OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. CONCLUSIONS: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Huntsman Mental Health Institute Salt Lake City

Komentář v

Am J Psychiatry. 2023 Oct 1;180(10):705-707. doi: 10.1176/appi.ajp.20230610. PubMed

Zobrazit více v PubMed

World Health Organization: Suicide. 2021. https://www.who.int/news-room/fact-sheets/detail/suicide

World Health Organization: Preventing Suicide: A Global Imperative. Geneva, World Health Organization, 2014. https://apps.who.int/iris/rest/bitstreams/585331/retrieve

Substance Abuse and Mental Health Services Administration (SAMHSA), Center for Behavioral Health Statistics and Quality: National Survey on Drug Use and Health, 2021. https://www.samhsa.gov/data/release/2021-national-survey-drug-use-and-health-nsduh-releases

Centers for Disease Control and Prevention: CDC WONDER Online Database. 2023. https://wonder.cdc.gov/

Franklin JC, Ribeiro JD, Fox KR, et al.: Risk factors for suicidal thoughts and behaviors: a meta-analysis of 50 years of research. Psychol Bull 2017; 143:187–232 PubMed

Voracek M, Loibl LM: Genetics of suicide: a systematic review of twin studies. Wien Klin Wochenschr 2007; 119:463–475 PubMed

Mullins N, Kang J, Campos AI, et al.: Dissecting the shared genetic architecture of suicide attempt, psychiatric disorders, and known risk factors. Biol Psychiatry 2022; 91:313–327 PubMed PMC

Kimbrel NA, Ashley-Koch AE, Qin XJ, et al.: A genome-wide association study of suicide attempts in the Million Veterans Program identifies evidence of pan-ancestry and ancestry-specific risk loci. Mol Psychiatry 2022; 27:2264–2272 PubMed PMC

Gaziano JM, Concato J, Brophy M, et al.: Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J Clin Epidemiol 2016; 70:214–223 PubMed

Sullivan PF, Agrawal A, Bulik CM, et al.: Psychiatric genomics: an update and an agenda. Am J Psychiatry 2018; 175:15–27 PubMed PMC

Martin AR, Gignoux CR, Walters RK, et al.: Human demographic history impacts genetic risk prediction across diverse populations. Am J Hum Genet 2017; 100:635–649 PubMed PMC

Docherty AR, Shabalin AA, DiBlasi E, et al.: Genome-wide association study of suicide death and polygenic prediction of clinical antecedents. Am J Psychiatry 2020; 177:917–927 PubMed PMC

Hoffmire C, Stephens B, Morley S, et al.: VA Suicide Prevention Applications Network: a national health care system-based suicide event tracking system. Public Health Rep 2016; 131:816–821 PubMed PMC

Bulik-Sullivan BK, Loh PR, Finucane HK, et al.: LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015; 47:291–295 PubMed PMC

Fang H, Hui Q, Lynch J, et al.: Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am J Hum Genet 2019; 105:763–772 PubMed PMC

Chang CC, Chow CC, Tellier LC, et al.: Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 2015; 4:7. PubMed PMC

Willer CJ, Li Y, Abecasis GR: METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010; 26:2190–2191 PubMed PMC

Luo Y, Li X, Wang X, et al.: Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations. Hum Mol Genet 2021; 30:1521–1534 PubMed PMC

Karczewski KJ, Francioli LC, Tiao G, et al.: The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 2020; 581:434–443 PubMed PMC

Nock MK, Borges G, Bromet EJ, et al.: Cross-national prevalence and risk factors for suicidal ideation, plans and attempts. Br J Psychiatry 2008; 192:98–105 PubMed PMC

Brown BC, Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, Ye CJ, et al.: Transethnic genetic-correlation estimates from summary statistics. Am J Hum Genet 2016; 99:76–88 PubMed PMC

Campos AI, Verweij KJH, Statham DJ, et al.: Genetic aetiology of self-harm ideation and behaviour. Sci Rep 2020; 10:9713. PubMed PMC

Demontis D, Walters RK, Martin J, et al.: Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet 2019; 51:63–75 PubMed PMC

Grove J, Ripke S, Als TD, et al.: Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 2019; 51:431–444 PubMed PMC

Karlsson Linnér R, Biroli P, Kong E, et al.: Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet 2019; 51:245–257 PubMed PMC

