@article{burghardt2017Guide,
	title = {A {Guide} to {Genome}-{Wide} {Association} {Mapping} in {Plants}},
	volume = {2},
	issn = {2379-8068},
	url = {https://currentprotocols.onlinelibrary.wiley.com/doi/abs/10.1002/cppb.20041},
	doi = {10.1002/cppb.20041},
	abstract = {Genome-wide association studies (GWAS) have developed into a valuable approach for identifying the genetic basis of phenotypic variation. In this article, we provide an overview of the design, analysis, and interpretation of GWAS. First, we present results from simulations that explore key elements of experimental design as well as considerations for collecting the relevant genomic and phenotypic data. Next, we outline current statistical methods and tools used for GWA analyses and discuss the inclusion of covariates to account for population structure and the interpretation of results. Given that many false positive associations will occur in any GWA analysis, we highlight strategies for prioritizing GWA candidates for further statistical and empirical validation. While focused on plants, the material we cover is also applicable to other systems. © 2017 by John Wiley \& Sons, Inc.},
	language = {en},
	number = {1},
	urldate = {2019-11-22},
	journal = {Current Protocols in Plant Biology},
	author = {Burghardt, Liana T. and Young, Nevin D. and Tiffin, Peter},
	year = {2017},
	keywords = {GWAS, QTL mapping, association mapping, genomics, genotype, phenotype},
	pages = {22--38}
}

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