With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci assoc iated with traits of importance in various crop species. However, curren t gen etic association mapping analyses are focused on identifying individua l QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential repl acement (SR) method. Through Monte Carlo simulation and compar ing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.
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