Gloria (Xuefei) Yang

Title: Covariance-adjusted, Sparse, Reduced-Rank Regression with Adjustment for Confounders
Date: August 18, 2021
Time: 10:30 am (PDT)
Location: Remote delivery

Abstract

There is evidence that the common genetic variation in gene NEDD9 is associated with developing Alzheimer’s Disease (AD). In this project, we aim to take advantage of the relationship between the brain-imaging biomarkers of AD and the gene NEDD9 while adjusting the effect of genetic population structure. The data we used in this project is collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which contains magnetic resonance imaging (MRI) measures of 56 brain regions of interest for 200 cognitively normal people and their corresponding Single Nucleotide Polymorphisms (SNPs) obtained from 33 candidate genes for AD. To solve such multiple response problem, simple separate linear regression models neglect the correlations between 56 brain areas and possible sparsity in the SNP effects. Thus, we review and modify the sparse and covariance adjusted reduced-rank regression such that the new regression model can help us select significant predictors, estimate covariance simultaneously, and adjust the confounding variable.