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

By Arthur Korte, Ashley Farlow. Plant Methods, 2013

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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.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/b1145d8c2eb59e49835445bcb5bcccce8db8e796

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Citations in the corpus

Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies (2017, background): “Existing solutions rely on traditional hypothesis testing after a dedicated confounding correction step, usually resulting in suboptimal performance [3], [4].”
New developments in fiber hemp (Cannabis sativa L.) breeding (2015, methodology): “Upon phenotyping of the mapping panel in three different environments, genome wide association mapping (GWAS; reviewed in Korte and Farlow, 2013) is being performed to provide information about important alleles, quantitative trait loci, and linked DNA polymorphisms in the underlying genes.”
Natural variations and genome-wide association studies in crop plants. (2014, background): “, seed size) have been under human selection and belong to domestication loci: Some QTLs are responsible only for natural intraspecies variation, and some have alleles that exist at a very low frequency in both cultivated and wild populations (55, 107).”
Identifying targets and agents of selection: innovative methods to evaluate the processes that contribute to local adaptation (2017, methodology): “GWAS use linear mixed models to find significant associations between phenotype and genotype across a large panel of individuals of either known or unknown relationship with one another (Korte & Farlow 2013).”
A practical guide to environmental association analysis in landscape genomics (2015, methodology): “…Christian Rellstab, Fax: +41 44 739 2215; E-mail: christian.rellstab@wsl.ch © 2015 John Wiley & Sons Ltd association studies (GWAS, reviewed in Korte & Farlow 2013) and quantitative trait locus (QTL) mapping (reviewed in Stinchcombe & Hoekstra 2008), take advantage of phenotypic…”
The Genetics and Genomics of Plant Domestication (2017, background): “Low-cost data as well as new algorithms and statistical tests permit estimation of genome-wide and region-specific proportions of putatively deleterious mutations and of their potential effects in a given genotype relative to other individuals within a species and to closely related species, such as”
Advances in the Application of Genetics in Marine Turtle Biology and Conservation (2017, background): “But beyond this, the versatility of HTS offers potential for a broad diversity of applications, such as genomewide association studies (GWAS) to identify the genomic basis of key phenotypic traits (Korte and Farlow, 2013), rapid genotyping of individuals tracked over larger spatio-temporal scales (e”