Brain morphology and underlying genetic correlates in schizophrenia

Abstract

Schizophrenia (SZ) is a psychiatric disorder associated with brain and neurodevelopmental alterations that affects nearly 1% of the worldwide population. SZ is a complex disorder, polygenic and multifactorial, and despite large research efforts, its etiopathophysiology is still largely unknown. In this study, we assessed whether polymorphic variation in genes of the Sonic Hedgehog signaling pathway were associated with brain shape variation and contributed to differences between individuals diagnosed with SZ and healthy controls (HC). We genotyped three SNPs in SUFU, SHH and GLI3 genes (SUFU‐rs10786679, SHH‐rs10949808, GLI3‐rs3735361) in a sample of 308 subjects (113 patients with SZ and 195 HC), and quantified brain shape differences in a subsample of 100 subjects using the cartesian coordinates of brain landmarks recorded on head magnetic resonance scans.The genetic association analyses revealed that the T allele of the SHH‐rs10949808 was significantly associated with the risk for SZ, in both allelic (P=0.02586) and recessive (p=0.0312) models. Geometric Morphometric analyses showed that brain shape was significantly different between individuals diagnosed with SZ and HC. Discriminant functions correctly classified 72% of HC and 60% of patients based on brain morphology. Procrustes‐ANOVA analysis detected that diagnosis was a significant factor (P=0.001) and explained 5.11% of brain morphological variance; whereas any SNP presented significant effects in brain shape variance when analysed separately. However, when the three SNPs were combined into a single variable quantifying the number of risk variants (0 to 3) carried by each person, the analyses showed that patients with 3 risk variants presented a significantly different brain morphology as compared those carrying 2 (P=0.0297) or none risk variants (P=0.0487). These findings underscore the role of neurodevelopment‐related genes and Sonic Hedgehog pathway in SZ and the need to account for the polygenic nature of this disorder to further understand its etiology. Combining brain and genetic data, along with other morphological biomarkers, may open new lines of research in psychotic disorders.