World Health Organization. Facts sheet: suicide. https://www.who.int/news-room/fact-sheets/detail/suicide.
National Institute of Mental Health. Suicide. https://www.nimh.nih.gov/health/statistics/suicide.
Nock MK, Hwang I, Sampson N, Kessler RC, Angermeyer M, Beautrais A, et al. Cross-national analysis of the associations among mental disorders and suicidal behavior: findings from the WHO world mental health surveys. PLoS Med. 2009;6:e1000123.
Article PubMed PubMed Central Google Scholar
Tanney BL Psychiatric diagnoses and suicidal acts. In: RW Maris, AL Berman, MM Silverman, editors. Comprehensive textbook of suicidology, New York: Guilford Press; 2000. 311–41.
Cai H, Xie X-M, Zhang Q, Cui X, Lin J-X, Sim K, et al. Prevalence of suicidality in major depressive disorder: a systematic review and meta-analysis of comparative studies. Front Psych. 2021;12:690130.
Turecki G, Brent DA, Gunnell D, O’Connor RC, Oquendo MA, Pirkis J, et al. Suicide and suicide risk. Nat Rev Dis Prim. 2019;5:74.
Soreff SM, Basit H, Attia FN Suicide risk. Treasure Island: StatPearls Publishing; 2021.
Diekstra RFW, Garnefski N. On the nature, magnitude, and causality of suicidal behaviors: an international perspective. Suicide Life Threat Behav. 1995;25:36–57.
Article CAS PubMed Google Scholar
Voracek M, Loibl L. Genetics of suicide: a systematic review of twin studies. Wien Klin Wochenschr. 2007;119:463–75.
Article CAS PubMed Google Scholar
Ruderfer DM, Walsh CG, Aguirre MW, Tanigawa Y, Ribeiro JD, Franklin JC, et al. Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide. Mol Psych. 2020;25:2422–30.
Mullins N, Bigdeli TB, Børglum AD, Coleman JRI, Demontis D, Mehta D, et al. GWAS of suicide attempt in psychiatric disorders and association with major depression polygenic risk scores. Am J Psych. 2019;176:651–60.
Willour VL, Seifuddin F, Mahon PB, Jancic D, Pirooznia M, Steele J, et al. A genome-wide association study of attempted suicide. Mol Psych. 2012;17:433–44.
Galfalvy H, Zalsman G, Huang Y-Y, Murphy L, Rosoklija G, Dwork AJ, et al. A pilot genome wide association and gene expression array study of suicide with and without major depression. World J Biol Psych. 2013;14:574–82.
Black C, Miller BJ. Meta-analysis of cytokines and chemokines in suicidality: distinguishing suicidal versus nonsuicidal patients. Biol Psych. 2015;78:28–37.
Lindqvist D, Janelidze S, Hagell P, Erhardt S, Samuelsson M, Minthon L, et al. Interleukin-6 is elevated in the cerebrospinal fluid of suicide attempters and related to symptom severity. Biol Psych. 2009;66:287–92.
Enache D, Pariante CM, Mondelli V. Markers of central inflammation in major depressive disorder: a systematic review and meta-analysis of studies examining cerebrospinal fluid, positron emission tomography and post-mortem brain tissue. Brain Behav Immun. 2019;81:24–40.
Van Heeringen K, Mann JJ. The neurobiology of suicide. Lancet Psych. 2014;1:63–72.
Pandey G, Rizavi H, Bhaumik R, Ren X. Innate immunity in the postmortem brain of depressed and suicide subjects: role of Toll-like receptors. Brain Behav Immun. 2018;75:101–11.
Pantazatos SP, Huang Y-Y, Rosoklija GB, Dwork AJ, Arango V, Mann JJ. Whole-transcriptome brain expression and exon-usage profiling in major depression and suicide: evidence for altered glial, endothelial and ATPase activity. Mol Psych. 2017;22:760–73.
Policicchio S, Washer S, Viana J, Iatrou A, Burrage J, Hannon E, et al. Genome-wide DNA methylation meta-analysis in the brains of suicide completers. Transl Psych. 2020;10:69.
Labonte B, Yerko V, Gross J, Mechawar N, Meaney MJ, Szyf M, et al. Differential glucocorticoid receptor exon 1B, 1C, and 1H expression and methylation in suicide completers with a history of childhood abuse. Biol Psych. 2012;72:41–8.
Guintivano J, Brown T, Newcomer A, Jones M, Cox O, Maher BS, et al. Identification and replication of a combined epigenetic and genetic biomarker predicting suicide and suicidal behaviors. Am J Psych. 2014;171:1287–96.
Underwood MD, Bakalian MJ, Escobar T, Kassir S, Mann JJ, Arango V. Early-life adversity, but not suicide, is associated with less prefrontal cortex gray matter in adulthood. Int J Neuropsychopharmacol. 2019;22:349–57.
