Significant outcomes
Suicidality, subcortical brain volume and Intracranial volume of individuals of European ancestry shared the same genetic common factor. Additionally, there is a positive genetic correlation between Suicide from FinnGen and Intracranial brain volume. Gene Ontology analyses, pathways and biological processes encompassing these phenotypes highlight shared mechanisms related to an inflammatory signature detectable in both blood and brain tissues.
Limitations
This study solely focuses on individuals of European ancestry. Future studies should focus on including diverse ancestries for a better generalization. Additionally, this study had a limited sample size, which might have resulted in the inability to detect signals, improve genetic correlation, and detect shared biological mechanisms.
Highlights
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Genomic Structural Equation Modeling analyses reveals a common latent factor divided into two distinct groups of phenotypes. The first group includes the suicidal traits from the UK Biobank (Ever Self Harm, Suicide Attempts, and Thought of Life Not Worth Living), while the second group includes Suicide from FinnGen, ICV, Accumbens, Caudate, Hippocampus, Pallidum, Thalamus, and Putamen from ENIGMA.
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Genome-wide association studies (GWAS) meta-analysis within the first group identifies 34 novel significant variants, mostly located within the DCC, STIM2, MEAF6, and RSPO1 genes; 50 significant hub genes mostly expressed in brains tissues which include brain anterior cingulate cortex, cultured fibroblast cells, brain hippocampus, brain putamen basal ganglia, and brain substantia nigra.
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GWAS meta-analysis within the second group identifies 64 SNPs showing potential pleiotropic effects; 22 significant hub genes with top enriched tissues located in the heart and left ventricle, pancreas, putamen basal ganglia, substantia nigra, and hippocampus.
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The resulting network combining the hub genes from the two groups exhibit significant pathways connected to inflammatory signature detectable in both blood and brain tissues.
Introduction
Suicidality has become an increasingly critical issue within public health, claiming approximately 700,000 lives globally each year and maintaining a suicide rate of 9.0 per 100,000 individuals worldwide (WHO, 2021). In the United States, it was the 12th leading cause of death from 2010 to 2018 (Hedegaard et al., Reference Hedegaard, Curtin and Warner2020). The economic impact of suicidal behaviour is profound, with an estimated cost of $70 billion annually in the U.S. alone (CDC, 2020). Research has highlighted the genetic basis of suicidality, complementing the roles of environmental and individual factors (Strawbridge et al., Reference Strawbridge, Ward, Ferguson, Graham, Shaw, Cullen, Pearsall, Lyall, Johnston, Niedzwiedz, Pell, Mackay, Martin, Lyall, Bailey and Smith2019; Li et al., Reference Li, Shabalin, DiBlasi, Gopal, Canuso, Drevets, Docherty and Coon2023). For instance, monozygotic twins demonstrate a significantly higher likelihood of suicide attempts and completions compared to dizygotic twins (Li et al., Reference Li, Shabalin, DiBlasi, Gopal, Canuso, Drevets, Docherty and Coon2023). Genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP) heritability of 3.5% in the UK Biobank and 6.8% in the International Suicide Genetics Consortium meta-analysis (Mullins et al., Reference Mullins, Perroud, Uher, Butler, Cohen‐Woods, Rivera, Malki, Euesden, Power, Tansey, Jones, Jones, Craddock, Owen, Korszun, Gill, Mors, Preisig, Maier, Rietschel, Rice, Müller‐Myhsok, Binder, Lucae, Ising, Craig, Farmer, McGuffin, Breen and Lewis2014; Reference Mullins, Kang, Campos, Coleman, Edwards, Galfalvy, Levey, Lori, Shabalin, Starnawska, Su, Watson, Adams, Awasthi, Gandal, Hafferty, Hishimoto, Kim, Okazaki, Otsuka, Ripke, Ware, Bergen, Berrettini, Bohus, Brandt, Chang, Chen, Chen, Crawford, Crow, DiBlasi, Duriez, Fernández-Aranda, Fichter, Gallinger, Glatt, Gorwood, Guo, Hakonarson, Halmi, Hwu, Jain, Jamain, Jiménez-Murcia, Johnson, Kaplan, Kaye, Keel, Kennedy, Klump, Li, Liao, Lieb, Lilenfeld, Liu, Magistretti, Marshall, Mitchell, Monson, Myers, Pinto, Powers, Ramoz, Roepke, Rozanov, Scherer, Schmahl, Sokolowski, Strober, Thornton, Treasure, Tsuang, Witt, Woodside, Yilmaz, Zillich, Adolfsson, Agartz, Air, Alda, Alfredsson, Andreassen, Anjorin, Appadurai, Soler Artigas, Van der Auwera, Azevedo, Bass, Bau, Baune, Bellivier, Berger, Biernacka, Bigdeli, Binder, Boehnke, Boks, Bosch, Braff, Bryant, Budde, Byrne, Cahn, Casas, Castelao, Cervilla, Chaumette, Cichon, Corvin, Craddock, Craig, Degenhardt, Djurovic, Edenberg, Fanous, Foo, Forstner, Frye, Fullerton, Gatt, Gejman, Giegling, Grabe, Green, Grevet, Grigoroiu-Serbanescu, Gutierrez, Guzman-Parra, Hamilton, Hamshere, Hartmann, Hauser, Heilmann-Heimbach, Hoffmann, Ising, Jones, Jones, Jonsson, Kahn, Kelsoe, Kendler, Kloiber, Koenen, Kogevinas, Konte, Krebs, Landén, Lawrence, Leboyer, Lee, Levinson, Liao, Lissowska, Lucae, Mayoral, McElroy, McGrath, McGuffin, McQuillin, Medland, Mehta, Melle, Milaneschi, Mitchell, Molina, Morken, Mortensen, Müller-Myhsok, Nievergelt, Nimgaonkar, Nöthen, O’Donovan, Ophoff, Owen, Pato, Pato, Penninx, Pimm, Pistis, Potash, Power, Preisig, Quested, Ramos-Quiroga, Reif, Ribasés, Richarte, Rietschel, Rivera, Roberts, Roberts, Rouleau, Rovaris, Rujescu, Sánchez-Mora, Sanders, Schofield, Schulze, Scott, Serretti, Shi, Shyn, Sirignano, Sklar, Smeland, Smoller, Sonuga-Barke, Spalletta, Strauss, Świątkowska, Trzaskowski, Turecki, Vilar-Ribó, Vincent, Völzke, Walters, Shannon Weickert, Weickert, Weissman, Williams, Wray, Zai, Ashley-Koch, Beckham, Hauser, Hauser, Kimbrel, Lindquist, McMahon, Oslin, Qin, Agerbo, Børglum, Breen, Erlangsen, Esko, Gelernter, Hougaard, Kessler, Kranzler, Li, Martin, McIntosh, Mors, Nordentoft, Olsen, Porteous, Ursano, Wasserman, Werge, Whiteman, Bulik, Coon, Demontis, Docherty, Kuo, Lewis, Mann, Rentería, Smith, Stahl, Stein, Streit, Willour, Ruderfer, Wray, Ripke, Mattheisen, Trzaskowski, Byrne, Abdellaoui, Adams, Agerbo, Air, Andlauer, Bacanu, Bækvad-Hansen, Beekman, Bigdeli, Binder, Bryois, Buttenschøn, Bybjerg-Grauholm, Cai, Castelao, Christensen, Clarke, Coleman, Colodro-Conde, Couvy-Duchesne, Craddock, Crawford, Davies, Degenhardt, Derks, Direk, Dolan, Dunn, Eley, Escott-Price, Hassan