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

J 2012

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

SILVER, Matt, Eva JANOUŠOVÁ, Xue HUA, Paul M. THOMPSON, Giovanni MONTANA et. al.

Basic information

Original name

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

Authors

SILVER, Matt (826 United Kingdom of Great Britain and Northern Ireland, guarantor), Eva JANOUŠOVÁ (203 Czech Republic, belonging to the institution), Xue HUA (840 United States of America), Paul M. THOMPSON (840 United States of America) and Giovanni MONTANA (840 United States of America)

Edition

Neuroimage, Elsevier, 2012, 1053-8119

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

Genetics and molecular biology

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

Impact factor

Impact factor: 6.252

RIV identification code

RIV/00216224:14110/12:00063448

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1016/j.neuroimage.2012.08.002

UT WoS

000310379100066

Keywords in English

Alzheimer's disease; Imaging genetics; Atrophy; Gene pathways; Sparse regression

Abstract

V originále

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 99 probable AD patients and 164 healthy elderly controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathway 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 insulin signalling, vascular smooth muscle contraction and focal adhesion. 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. High ranking genes include a number previously linked in gene expression studies to beta-amyloid plaque formation in the AD brain (PIK3R3,PIK3CC, PRKCA and PRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE.

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Citovat

SILVER, Matt, Eva JANOUŠOVÁ, Xue HUA, Paul M. THOMPSON and Giovanni MONTANA. Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression. Neuroimage. Elsevier, 2012, vol. 63, No 3, p. 1681-1694. ISSN 1053-8119. Available from: https://dx.doi.org/10.1016/j.neuroimage.2012.08.002.

@article{1084918,
   author = {Silver, Matt and Janoušová, Eva and Hua, Xue and Thompson, Paul M. and Montana, Giovanni},
   article_number = {3},
   doi = {http://dx.doi.org/10.1016/j.neuroimage.2012.08.002},
   keywords = {Alzheimer's disease; Imaging genetics; Atrophy; Gene pathways; Sparse regression},
   language = {eng},
   issn = {1053-8119},
   journal = {Neuroimage},
   title = {Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression},
   volume = {63},
   year = {2012}
}

TY  - JOUR
ID  - 1084918
AU  - Silver, Matt - Janoušová, Eva - Hua, Xue - Thompson, Paul M. - Montana, Giovanni
PY  - 2012
TI  - Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression
JF  - Neuroimage
VL  - 63
IS  - 3
SP  - 1681-1694
EP  - 1681-1694
PB  - Elsevier
SN  - 10538119
KW  - Alzheimer's disease
KW  - Imaging genetics
KW  - Atrophy
KW  - Gene pathways
KW  - Sparse regression
N2  - 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 99 probable AD patients and 164 healthy elderly controls in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathway 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 insulin signalling, vascular smooth muscle contraction and focal adhesion. 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. High ranking genes include a number previously linked in gene expression studies to beta-amyloid plaque formation in the AD brain (PIK3R3,PIK3CC, PRKCA and PRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE.
ER  -

SILVER, Matt, Eva JANOUŠOVÁ, Xue HUA, Paul M. THOMPSON and Giovanni MONTANA. Identification of gene pathways implicated in Alzheimer's disease using longitudinal imaging phenotypes with sparse regression. \textit{Neuroimage}. Elsevier, 2012, vol.~63, No~3, p.~1681-1694. ISSN~1053-8119. Available from: https://dx.doi.org/10.1016/j.neuroimage.2012.08.002.

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