Exploring the potential impact of TNF-α inhibition on major depressive disorder risk: Insights from a prospective cohort and genetic evidence—Supplement data

These datasets were prepared from publicly available GWAS summary statistics and is provided as the input data for the corresponding analysis code (https://github.com/Huanghao1111/TNFAImdd). The analysis was restricted to individuals of European ancestry.Data description and sources: 1. inflammation marker1) ukb-d-30000_irnt.csv: GWAS summary statistics for white blood cell (leukocyte) count obtained from the Neale Lab. The original dataset is available at: https://opengwas.io/datasets/ukb-d-30000_irnt2) ukb-d-30710_irnt.csv: GWAS summary statistics for C-reactive protein (CRP) obtained from the Neale Lab. The original dataset is available at: https://opengwas.io/datasets/ukb-d-30710_irnt 2. MDDMDD2025_PGC_no23andMe_noUKBB_eur.csv: GWAS summary statistics for major depressive disorder (MDD) obtained from the Psychiatric Genomics Consortium (PGC). Data from 23andMe and UK Biobank were excluded. The original GWAS was reported in: Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies. Cell, 2025; 188(3): 640–652.e9. doi:10.1016/j.cell.2024.12.002 3. MDD symptommdd_symptoms_2023-Clin-MDD1_depressed.txtmdd_symptoms_2023-Clin-MDD2_anhedonia.txtmdd_symptoms_2023-Clin-MDD3a_weightLoss.txtmdd_symptoms_2023-Clin-MDD3b_weightGain.txtmdd_symptoms_2023-Clin-MDD4a_sleepProb.txtmdd_symptoms_2023-Clin-MDD4b_sleepMore.txtmdd_symptoms_2023-Clin-MDD5a_psychomotorFast.txtmdd_symptoms_2023-Clin-MDD5b_psychomotorSlow.txtmdd_symptoms_2023-Clin-MDD6_fatigue.txtmdd_symptoms_2023-Clin-MDD7_worthless.txtmdd_symptoms_2023-Clin-MDD8_concentration.txtmdd_symptoms_2023-Clin-MDD9_death.txtThese GWAS datasets are available at: https://figshare.com/articles/dataset/GWAS_summary_statistics_for_major_depressive_disorder_symptoms/22745573These GWAS datasets were originally reported in: Adams, M. J., et al. "Genome-wide Meta-analysis of Ascertainment and Symptom Structures of Major Depression in Case-enriched and Community Cohorts." Psychological Medicine, vol. 54, no. 12, 2024, pp. 3459-3468. Psychological Medicine, https://doi.org/10.1017/S0033291724001880 4. inflammatory bowel disease1) CD.csv: GWAS summary statistics for Crohn's disease. The dataset is available at: https://opengwas.io/datasets/ieu-a-122) IBD.csv: GWAS summary statistics for inflammatory bowel disease. The dataset is available at: https://opengwas.io/datasets/ieu-a-2943) UC.csv: GWAS summary statistics for ulcerative colitis. The dataset is available at: https://opengwas.io/datasets/ieu-a-32These GWAS datasets were originally reported in: Liu JZ et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nature Genetics, 2015; 47(9): 979–986. doi:10.1038/ng.3359 5. Inflammatory skin conditions1) Psoriasis.csv: GWAS summary statistics for psoriasis obtained from the Million Veteran Program (MVP). The dataset is available at: https://www.ebi.ac.uk/gwas/studies/GCST90436606. This GWAS dataset was originally reported in: Verma A, Huffman JE, Rodriguez A, et al. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science, 2024; 385(6706): eadj1182. doi:10.1126/science.adj11822) HS.csv: GWAS summary statistics for hidradenitis suppurativa obtained from FinnGen. The dataset is available at: https://opengwas.io/datasets/finn-b-L12_HIDRADENITISSUP. This GWAS dataset was originally reported in: Kurki MI, Karjalainen J, Palta P, et al.; FinnGen. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 2023; 613(7944): 508–518. doi:10.1038/s41586-022-05473-8 6. Inflammatory arthritis1) AS.csv: GWAS summary statistics for ankylosing spondylitis. The dataset is available at: https://opengwas.io/datasets/ebi-a-GCST005529. This GWAS dataset was originally reported in: International Genetics of Ankylosing Spondylitis Consortium (IGAS). Identification of multiple risk variants for ankylosing spondylitis through high-density genotyping of immune-related loci. Nature Genetics, 2013; 45(7): 730–738. doi:10.1038/ng.26672) RA.csv: GWAS summary statistics for rheumatoid arthritis. The dataset is available at: https://www.ebi.ac.uk/gwas/publications/24390342. This GWAS dataset was originally reported in: Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature, 2014; 506(7488): 376–381. doi:10.1038/nature128733) PsA.csv: GWAS summary statistics for psoriatic arthritis (PsA). The dataset is available at: https://www.ebi.ac.uk/gwas/studies/GCST007043. This GWAS dataset was originally reported in: Aterido A, Cañete JD, Tornero J, et al. Genetic variation at the glycosaminoglycan metabolism pathway contributes to the risk of psoriatic arthritis but not psoriasis. Annals of the Rheumatic Diseases, 2019; 78(3): e214158. doi:10.1136/annrheumdis-2018-214158 7. Eye inflammationUveitis.csv: GWAS summary statistics for uveitis obtained from the Million Veteran Program (MVP). The dataset is available at:https://www.ebi.ac.uk/gwas/studies/GCST90477735. This GWAS dataset was originally reported in: Verma A, Huffman JE, Rodriguez A, et al. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science, 2024; 385(6706): eadj1182. doi:10.1126/science.adj1182 8. Negative Controls1) CHD.csv: GWAS summary statistics for congenital malformations of the heart and great arteries. Dataset available at: https://opengwas.io/datasets/finn-b-CONGEN_HEART_ARTER2) HD.csv: GWAS summary statistics for hereditary deficiency of other clotting factors. Dataset available at: https://opengwas.io/datasets/finn-b-D3_HEREDOTHCLOFACTORSThese GWAS datasets were originally reported in: Kurki MI, Karjalainen J, Palta P, et al.; FinnGen. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 2023; 613(7944): 508–518. doi:10.1038/s41586-022-05473-8