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ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #408750
Research Project: Enhancing Agronomic Traits, Fiber Quality, and Resistance to Environmental Stress, Nematodes, and Fungal Diseases in Cotton

Location: Genetics and Sustainable Agriculture Research

Title: An adaptive sequential replacement method to variable selection in linear regression analysis

Author
item Wu, Jixiang
item Jenkins, Johnie
item McCarty, Jack

Submitted to: Open Journal of Statistics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/22/2023
Publication Date: 10/25/2023
Citation: Wu, J., Jenkins, J.N., Mccarty Jr, J.C. 2023. An adaptive sequential replacement method to variable selection in linear regression analysis. Open Journal of Statistics. 13:746-760. https://doi.org/10.4236/ojs.2023.135036.
DOI: https://doi.org/10.4236/ojs.2023.135036

Interpretive Summary: Selecting a set of k loci that can capture maximum genetic variation from genomic data requires a method to be robust, consistent, and less time-consuming. In this study, we proposed an adaptive sequential replacement (ASR) method, which is a variant of the sequential replacement (SR) method. This ASR method was numerically evaluated through Monte Carlo simulation and was compared to four other selection methods: exhaustive, SR method, forward, and backward methods. The results showed that the ASR method yielded consistent and repeatable results comparable to the exhaustive method, which is prohibited due to high computational burden. This ASR method along with the R package can be applied to genetic association mapping studies to detect genetic variation for marker-assisted selection.

Technical Abstract: With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual 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 replacement (SR) method. Through Monte Carlo simulation and comparing 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.