Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression

TL;DR

This paper introduces PsRRR, a novel sparse regression method that identifies gene pathways linked to Alzheimer's disease by analyzing longitudinal brain imaging and genetic data, revealing biologically relevant pathways and genes.

Contribution

The study develops and applies a new pathway-based sparse regression technique, PsRRR, for integrating genome-wide SNPs with longitudinal imaging data to uncover AD-related pathways.

Findings

Identified key pathways like chemokine, Jak-stat, and insulin signaling associated with AD.

Detected known AD genes such as CR1, APOE, and TOMM40.

Validated the method's ability to find biologically relevant pathways and genes.

Abstract

We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer's disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene-gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 464 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathways database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24 months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing chemokine, Jak-stat and insulin signalling pathways, and tight junction interactions. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection, and identify a number of previously validated AD genes including CR1, APOE and TOMM40.