Efficient genotyping by sampling extreme individuals in a genome wide association study in plants
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We evaluated statistical power of selective genotyping strategies based on sampling extreme individuals a genome-wide association study (GWAS). Simulation with a theoretical set-up and with application in the actual data-set provides guidance on determining the minimum individuals from the extremes needed to detect causal variants to reach 80% statistical power. We compared power and false discovery rates of three different methods in a real-world sorghum diversity panel using Fisher’s exact test, analysis of variance (ANOVA) and a popular software GAPIT which applies mixed model for variant detection and controls for population structure. Our simulation results also discover that the power of detecting causal SNP markers in selective genotyping is dependent on the initial population size. This strat- egy is particularly helpful in genetic studies to reduce genotyping costs for variant detection and validation.