Keywords
SNPs, maize, regularization, crossing, Lasso, Ridge, Elastic Net
Abstract
Over a century, the maize crop has been one of the most important crop species that is targeted for genetic investigations and experiments. One of the major experiments that have been a topic of interest is crossing inbred lines to produce better offspring through a process called heterosis. Crossing the inbred lines create numerous SNP markers that determine the time to male flowering. This project seeks to explore the SNP markers to select the most relevant ones for predicting time to male flowering using linear regression with regularization methods due to the fact that p > n in our dataset. Various regularization methods were employed and compared. The l1-norm regularization method (LASSO) was chosen as the best regularization method for our data.
Course Name
STA 5703 Data Mining 1
Instructor Name
Dr. Rui Xie
College
College of Sciences
STARS Citation
Fiagbe, Roland, "Linear Regression with Regularization on the Genetic Architecture of Maize Flowering Time" (2023). Data Science and Data Mining. 8.
https://stars.library.ucf.edu/data-science-mining/8