Rose Mary Xavier1, Allison Vorderstrasse2, Richard S E Keefe3, Jennifer R Dungan4. 1. Neuropsychiatry Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, 1034 Gates Pavilion, HUP, 3400 Spruce Street, Philadelphia, PA 19104, United States. Electronic address: rxavier@pennmedicine.upenn.edu. 2. NYU Rory Meyers College of Nursing, United States. Electronic address: allisonvorderstrasse@nyu.edu. 3. Psychiatry and Behavioral Sciences, Duke University School of Medicine, United States. Electronic address: richard.keefe@duke.edu. 4. Duke University School of Nursing, United States. Electronic address: jennifer.dungan@duke.edu.
Abstract
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.
RCT Entities:
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.
Authors: Peter Kochunov; Yizhou Ma; Kathryn S Hatch; Neda Jahanshad; Paul M Thompson; Bhim M Adhikari; Heather Bruce; Andrew Van der Vaart; Eric L Goldwaser; Aris Sotiras; Mark D Kvarta; Tianzhou Ma; Shuo Chen; Thomas E Nichols; L Elliot Hong Journal: Hum Brain Mapp Date: 2022-08-30 Impact factor: 5.399