FUMA (Watanabe et al., 2017), a web-based tool that incorporates information from multiple public databases, was used to prioritize potentially causal genes. Three gene prioritization strategies are implemented in FUMA: positional mapping, expression Quantitative Trait Locus (eQTL) mapping, and chromatin interaction mapping. Positional mapping annotates variants based on their physical positions and functional consequences. For any region in which at least one variant met genomewide significance, all variants in that region with p values ≤1.0E-04 were annotated. If an annotated variant was in the promoter or untranslated regions (UTRs) of a gene, or in exonic or splicing regions of a gene and predicted to be deleterious, that gene was prioritized as a potential causal gene. Ensembl (build 85; http://www.ensembl.org/) was used to map variants to genes. For eQTL mapping, genes within 1 Mb of the most significant variant were tested, and those with significant associations (defined as FDR <0.05) were considered as potential causal genes. eQTL data from brain tissues in BRAINEAC (http://www.braineac.org/), CommonMind Consortium (https://www.synapse.org), and GTEx v7 (https://www.gtexportal.org/) were used. Genes prioritized by any of these three strategies were considered as potential causal genes and further examined by subsequent RNA expression analysis.
