Comparison of Weighted and Unweighted Stage‐Wise Analysis for Genome‐Wide Association Studies and Genomic Selection
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Both genome-wide association studies (GWAS) and genomic selection (GS) are done using phenotypic and genomic data. The phenotypic data are usually based on multi-environment trials (MET). For both GWAS and GS the analysis can be conducted using a single-stage or a stage-wise approach. Single-stage analysis is most efficient but it can also be computationally demanding. The computational demand increases compared to purely phenotypic analysis when marker information is added for doing the GWAS or the GS. Application of stage-wise analysis is a common alternative procedure to alleviate the computational burden in MET analysis, and it can also be used for GWAS and/or GS. If done properly, it can closely mimic single-stage analysis. The aim of this study is to compare weighted stage-wise analysis versus unweighted stage-wise analysis for GWAS and GS using phenotypic and genotypic maize data. For weighting we use a fully efficient and a diagonal method. Our result show that weighting is to be preferred over unweighted analysis and that there is a modest advantage in using the fully efficient weighting method over other weighting methods for GS. For GWAS the diagonal weighting method performs better, however, its difference from the fully efficient weighting is very small.
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