Comparison of Methods for Longitudinal Analysis of Quantitative Traits in Genome-wide Association Studies
Year: 2019
Type: dissertation
Abstract: Longitudinal genome-wide association studies provide us more information on the relationship between repeated-measured traits and genetic variants (SNPs) than cross-sectional ones. The Linear Mixed Model (LMM) has been a popular tool for such analysis but is deficient in computational speed. Inspired by fast methods proposed by Sikorska et al. which work as an approximation of LMM, we extended previous simulation scenarios and made comparison on cross-sectional, longitudinal SNP effects and joint test inference between fast methods and LMM. We also applied fast methods on real data for each of two renal outcomes. Results showed that using fast methods, we detected several SNPs with longitudinal effects on the two outcomes. These fast methods are effective and much faster than LMM, however they differ in computational efficiency and speed. We also discuss limitations existing in our simulation study and application to real data, along with discussion of future directions.
Source: TSpace (University of Toronto)
Author: Mengdan Xu
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2996471722