Concordance analysis of microarray studies identifies representative gene expression changes in Parkinson's disease: a comparison of 33 human and animal studies.
BACKGROUND
As the popularity of transcriptomic analysis has grown, the reported lack of concordance between different studies of the same condition has become a growing concern, raising questions as to the representativeness of different study types, such as non-human disease models or studies of surrogate tissues, to gene expression in the human condition.


METHODS
In a comparison of 33 microarray studies of Parkinson's disease, correlation and clustering analyses were used to determine the factors influencing concordance between studies, including agreement between different tissue types, different microarray platforms, and between neurotoxic and genetic disease models and human Parkinson's disease.


RESULTS
Concordance over all studies is low, with correlation of only 0.05 between differential gene expression signatures on average, but increases within human patients and studies of the same tissue type, rising to 0.38 for studies of human substantia nigra. Agreement of animal models, however, is dependent on model type. Studies of brain tissue from Parkinson's disease patients (specifically the substantia nigra) form a distinct group, showing patterns of differential gene expression noticeably different from that in non-brain tissues and animal models of Parkinson's disease; while comparison with other brain diseases (Alzheimer's disease and brain cancer) suggests that the mixed study types display a general signal of neurodegenerative disease. A meta-analysis of these 33 microarray studies demonstrates the greater ability of studies in humans and highly-affected tissues to identify genes previously known to be associated with Parkinson's disease.


CONCLUSIONS
The observed clustering and concordance results suggest the existence of a 'characteristic' signal of Parkinson's disease found in significantly affected human tissues in humans. These results help to account for the consistency (or lack thereof) so far observed in microarray studies of Parkinson's disease, and act as a guide to the selection of transcriptomic studies most representative of the underlying gene expression changes in the human disease.
Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK.
Erin
E
Oerton
Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. ab454@cam.ac.uk.
Andreas
http://orcid.org/0000-0002-7367-4263
A
Bender
D000818:Animals; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D051379:Mice; D046228:Microarray Analysis / Q000706:statistics & numerical data*; D010300:Parkinson Disease / Q000235:genetics*; D051381:Rats
Concordance; Gene expression; Meta-analysis; Microarray; Parkinson’s disease