👤 Francesco Bettella

Schizophrenia (SCZ) is associated with differences in subcortical brain volumes and intracranial volume (ICV). However, little is known about the underlying etiology of these brain alterations. Here, we explored whether brain structure volumes and SCZ share genetic risk factors. Using conditional false discovery rate (FDR) analysis, we integrated genome-wide association study (GWAS) data on SCZ (n = 82315) and GWAS data on 7 subcortical brain volumes and ICV (n = 11840). By conditioning the FDR on overlapping associations, this statistical approach increases power to discover genetic loci. To assess the credibility of our approach, we studied the identified loci in larger GWAS samples on ICV (n = 26577) and hippocampal volume (n = 26814). We observed polygenic overlap between SCZ and volumes of hippocampus, putamen, and ICV. Based on conjunctional FDR < 0.05, we identified 2 loci shared between SCZ and ICV implicating genes FOXO3 (rs10457180) and ITIH4 (rs4687658), 2 loci shared between SCZ and hippocampal volume implicating SLC4A10 (rs4664442) and SPATS2L (rs1653290), and 2 loci shared between SCZ and volume of putamen implicating DCC (rs4632195) and DLG2 (rs11233632). The loci shared between SCZ and hippocampal volume or ICV had not reached significance in the primary GWAS on brain phenotypes. Proving our point of increased power, 2 loci did reach genome-wide significance with ICV (rs10457180) and hippocampal volume (rs4664442) in the larger GWAS. Three of the 6 identified loci are novel for SCZ. Altogether, the findings provide new insights into the relationship between SCZ and brain structure volumes, suggesting that their genetic architectures are not independent. Show less

Recent genome-wide association studies (GWAS) and pathway analyses supported long-standing observations of an association between immune-mediated diseases and Parkinson disease (PD). The post-GWAS era provides an opportunity for cross-phenotype analyses between different complex phenotypes. To test the hypothesis that there are common genetic risk variants conveying risk of both PD and autoimmune diseases (ie, pleiotropy) and to identify new shared genetic variants and their pathways by applying a novel statistical framework in a genome-wide approach. Using the conjunction false discovery rate method, this study analyzed GWAS data from a selection of archetypal autoimmune diseases among 138 511 individuals of European ancestry and systemically investigated pleiotropy between PD and type 1 diabetes, Crohn disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis. NeuroX data (6927 PD cases and 6108 controls) were used for replication. The study investigated the biological correlation between the top loci through protein-protein interaction and changes in the gene expression and methylation levels. The dates of the analysis were June 10, 2015, to March 4, 2017. The primary outcome was a list of novel loci and their pathways involved in PD and autoimmune diseases. Genome-wide conjunctional analysis identified 17 novel loci at false discovery rate less than 0.05 with overlap between PD and autoimmune diseases, including known PD loci adjacent to GAK, HLA-DRB5, LRRK2, and MAPT for rheumatoid arthritis, ulcerative colitis and Crohn disease. Replication confirmed the involvement of HLA, LRRK2, MAPT, TRIM10, and SETD1A in PD. Among the novel genes discovered, WNT3, KANSL1, CRHR1, BOLA2, and GUCY1A3 are within a protein-protein interaction network with known PD genes. A subset of novel loci was significantly associated with changes in methylation or expression levels of adjacent genes. The study findings provide novel mechanistic insights into PD and autoimmune diseases and identify a common genetic pathway between these phenotypes. The results may have implications for future therapeutic trials involving anti-inflammatory agents. Show less