Background Endophenotypes are laboratory-based measures hypothesized to lie in the causal chain between genes and clinical disorder, and to serve as a more powerful way to identify genes associated with the disorder. One promise of endophenotypes is that they may assist in elucidating the neurobehavioral mechanisms by which an associated genetic polymorphism affects disorder risk in complex traits. We evaluated this promise by testing the extent to which variants discovered to be associated with schizophrenia through large-scale meta-analysis show associations with psychophysiological endophenotypes. Method We genome-wide genotyped and imputed 4905 individuals. Of these, 1837 were whole-genome-sequenced at 11× depth. In a community-based sample, we conducted targeted tests of variants within schizophrenia-associated loci, as well as genome-wide polygenic tests of association, with 17 psychophysiological endophenotypes including acoustic startle response and affective startle modulation, antisaccade, multiple frequencies of resting electroencephalogram (EEG), electrodermal activity and P300 event-related potential. Results Using single variant tests and gene-based tests we found suggestive evidence for an association between contactin 4 (CNTN4) and antisaccade and P300. We were unable to find any other variant or gene within the 108 schizophrenia loci significantly associated with any of our 17 endophenotypes. Polygenic risk scores indexing genetic vulnerability to schizophrenia were not related to any of the psychophysiological endophenotypes after correction for multiple testing. Conclusions The results indicate significant difficulty in using psychophysiological endophenotypes to characterize the genetically influenced neurobehavioral mechanisms by which risk loci identified in genome-wide association studies affect disorder risk.
Bibliographical noteFunding Information:
This research was supported through National Institutes of Health grants AA023974, HG008983, DA037904, DA040177, DA041120, DA036216, DA005147, AA009367 and DA024417.
Copyright © Cambridge University Press 2016.
- gene-based tests
- polygenic risk scores
- single variant association