Nagel M, Jansen PR, Stringer S, et al.: Meta-analysis of genomewide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways. Nat Genet 2018; 50:920–927 PubMed

Nievergelt CM, Maihofer AX, Klengel T, et al.: International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat Commun 2019; 10:4558. PubMed PMC

Stahl EA, Breen G, Forstner AJ, et al.: Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet 2019; 51:793–803 PubMed PMC

Yengo L, Sidorenko J, Kemper KE, et al.: Meta-analysis of genome-wide association studies for height and body mass index in ~700000 individuals of European ancestry. Hum Mol Genet 2018; 27:3641–3649 PubMed PMC

Johnston KJA, Adams MJ, Nicholl BI, et al.: Genome-wide association study of multisite chronic pain in UK Biobank. PLoS Genet 2019; 15:e1008164. PubMed PMC

Liu M, Jiang Y, Wedow R, et al.: Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 2019; 51:237–244 PubMed PMC

Howard DM, Adams MJ, Clarke TK, et al.: Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci 2019; 22:343–352 PubMed PMC

Jansen PR, Watanabe K, Stringer S, et al.: Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways. Nat Genet 2019; 51:394–403 PubMed

Zheng J, Erzurumluoglu AM, Elsworth BL, et al.: LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 2017; 33:272–279 PubMed PMC

Ahmedani BK, Peterson EL, Hu Y, et al.: Major physical health conditions and risk of suicide. Am J Prev Med 2017; 53:308–315 PubMed PMC

Fazel S, Runeson B: Suicide. N Engl J Med 2020; 382:266–274 PubMed PMC

Hübel C, Gaspar HA, Coleman JRI, et al.: Genomics of body fat percentage may contribute to sex bias in anorexia nervosa. Am J Med Genet B Neuropsychiatr Genet 2019; 180:428–438 PubMed PMC

Haworth S, Kho PF, Holgerson PL, et al.: Assessment and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis. Eur J Hum Genet 2021; 29:300–308 PubMed PMC

Zhu Z, Zheng Z, Zhang F, et al.: Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat Commun 2018; 9:224. PubMed PMC

Yang J, Lee SH, Goddard ME, et al.: GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 2011; 88:76–82 PubMed PMC

1000 Genomes Project Consortium, Auton A, Brooks LD, et al.: A global reference for human genetic variation. Nature 2015; 526:68–74 PubMed PMC

de Leeuw CA, Mooij JM, Heskes T, et al.: MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol 2015; 11:e1004219. PubMed PMC

Watanabe K, Taskesen E, van Bochoven A, et al.: Functional mapping and annotation of genetic associations with FUMA. Nat Commun 2017; 8:1826. PubMed PMC

GTEx Consortium, Laboratory, Data Analysis and Coordinating Center (LDACC)–Analysis Working Group, Statistical Methods groups–Analysis Working Group, et al.: Genetic effects on gene expression across human tissues. Nature 2017; 550:204–213 PubMed PMC

Gaspar HA, Breen G: Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach. Sci Rep 2017; 7:12460. PubMed PMC

Wagner AH, Coffman AC, Ainscough BJ, et al.: DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res 2016; 44:D1036–D1044 PubMed PMC

Roth BL, Lopez E, Patel S, et al.: The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches? Neuroscientist 2000; 6:252–262

Wu Y, Zeng J, Zhang F, et al.: Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 2018; 9:918. PubMed PMC

Zhu Z, Zhang F, Hu H, et al.: Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet 2016; 48:481–487 PubMed

de Klein N, Tsai EA, Vochteloo M, et al.: Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nat Genet 2023; 55:377–388 PubMed PMC

Ge T, Chen CY, Ni Y, et al.: Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun 2019; 10:1776. PubMed PMC

Sollis E, Mosaku A, Abid A, et al.: The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res 2023; 51:D977–D985 PubMed PMC

Ikeda M, Takahashi A, Kamatani Y, et al.: Genome-wide association study detected novel susceptibility genes for schizophrenia and shared trans-populations/diseases genetic effect. Schizophr Bull 2019; 45:824–834 PubMed PMC

Pfennig A, Kunzel HE, Kern N, et al.: Hypothalamus-pituitary-adrenal system regulation and suicidal behavior in depression. Biol Psychiatry 2005; 57:336–342 PubMed

Mann JJ, Rizk MM: A brain-centric model of suicidal behavior. Am J Psychiatry 2020; 177:902–916 PubMed PMC