TITLE:
An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis
AUTHORS:
Jixiang Wu, Johnie N. Jenkins, Jack C. McCarty Jr.
KEYWORDS:
Adaptive Sequential Replacement, Association Mapping, Exhaustive Method, Global Optimal Solution, Sequential Replacement, Variable Selection
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.5,
October
25,
2023
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.