Correcting Pervasive Errors in Genotypic Datasets to Develop Genetic Maps
AbstractGenetic mapping studies provide improved estimates for novel genomic loci, allelic effects and gene action controlling important traits. Such mapping studies are regularly performed by using a combination of genotypic data (e.g., genotyping markers tagging genetic variation within populations) and phenotypic data of appropriately structured mapping populations. Randomly obtained DNA information and more recent high-throughput genome sequencing efforts have dramatically increased the ability to obtain genetic markers for any plant species. Despite the presence of constantly and rapidly increasing genotypic data, necessary steps to determine whether specific markers can be associated with genetic variation may often be initially neglected, meaning that ever-growing genotypic markers do not necessarily maximize the power of mapping studies and often generate false results. To address this issue, we present a framework for analyzing genotypic data while developing a genetic linkage map. Our goal is to raise awareness of a stepwise procedure in the development of genetic maps as well as to outline the current and potential contribution of this procedure to minimize bias caused by errors in genotypic datasets. Empirical results obtained from the R/qtl package for the statistical language/software R are prepared with details of how we handled genotypic data to develop the genetic map of a major plant species. This study provides a stepwise procedure to correct pervasive errors in genotypic data while developing genetic maps. For use in custom follow-up studies, we provide input files and written R codes. View Full-Text
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Hwang, S.; Lee, T.G. Correcting Pervasive Errors in Genotypic Datasets to Develop Genetic Maps. Agronomy 2019, 9, 196.
Hwang S, Lee TG. Correcting Pervasive Errors in Genotypic Datasets to Develop Genetic Maps. Agronomy. 2019; 9(4):196.Chicago/Turabian Style
Hwang, Sadal; Lee, Tong G. 2019. "Correcting Pervasive Errors in Genotypic Datasets to Develop Genetic Maps." Agronomy 9, no. 4: 196.
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