Menu
Currency
GWAS Study

A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's disease in the ADNI cohort.

Meda SA, Narayanan B, Liu J et al.

22245343 PubMed ID
GWAS Study Type
757 Participants
44 Views
Scroll to explore
Chapter I

Publication Details

Comprehensive information about this research publication

Authors

MS
Meda SA
NB
Narayanan B
LJ
Liu J
PN
Perrone-Bizzozero NI
SM
Stevens MC
CV
Calhoun VD
GD
Glahn DC
SL
Shen L
RS
Risacher SL
SA
Saykin AJ
PG
Pearlson GD
Chapter II

Abstract

Summary of the research findings

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype-phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5 T scanners at over 50 sites in the USA/Canada. Four primary "genetic components" were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.

367 European ancestry individuals with mild cognitive impairment, 181 European ancestry individuals with mild early-stage LOAD, 209 European ancestry controls

Chapter III

Study Statistics

Key metrics and study information

757
Total Participants
GWAS
Study Type
No
Replicated
European
Ancestry
U.S., Canada
Recruitment Country
Chapter IV

AI-Generated Summary

AI-generated by DNAGENICS

Independent AI summary of health and genetic findings from the published study

Important: This summary is AI-generated by DNAGENICS for informational purposes only. It was not created by, affiliated with, or endorsed by the researchers behind the original publication, and is based solely on that published research. It may contain errors or omissions. DNAGENICS disclaims all liability for any inaccuracies or consequences arising from use of this information. Verify all information against the original publication. This is not professional scientific review or medical advice.

AI Summary In Progress

Our AI-generated summary of this publication is being prepared. Please check back soon.