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Uncovering the Genetic Architectures of Quantitative Traits

The aim of a genome-wide association study (GWAS) is to identify loci in the human genome affecting a phenotype of interest. This review summarizes some recent work on conceptual and methodological aspects of GWAS. The average effect of gene substitution at a given causal site in the genome is the k...

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Autores principales: Lee, James J., Vattikuti, Shashaank, Chow, Carson C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816193/
https://www.ncbi.nlm.nih.gov/pubmed/27076877
http://dx.doi.org/10.1016/j.csbj.2015.10.002
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author Lee, James J.
Vattikuti, Shashaank
Chow, Carson C.
author_facet Lee, James J.
Vattikuti, Shashaank
Chow, Carson C.
author_sort Lee, James J.
collection PubMed
description The aim of a genome-wide association study (GWAS) is to identify loci in the human genome affecting a phenotype of interest. This review summarizes some recent work on conceptual and methodological aspects of GWAS. The average effect of gene substitution at a given causal site in the genome is the key estimand in GWAS, and we argue for its fundamental importance. Implicit in the definition of average effect is a linear model relating genotype to phenotype. The fraction of the phenotypic variance ascribable to polymorphic sites with nonzero average effects in this linear model is called the heritability, and we describe methods for estimating this quantity from GWAS data. Finally, we show that the theory of compressed sensing can be used to provide a sharp estimate of the sample size required to identify essentially all sites contributing to the heritability of a given phenotype.
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spelling pubmed-48161932016-04-13 Uncovering the Genetic Architectures of Quantitative Traits Lee, James J. Vattikuti, Shashaank Chow, Carson C. Comput Struct Biotechnol J Mini Review The aim of a genome-wide association study (GWAS) is to identify loci in the human genome affecting a phenotype of interest. This review summarizes some recent work on conceptual and methodological aspects of GWAS. The average effect of gene substitution at a given causal site in the genome is the key estimand in GWAS, and we argue for its fundamental importance. Implicit in the definition of average effect is a linear model relating genotype to phenotype. The fraction of the phenotypic variance ascribable to polymorphic sites with nonzero average effects in this linear model is called the heritability, and we describe methods for estimating this quantity from GWAS data. Finally, we show that the theory of compressed sensing can be used to provide a sharp estimate of the sample size required to identify essentially all sites contributing to the heritability of a given phenotype. Research Network of Computational and Structural Biotechnology 2015-11-23 /pmc/articles/PMC4816193/ /pubmed/27076877 http://dx.doi.org/10.1016/j.csbj.2015.10.002 Text en http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Mini Review
Lee, James J.
Vattikuti, Shashaank
Chow, Carson C.
Uncovering the Genetic Architectures of Quantitative Traits
title Uncovering the Genetic Architectures of Quantitative Traits
title_full Uncovering the Genetic Architectures of Quantitative Traits
title_fullStr Uncovering the Genetic Architectures of Quantitative Traits
title_full_unstemmed Uncovering the Genetic Architectures of Quantitative Traits
title_short Uncovering the Genetic Architectures of Quantitative Traits
title_sort uncovering the genetic architectures of quantitative traits
topic Mini Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816193/
https://www.ncbi.nlm.nih.gov/pubmed/27076877
http://dx.doi.org/10.1016/j.csbj.2015.10.002
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