locus under consideration but also various types of epistatic effects that are measures of interactions among alleles at different loci, governing the inheritance of a quantitative trait. These straight forward methods of estimation differ from those used in classical quantitative genetics, because such effects and corresponding variance components could be estimated indirectly, using analysis of variance procedures or some version of general linear models that have been and are widely in statistical genetics. The direct methods of estimation described in this paper, show promise towards shifting the working paradigm that has been used in classical models of the genetics of quantitative traits involving the estimation of variance components to a simpler and more direct approach.

Charles J. Mode
Charles J. Mode Drexel University

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Charles J. Mode. 2014. “. Global Journal of Science Frontier Research – F: Mathematics & Decision GJSFR-F Volume 14 (GJSFR Volume 14 Issue F5): .

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Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

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Estimating Effects and Variance Components in Models of Quantitative Genetics in an Era Sequenced Genomes

Charles J. Mode
Charles J. Mode Drexel University

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