TY - JOUR
T1 - Genetic correlates of insight in schizophrenia
AU - Xavier, Rose Mary
AU - Vorderstrasse, Allison
AU - Keefe, Richard S.E.
AU - Dungan, Jennifer R.
N1 - Funding Information:
Data for this study were obtained from the NIMH Repository and Genomics Resource (RGR). Dataset Identifier: NIMH Study 17, Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE)-schizophrenia trial. The principal investigators of the CATIE trial were Jeffrey A. Lieberman, M.D., T. Scott Stroup, M.D., M.P.H., and Joseph P. McEvoy, M.D. The CATIE trial was funded by a grant from the National Institute of Mental Health (N01 MH900001) along with MH074027 (PI PF Sullivan). Genotyping was funded by Eli Lilly and Company. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIMH or of the submitters submitting original data to NIMH RGR. This work was supported in part by a doctoral training grant to RX by the Robert Wood Johnson Foundation Future of Nursing Scholars program (project number 72099). We also gratefully acknowledge the support and data provided by the Schizophrenia Working Group of the Psychiatric Genomics Consortium for this study.
PY - 2018/5
Y1 - 2018/5
N2 - Insight in schizophrenia is clinically important as it is associated with several adverse outcomes. Genetic contributions to insight are unknown. We examined genetic contributions to insight by investigating if polygenic risk scores (PRS) and candidate regions were associated with insight. Method: Schizophrenia case-only analysis of the Clinical Antipsychotics Trials of Intervention Effectiveness trial. Schizophrenia PRS was constructed using Psychiatric Genomics Consortium (PGC) leave-one out GWAS as discovery data set. For candidate regions, we selected 105 schizophrenia-associated autosomal loci and 11 schizophrenia-related oligodendrocyte genes. We used regressions to examine PRS associations and set-based testing for candidate analysis. Results: We examined data from 730 subjects. Best-fit PRS at p-threshold of 1e − 07 was associated with total insight (R2 = 0.005, P = 0.05, empirical P = 0.054) and treatment insight (R2 = 0.005, P = 0.048, empirical P = 0.048). For models that controlled for neurocognition, PRS significantly predicted treatment insight but at higher p-thresholds (0.1 to 0.5) but did not survive correction. Patients with highest polygenic burden had 5.9 times increased risk for poor insight compared to patients with lowest burden. PRS explained 3.2% (P = 0.002, empirical P = 0.011) of variance in poor insight. Set-based analyses identified two variants associated with poor insight- rs320703, an intergenic variant (within-set P = 6e − 04, FDR P = 0.046) and rs1479165 in SOX2-OT (within-set P = 9e − 04, FDR P = 0.046). Conclusion: To the best of our knowledge, this is the first study examining genetic basis of insight. We provide evidence for genetic contributions to impaired insight. Relevance of findings and necessity for replication are discussed.
AB - Insight in schizophrenia is clinically important as it is associated with several adverse outcomes. Genetic contributions to insight are unknown. We examined genetic contributions to insight by investigating if polygenic risk scores (PRS) and candidate regions were associated with insight. Method: Schizophrenia case-only analysis of the Clinical Antipsychotics Trials of Intervention Effectiveness trial. Schizophrenia PRS was constructed using Psychiatric Genomics Consortium (PGC) leave-one out GWAS as discovery data set. For candidate regions, we selected 105 schizophrenia-associated autosomal loci and 11 schizophrenia-related oligodendrocyte genes. We used regressions to examine PRS associations and set-based testing for candidate analysis. Results: We examined data from 730 subjects. Best-fit PRS at p-threshold of 1e − 07 was associated with total insight (R2 = 0.005, P = 0.05, empirical P = 0.054) and treatment insight (R2 = 0.005, P = 0.048, empirical P = 0.048). For models that controlled for neurocognition, PRS significantly predicted treatment insight but at higher p-thresholds (0.1 to 0.5) but did not survive correction. Patients with highest polygenic burden had 5.9 times increased risk for poor insight compared to patients with lowest burden. PRS explained 3.2% (P = 0.002, empirical P = 0.011) of variance in poor insight. Set-based analyses identified two variants associated with poor insight- rs320703, an intergenic variant (within-set P = 6e − 04, FDR P = 0.046) and rs1479165 in SOX2-OT (within-set P = 9e − 04, FDR P = 0.046). Conclusion: To the best of our knowledge, this is the first study examining genetic basis of insight. We provide evidence for genetic contributions to impaired insight. Relevance of findings and necessity for replication are discussed.
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U2 - 10.1016/j.schres.2017.10.021
DO - 10.1016/j.schres.2017.10.021
M3 - Article
C2 - 29054485
AN - SCOPUS:85031685845
SN - 0920-9964
VL - 195
SP - 290
EP - 297
JO - Schizophrenia Research
JF - Schizophrenia Research
ER -