Dataset Information

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

ABSTRACT:

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.

SUBMITTER: Docherty AR

PROVIDER: S-EPMC10603363 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Docherty Anna R AR Mullins Niamh N Ashley-Koch Allison E AE Qin Xuejun X Coleman Jonathan R I JRI Shabalin Andrey A Kang JooEun J Murnyak Balasz B Wendt Frank F Adams Mark M Campos Adrian I AI DiBlasi Emily E Fullerton Janice M JM Kranzler Henry R HR Bakian Amanda V AV Monson Eric T ET Rentería Miguel E ME Walss-Bass Consuelo C Andreassen Ole A OA Behera Chittaranjan C Bulik Cynthia M CM Edenberg Howard J HJ Kessler Ronald C RC Mann J John JJ Nurnberger John I JI Pistis Giorgio G Streit Fabian F Ursano Robert J RJ Polimanti Renato R Dennis Michelle M Garrett Melanie M Hair Lauren L Harvey Philip P Hauser Elizabeth R ER Hauser Michael A MA Huffman Jennifer J Jacobson Daniel D Madduri Ravi R McMahon Benjamin B Oslin David W DW Trafton Jodie J Awasthi Swapnil S Berrettini Wade H WH Bohus Martin M Chang Xiao X Chen Hsi-Chung HC Chen Wei J WJ Christensen Erik D ED Crow Scott S Duriez Philibert P Edwards Alexis C AC Fernández-Aranda Fernando F Galfalvy Hanga H Gandal Michael M Gorwood Philip P Guo Yiran Y Hafferty Jonathan D JD Hakonarson Hakon H Halmi Katherine A KA Hishimoto Akitoyo A Jain Sonia S Jamain Stéphane S Jiménez-Murcia Susana S Johnson Craig C Kaplan Allan S AS Kaye Walter H WH Keel Pamela K PK Kennedy James L JL Kim Minsoo M Klump Kelly L KL Levey Daniel F DF Li Dong D Liao Shih-Cheng SC Lieb Klaus K Lilenfeld Lisa L Marshall Christian R CR Mitchell James E JE Okazaki Satoshi S Otsuka Ikuo I Pinto Dalila D Powers Abigail A Ramoz Nicolas N Ripke Stephan S Roepke Stefan S Rozanov Vsevolod V Scherer Stephen W SW Schmahl Christian C Sokolowski Marcus M Starnawska Anna A Strober Michael M Su Mei-Hsin MH Thornton Laura M LM Treasure Janet J Ware Erin B EB Watson Hunna J HJ Witt Stephanie H SH Woodside D Blake DB Yilmaz Zeynep Z Zillich Lea L Adolfsson Rolf R Agartz Ingrid I Alda Martin M Alfredsson Lars L Appadurai Vivek V Artigas María Soler MS Van der Auwera Sandra S Azevedo M Helena MH Bass Nicholas N Bau Claiton H D CHD Baune Bernhard T BT Bellivier Frank F Berger Klaus K Biernacka Joanna M JM Bigdeli Tim B TB Binder Elisabeth B EB Boehnke Michael M Boks Marco P MP Braff David L DL Bryant Richard R Budde Monika M Byrne Enda M EM Cahn Wiepke W Castelao Enrique E Cervilla Jorge A JA Chaumette Boris B Corvin Aiden A Craddock Nicholas N Djurovic Srdjan S Foo Jerome C JC Forstner Andreas J AJ Frye Mark M Gatt Justine M JM Giegling Ina I Grabe Hans J HJ Green Melissa J MJ Grevet Eugenio H EH Grigoroiu-Serbanescu Maria M Gutierrez Blanca B Guzman-Parra Jose J Hamshere Marian L ML Hartmann Annette M AM Hauser Joanna J Heilmann-Heimbach Stefanie S Hoffmann Per P Ising Marcus M Jones Ian I Jones Lisa A LA Jonsson Lina L Kahn René S RS Kelsoe John R JR Kendler Kenneth S KS Kloiber Stefan S Koenen Karestan C KC Kogevinas Manolis M Krebs Marie-Odile MO Landén Mikael M Leboyer Marion M Lee Phil H PH Levinson Douglas F DF Liao Calwing C Lissowska Jolanta J Mayoral Fermin F McElroy Susan L SL McGrath Patrick P McGuffin Peter P McQuillin Andrew A Mehta Divya D Melle Ingrid I Mitchell Philip B PB Molina Esther E Morken Gunnar G Nievergelt Caroline C Nöthen Markus M MM O'Donovan Michael C MC Ophoff Roel A RA Owen Michael J MJ Pato Carlos C Pato Michele T MT Penninx Brenda W J H BWJH Potash James B JB Power Robert A RA Preisig Martin M Quested Digby D Ramos-Quiroga Josep Antoni JA Reif Andreas A Ribasés Marta M Richarte Vanesa V Rietschel Marcella M Rivera Margarita M Roberts Andrea A Roberts Gloria G Rouleau Guy A GA Rovaris Diego L DL Sanders Alan R AR Schofield Peter R PR Schulze Thomas G TG Scott Laura J LJ Serretti Alessandro A Shi Jianxin J Sirignano Lea L Sklar Pamela P Smeland Olav B OB Smoller Jordan W JW Sonuga-Barke Edmund J S EJS Trzaskowski Maciej M Tsuang Ming T MT Turecki Gustavo G Vilar-Ribó Laura L Vincent John B JB Völzke Henry H Walters James T R JTR Weickert Cynthia Shannon CS Weickert Thomas W TW Weissman Myrna M MM Williams Leanne M LM Wray Naomi R NR Zai Clement C CC Agerbo Esben E Børglum Anders D AD Breen Gerome G Demontis Ditte D Erlangsen Annette A Gelernter Joel J Glatt Stephen J SJ Hougaard David M DM Hwu Hai-Gwo HG Kuo Po-Hsiu PH Lewis Cathryn M CM Li Qingqin S QS Liu Chih-Min CM Martin Nicholas G NG McIntosh Andrew M AM Medland Sarah E SE Mors Ole O Nordentoft Merete M Olsen Catherine M CM Porteous David D Smith Daniel J DJ Stahl Eli A EA Stein Murray B MB Wasserman Danuta D Werge Thomas T Whiteman David C DC Willour Virginia V Coon Hilary H Beckham Jean C JC Kimbrel Nathan A NA Ruderfer Douglas M DM

