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Multiple integrative studies have been performed to identify the potential target genes of the non-coding schizophrenia (SCZ) risk variants. However, all the integrative studies used expression quantitative trait loci (eQTL) data from bulk tissues. Considering the cell type-specific regulatory effect of many genetic variants, it is important to conduct integrative studies using cell type-specific eQTL data. Here, we conduct a Mendelian randomization (MR) study by integrating genome-wide associations of SCZ (74,776 cases and 101,023 controls) and eQTL data (N = 215) from dopaminergic neurons, which were differentiated from human-induced pluripotent stem cell (iPSC) lines. For eQTL from young post-mitotic dopaminergic neurons (differentiation of iPSC for 30 days, D30), we identified 34 genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Among which, ARL3 showed the most significant associations with SCZ. For eQTL from more mature dopaminergic neurons (D52), we identified 37 potential SCZ causal genes, and ARL3 and GNL3 showed the most significant associations. Only 12 genes showed significant associations with SCZ in both D30 and D52 eQTL datasets, indicating the time point-specific genetic regulatory effects in young post-mitotic dopaminergic neurons and more mature dopaminergic neurons. Comparing the results from dopaminergic neurons with bulk brain tissues prioritized 2 high-confidence risk genes, including DDHD2 and GALNT10. Our study identifies multiple risk genes whose genetically regulated expression in dopaminergic neurons may have a causal role in SCZ. Further mechanistic investigation will provide pivotal insights into SCZ pathophysiology.

Large-scale genome-wide association studies (GWASs) have reported multiple risk variants for Parkinson's disease (PD). However, little is known about how these reported risk variants confer risk of PD.To nominate genes whose genetically regulated expression in dopaminergic neurons may have a causal role in PD.We conducted a two-sample Mendelian randomization (MR) study by integrating large-scale genome-wide associations and expression quantitative trait loci (eQTL) data from dopaminergic neurons.MR analysis nominated 10 risk genes whose genetically regulated expression in dopaminergic neurons may have a causal role in PD. These MR significant genes include FAM200B, NDUFAF2, NUP42, SH3GL2, STX1B, CCDC189, KAT8, PRSS36, VAMP4, and ZSWIM7.We report the first MR study of PD by using dopaminergic neuron-specific eQTL and nominate novel risk genes for PD. Further functional characterization of the nominated risk genes will provide mechanistic insights into PD pathogenesis and potential therapeutic targets. © 2022 International Parkinson and Movement Disorder Society.

Custom codes from"Genome-wide meta-analysis, functional genomics and integrative analyses implicate new risk genes and therapeutic targets for anxiety disorders".

Deciphering the genetic architecture of depression is pivotal for characterizing the associated pathophysiological processes and development of new therapeutics. Here we conducted a cross-ancestry genome-wide meta-analysis on depression (416,437 cases and 1,308,758 controls) and identified 287 risk loci, of which 140 are new. Variant-level fine-mapping prioritized potential causal variants and functional genomic analysis identified variants that regulate the binding of transcription factors. We validated that 80% of the identified functional variants are regulatory variants and expression quantitative trait loci (eQTL) analysis uncovered the potential target genes regulated by the prioritized risk variants. Gene-level analysis, including transcriptome-wide association study (TWAS), proteome-wide association study (PWAS), colocalization and Mendelian randomization-based analyses, prioritized potential causal genes and drug targets. Combining evidence from different analyses revealed likely causal genes, includingTMEM106B, CTNND1, EPHB2, AREL1, CSE1L, RAB27B, SATU1, TMEM258, DCC, etc. Pathway analysis showed significant enrichment of depression risk genes in synapse-related pathways. Finally, we showed thatTmem106bknockdown resulted in depression-like behaviors in mice, supporting involvement ofTmem106bin depression. Our study identified new risk loci, likely causal variants and genes for depression, providing important insights into the genetic architecture of depression and potential therapeutic targets.