LAUSR

Abstract Background The relationships between schizophrenia polygenic risk and connectome-wide brain connectivity remain unclear. In particular, it is unknown whether and how schizophrenia polygenic risk would influence functional connectivity both at the connectome level and in a state-independent way. In this study, we used multi-paradigm fMRI data from two independent cohorts to investigate these questions. Methods The discovery sample included 623 healthy Caucasian participants (age 28.86 ± 3.63 years, 302 males) acquired from the Human Connectome Project (HCP). All subjects completed fMRI scans for a battery of eight paradigms and had imputed genetic data available. Following the procedures in our prior studies, we constructed whole-brain connectivity matrices for each paradigm in each individual. From these derived matrices, we computed cross-paradigm connectivity (CPC) using principal component analysis. These CPC matrices quantify shared connectivity patterns across all paradigms for each individual and thus represent state-independent “trait” network architecture of each subject. The polygenic risk scores (PRSs) for each subject were calculated based on the genome-wide association study (GWAS) results from the Psychiatric Genomics Consortium. The scores were calculated as the sum of genome-wide risk alleles for each individual, weighted by the corresponding odds ratios to schizophrenia. We report our main findings based on the GWAS-significant threshold (P = 5×10–8). In addition, to test the robustness of our findings, we also calculated PRSs with a set of other thresholds ranging from 5×10–7 to 5×10–2. The network-based statistic (NBS) analysis was performed to associate PRSs with CPC matrices, where age, sex, and head motion were included as covariates. Significance was determined by 10,000 permutations of the original sample. The validation sample included 44 patients with schizophrenia, 43 patients with bipolar disorders, 34 patients with attention deficit hyperactivity disorder, and 77 healthy controls drawn from the Consortium for Neuropsychiatric Phenomics (CNP). All subjects completed a battery of seven fMRI paradigms. We used this sample to examine 1) whether the identified connectomic findings were specifically detected in patients with schizophrenia; and 2) whether these findings could be related to behavioral deficits in patients with schizophrenia. Results In the HCP sample, the NBS analysis revealed a significant association (PFWE < 0.05) between schizophrenia PRS and a large-scale network involving a total of 69 edges connecting between 54 nodes. These nodes were predominantly distributed in the brain’s visual system, default-mode system, and frontoparietal system. Specifically, higher PRSs were associated with lower connectivity for all connections in the identified network (R = -0.37). The results were significant across all paradigms (R < -0.13, P < 0.001) and remained robust across multiple PRS thresholds (R < -0.10, P < 0.02). In the CNP sample, the connectivity of the detected network differed significantly between groups (P = 0.005), which was particularly driven by decreased connectivity in patients with SZ compared with that in HCs (PBonferroni = 0.03). The connectivity of the identified network was significantly correlated with both performance IQ (R = 0.28, P =0.002) and verbal IQ (R = 0.29, P = 0.001). Discussion These findings provide the first evidence for state-independent connectome-wide associations of schizophrenia polygenic risk at the systems level and suggest that disrupted integration of sensori-cognitive information may be a hallmark of genetic effects on the brain that contributes to the pathogenesis of schizophrenia.