Previous studies have suggested that vitamin D (VD) was associated with psychiatric diseases, but efforts to elucidate the functional relevance of VD with depression and anxiety from genetic perspective have been limited. Based on the UK Biobank cohort, we first calculated polygenic risk score (PRS) for VD from genome-wide association study (GWAS) data of VD. Linear and logistic regression analysis were conducted to evaluate the associations of VD traits with depression and anxiety traits, respectively. Then, using individual genotype and phenotype data from the UK Biobank, genome-wide environment interaction studies (GWEIS) were performed to identify the potential effects of gene × VD interactions on the risks of depression and anxiety traits. In the UK Biobank cohort, we observed significant associations of blood VD level with depression and anxiety traits, as well as significant associations of VD PRS and depression and anxiety traits. GWEIS identified multiple candidate loci, such as rs114086183 (p = 4.11 × 10⁻⁸, LRRTM4) for self-reported depression status and rs149760119 (p = 3.88 × 10⁻⁸, GNB5) for self-reported anxiety status. Our study results suggested that VD was negatively associated with depression and anxiety. GWEIS identified multiple candidate genes interacting with VD, providing novel clues for understanding the biological mechanism potential associations between VD and psychiatric disorders.

Although previous genome-wide association studies (GWASs) on post-traumatic stress disorder (PTSD) have identified multiple risk loci, how these loci confer risk of PTSD remains unclear. Through the FUSION pipeline, we integrated two human brain proteome reference datasets (ROS/MAP and Banner) with the PTSD GWAS dataset, respectively, to conduct a proteome-wide association study (PWAS) analysis. Then two transcriptome reference weights (Rnaseq and Splicing) were applied to a transcriptome-wide association study (TWAS) analysis. Finally, the PWAS and TWAS results were investigated through brain imaging analysis. In the PWAS analysis, 8 and 13 candidate genes were identified in the ROS/MAP and Banner reference weight groups, respectively. Examples included ADK (p_PWAS-ROS/MAP = 3.00 × 10⁻⁵) and C3orf18 (p_PWAS-Banner = 7.07 × 10⁻³¹). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (p_{TWAS-Splicing} = 3.84 × 10⁻²), CHMP1A (p_TWAS-Rnaseq = 5.09 × 10⁻⁴), and SIRT5 (p_{TWAS-Splicing} = 4.81 × 10⁻³). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (p_PWAS-Banner = 4.90 × 10⁻², p_{TWAS-Splicing} = 1.23 × 10⁻²) in the total population and GLO1(p_PWAS-Banner = 4.89 × 10⁻³, p_TWAS-Rnaseq = 1.41 × 10⁻³) in females. Brain imaging analysis revealed several different brain imaging phenotypes associated with MADD and GLO1 genes. Our study identified multiple candidate genes associated with PTSD in the proteome and transcriptome levels, which may provide new clues to the pathogenesis of PTSD.

Background Pain symptoms are common in the patients with depression. Comparing with the general population, the pain in depression patients has more complex biological mechanism. We aim to explore the etiological mechanism of pain in depression patients from the perspective of genetics. Methods Utilizing the UK Biobank samples with self-reported depression status or PHQ score ≥10, we conducted genome-wide association study (GWAS) of seven pain traits (N=1,133-58,349). The GWAS summary were then integrated with two different reference protein weights (ROS/MAP and Banner) for proteome-wide association study (PWAS) using the FUSION pipeline. Additionally, LDSC analysis was performed to explore the genetic correlation between pain traits in depression patients and common psychiatry disorders. And biological processes and functions that related to pain associated genes in depression patients were analyzed by gene set enrichment analysis. Results GWAS identified 3 significant genes associated with different pain traits in depression patients, including TRIOBP (P_GWAS= 4.48× 10⁻⁸) for stomach or abdominal pain, SLC9A9(P_GWAS= 2.77× 10⁻⁸) for multisite chronic pain (MCP) and ADGRF1 (P_GWAS= 1.51× 10⁻⁸) for neck or shoulder pain. PWAS also identified multiple candidate genes associated with different pain traits in depression patients, such as TPRG1L (permutation-based P_{PWAS−Banner}= 3.38× 10⁻²) and SIRPA (permutation-based P_{PWAS−Banner}= 3.65×10⁻²) for MCP etc. LDSC analysis results showed that MCP was positively correlated with attention-deficit hyperactivity disorder (ADHD) (genetic correlation(rg) = 0.123, P_LDSC = 0.039) and post-traumatic stress disorder (PTSD) (rg = 0.217, P_LDSC = 0.029). Conclusions We reported multiple novel candidate genes and genetic correlations for pain traits in depression patients, providing novel clues for understanding the genetic mechanisms underlying the pain in depression patients.