The American journal of psychiatry 20231001 10

<h4>Objective</h4>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 ...[more]

PMID: 37777856

Similar Datasets

Project description:Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10-4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10-5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34-0.81) as well as several psychiatric disorders (rg = 0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Project description:Suicidal behavior (SB) has a complex etiology involving genes and environment. One of the genetic components in SB could be copy number variations (CNVs), as CNVs are implicated in neurodevelopmental disorders. However, a recently published genome-wide and case-control study did not observe any significant role of CNVs in SB. Here we complemented these initial observations by instead using a family-based trio-sample that is robust to control biases, having severe suicide attempt (SA) in offspring as main outcome (n = 660 trios). We first tested for CNV associations on the genome-wide Illumina 1M SNP-array by using FBAT-CNV methodology, which allows for evaluating CNVs without reliance on CNV calling algorithms, analogous to a common SNP-based GWAS. We observed association of certain T-cell receptor markers, but this likely reflected inter-individual variation in somatic rearrangements rather than association with SA outcome. Next, we used the PennCNV software to call 385 putative rare (<1%) and large (>100 kb) CNVs, observed in n = 225 SA offspring. Nine SA offspring had rare CNV calls in a set of previously schizophrenia-associated loci, indicating the importance of such CNVs in certain SA subjects. Several additional, very large (>1MB) sized CNV calls in 15 other SA offspring also spanned pathogenic regions or other neural genes of interest. Overall, 45 SA had CNVs enriched for 65 medically relevant genes previously shown to be affected by CNVs, which were characterized by a neurodevelopmental biology. A neurodevelopmental implication was partly congruent with our previous SNP-based GWAS, but follow-up analysis here indicated that carriers of rare CNVs had a decreased burden of common SNP risk-alleles compared to non-carriers. In conclusion, while CNVs did not show genome-wide association by the FBAT-CNV methodology, our preliminary observations indicate rare pathogenic CNVs affecting neurodevelopmental functions in a subset of SA, who were distinct from SA having increased SNP risk-allele burden. These observations may open up new avenues in the genetic etiology of SB.

Project description:ObjectiveMore than 90% of people who attempt suicide have a psychiatric diagnosis; however, twin and family studies suggest that the genetic etiology of suicide attempt is partially distinct from that of the psychiatric disorders themselves. The authors present the largest genome-wide association study (GWAS) on suicide attempt, using cohorts of individuals with major depressive disorder, bipolar disorder, and schizophrenia from the Psychiatric Genomics Consortium.MethodsThe samples comprised 1,622 suicide attempters and 8,786 nonattempters with major depressive disorder; 3,264 attempters and 5,500 nonattempters with bipolar disorder; and 1,683 attempters and 2,946 nonattempters with schizophrenia. A GWAS on suicide attempt was performed by comparing attempters to nonattempters with each disorder, followed by a meta-analysis across disorders. Polygenic risk scoring was used to investigate the genetic relationship between suicide attempt and the psychiatric disorders.ResultsThree genome-wide significant loci for suicide attempt were found: one associated with suicide attempt in major depressive disorder, one associated with suicide attempt in bipolar disorder, and one in the meta-analysis of suicide attempt in mood disorders. These associations were not replicated in independent mood disorder cohorts from the UK Biobank and iPSYCH. No significant associations were found in the meta-analysis of all three disorders. Polygenic risk scores for major depression were significantly associated with suicide attempt in major depressive disorder (R2=0.25%), bipolar disorder (R2=0.24%), and schizophrenia (R2=0.40%).ConclusionsThis study provides new information on genetic associations and demonstrates that genetic liability for major depression increases risk for suicide attempt across psychiatric disorders. Further collaborative efforts to increase sample size may help to robustly identify genetic associations and provide biological insights into the etiology of suicide attempt.

Dataset Information

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Objective

Methods

Results

Conclusions

Publications

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets