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Process genes list: An approach to link genetics and human brain imaging

Authors :
Ramiro Salas
David A. Nielsen
Michelle A. Patriquin
Savannah N. Gosnell
Guillermo F. Poblete
Thomas R. Kosten
Mary Fang
Matthew J. Meyer
Tien Nguyen
Source :
Journal of neuroscience methods. 339
Publication Year :
2019

Abstract

Background Linking human genetics and brain imaging data is extremely challenging, among other reasons because both fields suffer from multiple comparison problems. New method ProcessGeneLists (PGL) links genetics and human brain imaging by using genes associated with a disease and calculating a normalized mRNA expression average of those genes in each brain region. Brain regions in which those genes are most co-expressed become regions of interest (ROIs) to perform brain imaging in participants with and without the disease, decreasing multiple comparisons. Once a region is identified as “imaging-relevant”, the genes most responsible for that ROI being highlighted can be genotyped in the imaged sample. This allows to re-analyze imaging data under the light of likely relevant genetics, to study possible brain imaging/gene variant interactions. Results As proof-of-concept, we created two lists of genes expressed in the habenula and the striatum, to verified that PGL would highlight those regions. Next, we used a list of genes likely important in alcohol abuse from the literature, which identified several brain regions previously associated with alcohol abuse such as the striatum, habenula, and hippocampus. Comparison with existing methods To our knowledge there is no current method to obtain brain regions of interest from genetics data. Conclusions Genetics typically asks “which genes are associated with a disease?” while human brain imaging typically asks “which brain regions are associated with a disease?” PGL asks “which genes, via modulation within specific brain regions, are found to be associated with a disease?”.

Details

ISSN :
1872678X
Volume :
339
Database :
OpenAIRE
Journal :
Journal of neuroscience methods
Accession number :
edsair.doi.dedup.....39f83989c63b798cf1a4dd269f967e7f