Article CAS PubMed PubMed Central Google Scholar
Spitzer RL, Williams JBW, Gibbon M, First MB. The structured clinical interview for DSM-III-R (SCID): I: history, rationale, and description. Arch Gen Psych. 1992;49:624–9.
First M, Gibbon M, Spitzer R, Benjamin L, Williams J. Structured clinical interview for DSM-IV axis II personality disorders (SCID-II). Washington DC: American Psychiatric Press, 1997.
Brown GL, Goodwin FK, Ballenger JC, Goyer PF, Major LF. Aggression in humans correlates with cerebrospinal fluid amine metabolites. Psych Res. 1979;1:131–9.
Oquendo MA, Halberstam B, Mann JJ. Risk factors for suicidal behavior: Utility and limitations of research instruments. In: First MB. editor. Standardized Evaluation in Clinical Practice. American Psychiatric Publishing, Inc.; 2003. pp. 103–130.
Kelly TM, Mann JJ. Validity of DSM-III-R diagnosis by psychological autopsy: a comparison with clinician ante-mortem diagnosis. Acta Psych Scand. 1996;94:337–43.
Clive ML, Boks MP, Vinkers CH, Osborne LM, Payne JL, Ressler KJ, et al. Discovery and replication of a peripheral tissue DNA methylation biosignature to augment a suicide prediction model. Clin Epigenetics. 2016;8:113.
Article PubMed PubMed Central Google Scholar
Martin M. CUTADAPT removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
Article CAS PubMed Google Scholar
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.
Article CAS PubMed Google Scholar
Wang L, Nie J, Sicotte H, Li Y, Eckel-Passow JE, Dasari S, et al. Measure transcript integrity using RNA-seq data. BMC Bioinforma. 2016;17:58.
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102:15545–50.
Article CAS PubMed PubMed Central Google Scholar
Igor Dolgalev. msigdbr: MSigDB gene sets for multiple organisms in a tidy data format. 2022.
Venables WN, Ripley BD. Modern applied statistics with S. 4th ed. Springer; 2002.
McKenzie AT, Katsyv I, Song W-M, Wang M, Zhang B. DGCA: a comprehensive R package for differential gene correlation analysis. BMC Syst Biol. 2016;10:106.
Article PubMed PubMed Central Google Scholar
Miller HE, Bishop AJR. Correlation AnalyzeR: functional predictions from gene co-expression correlations. BMC Bioinforma. 2021;22:206.
New York Genome Center. https://www.nygenome.org/.
Zhou W, Triche TJ Jr, Laird PW, Shen H. SeSAMe: reducing artifactual detection of DNA methylation by Infinium BeadChips in genomic deletions. Nucl Acids Res. 2018;46:e123.
PubMed PubMed Central Google Scholar
Ren X, Kuan PF. methylGSA: a Bioconductor package and Shiny app for DNA methylation data length bias adjustment in gene set testing. Bioinformatics. 2019;35:1958–9.
Article CAS PubMed Google Scholar
Dong M, Thennavan A, Urrutia E, Li Y, Perou CM, Zou F, et al. SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references. Brief Bioinform. 2021;22:416–27.
Article CAS PubMed Google Scholar
Wang X, Park J, Susztak K, Zhang NR, Li M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun. 2019;10:380.
Article CAS PubMed PubMed Central Google Scholar
Hodge RD, Bakken TE, Miller JA, Smith KA, Barkan ER, Graybuck LT, et al. Conserved cell types with divergent features in human versus mouse cortex. Nature. 2019;573:61–8.
Article CAS PubMed PubMed Central Google Scholar
The Allen Institute. Cell type database: RNA-Seq data. https://portal.brain-map.org/atlases-and-data/rnaseq.
Brooks M, Kristensen K, van Benthem K, Magnusson A, Berg CW, Nielsen A, et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 2017;9:378–400.
Russell V Lenth. emmeans: estimated marginal means, aka least-squares means. 2022. https://CRAN.R-project.org/package=emmeans.
Koller M. robustlmm: an R package for robust estimation of linear mixed-effects models. J Stat Softw. 2016;75:1–24.
Coutellier L, Beraki S, Ardestani PM, Saw NL, Shamloo M. Npas4: a neuronal transcription factor with a key role in social and cognitive functions relevant to developmental disorders. PLoS One. 2012;7:e46604.
Article CAS PubMed PubMed Central Google Scholar
Fu J, Guo O, Zhen Z, Zhen J. Essential functions of the transcription factor Npas4 in neural circuit development, plasticity, and diseases. Front Neurosci. 2020;14:603373.
Bloodgood BL, Sharma N, Browne HA, Trepman AZ, Greenberg ME. The activity-dependent transcription factor NPAS4 regulates domain-specific inhibition. Nature. 2013;503:121–5.
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