Meta-analysis on the association between genetic polymorphisms and prepulse inhibition of the acoustic startle response.

Schizophr Res

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA; Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, New York, USA. Electronic address:

Published: August 2018

Sensorimotor gating measured by prepulse inhibition (PPI) of the acoustic startle response (ASR) has been proposed as one of the most promising electrophysiological endophenotypes of schizophrenia. During the past decade, a number of publications have reported significant associations between genetic polymorphisms and PPI in samples of schizophrenia patients and healthy volunteers. However, an overall evaluation of the robustness of these results has not been published so far. Therefore, we performed the first meta-analysis of published and unpublished associations between gene polymorphisms and PPI of ASR. Unpublished associations between genetic polymorphisms and PPI were derived from three independent samples. In total, 120 single observations from 16 independent samples with 2660 study participants and 43 polymorphisms were included. After correction for multiple testing based on false discovery rate and considering the number of analyzed polymorphisms, significant associations were shown for four variants, even though none of these associations survived a genome-wide correction (P<5∗10). These results imply that PPI might be modulated by four genotypes - COMT rs4680 (primarily in males), GRIK3 rs1027599, TCF4 rs9960767, and PRODH rs385440 - indicating a role of these gene variations in the development of early information processing deficits in schizophrenia. However, the overall impact of single genes on PPI is still rather small suggesting that PPI is - like the disease phenotype - highly polygenic. Future genome-wide analyses studies with large sample sizes will enhance our understanding on the genetic architecture of PPI.

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http://dx.doi.org/10.1016/j.schres.2017.12.011DOI Listing

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