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      The advantages and limitations of trait analysis with GWAS: a review

      review-article
      1 , , 1
      Plant Methods
      BioMed Central
      GWAS, Arabidopsis, Mixed model, Effect size, Genetic heterogeneity

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          Abstract

          Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.

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          Most cited references49

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          Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems.

          Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.
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            Rare and common variants: twenty arguments.

            Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.
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              An efficient multi-locus mixed model approach for genome-wide association studies in structured populations

              Population structure causes genome-wide linkage disequilibrium between unlinked loci, leading to statistical confounding in genome-wide association studies. Mixed models have been shown to handle the confounding effects of a diffuse background of large numbers of loci of small effect well, but do not always account for loci of larger effect. Here we propose a multi-locus mixed model as a general method for mapping complex traits in structured populations. Simulations suggest that our method outperforms existing methods, in terms of power as well as false discovery rate. We apply our method to human and Arabidopsis thaliana data, identifying novel associations in known candidates as well as evidence for allelic heterogeneity. We also demonstrate how a priori knowledge from an A. thaliana linkage mapping study can be integrated into our method using a Bayesian approach. Our implementation is computationally efficient, making the analysis of large datasets (n > 10000) practicable.
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                Author and article information

                Contributors
                Journal
                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central
                1746-4811
                2013
                22 July 2013
                : 9
                : 29
                Affiliations
                [1 ]Gregor Mendel Institute of Molecular Plant Biology, Vienna, Austria
                Article
                1746-4811-9-29
                10.1186/1746-4811-9-29
                3750305
                23876160
                3f5209c3-2f8b-4f8d-9c83-f1832b3c831b
                Copyright © 2013 Korte and Farlow; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 February 2013
                : 13 June 2013
                Categories
                Review

                Plant science & Botany
                gwas,arabidopsis,mixed model,effect size,genetic heterogeneity
                Plant science & Botany
                gwas, arabidopsis, mixed model, effect size, genetic heterogeneity

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