Kiadeh, Finucane, Foo, Forstner, Frank, Gaspar, Gill, Goes, Gordon, Weinsheimer, Wellmann, Willemsen, Witt, Wu, Xi, Yang, Zhang, Arolt, Baune, Berger, Boomsma, Cichon, Dannlowski, de Geus, Depaulo, Domenici, Domschke, Esko, Grabe, Hamilton, Grove, Hall, Hansen, Hansen, Herms, Hickie, Hoffmann, Homuth, Horn, Hottenga, Hougaard, Howard, Ising, Jansen, Jones, Jones, Jorgenson, Knowles, Kohane, Kraft, Kretzschmar, Kutalik, Li, Lind, MacIntyre, MacKinnon, Maier, Maier, Marchini, Mbarek, McGrath, McGuffin, Medland, Mehta, Middeldorp, Mihailov, Milaneschi, Milani, Mondimore, Montgomery, Mostafavi, Mullins, Nauck, Ng, Nivard, Nyholt, O’Reilly, Oskarsson, Hayward, Heath, Kendler, Kloiber, Lewis, Li, Lucae, Madden, Magnusson, Martin, McIntosh, Metspalu, Mors, Mortensen, Müller-Myhsok, Nordentoft, Nöthen, O’Donovan, Paciga, Pedersen, Owen, Painter, Pedersen, Pedersen, Peterson, Peyrot, Pistis, Posthuma, Quiroz, Qvist, Rice, Riley, Rivera, Mirza, Schoevers, Schulte, Shen, Shi, Shyn, Sigurdsson, Sinnamon, Smit, Smith, Stefansson, Steinberg, Streit, Strohmaier, Tansey, Teismann, Teumer, Thompson, Thomson, Thorgeirsson, Traylor, Treutlein, Trubetskoy, Uitterlinden, Umbricht, der Auwera, van Hemert, Viktorin, Visscher, Wang, Webb, Penninx, Perlis, Porteous, Potash, Preisig, Rietschel, Schaefer, Schulze, Smoller, Stefansson, Tiemeier, Uher, Völzke, Weissman, Werge, Lewis, Levinson, Breen, Børglum, Sullivan, Mullins, Forstner, O’Connell, Coombes, Coleman, Qiao, Als, Bigdeli, Børte, Bryois, Charney, Drange, Gandal, Hagenaars, Ikeda, Kamitaki, Kim, Krebs, Panagiotaropoulou, Schilder, Sloofman, Steinberg, Trubetskoy, Winsvold, Won, Abramova, Adorjan, Agerbo, Al Eissa, Albani, Alliey-Rodriguez, Anjorin, Antilla, Antoniou, Awasthi, Baek, Bækvad-Hansen, Bass, Bauer, Beins, Bergen, Birner, Pedersen, Bøen, Boks, Bosch, Brum, Brumpton, Brunkhorst-Kanaan, Budde, Bybjerg-Grauholm, Byerley, Cairns, Casas, Cervantes, Clarke, Cruceanu, Cuellar-Barboza, Cunningham, Curtis, Czerski, Dale, Dalkner, David, Degenhardt, Djurovic, Dobbyn, Douzenis, Elvsåshagen, Escott-Price, Ferrier, Fiorentino, Foroud, Forty, Frank, Frei, Freimer, Frisén, Gade, Garnham, Gelernter, Pedersen, Gizer, Gordon, Gordon-Smith, Greenwood, Grove, Guzman-Parra, Ha, Haraldsson, Hautzinger, Heilbronner, Hellgren, Herms, Hoffmann, Holmans, Huckins, Jamain, Johnson, Kalman, Kamatani, Kennedy, Kittel-Schneider, Knowles, Kogevinas, Koromina, Kranz, Kranzler, Kubo, Kupka, Kushner, Lavebratt, Lawrence, Leber, Lee, Lee, Levy, Lewis, Liao, Lucae, Lundberg, MacIntyre, Magnusson, Maier, Maihofer, Malaspina, Maratou, Martinsson, Mattheisen, McGregor, McGuffin, McKay, Medeiros, Medland, Millischer, Montgomery, Moran, Morris, Mühleisen, O’Brien, O’Donovan, Olde Loohuis, Oruc, Papiol, Pardiñas, Perry, Pfennig, Porichi, Potash, Quested, Raj, Rapaport, DePaulo, Regeer, Rice, Rivas, Rivera, Roth, Roussos, Ruderfer, Sánchez-Mora, Schulte, Senner, Sharp, Shilling, Sigurdsson, Sirignano, Slaney, Smeland, Smith, Sobell, Hansen, Artigas, Spijker, Stein, Strauss, Świątkowska, Terao, Thorgeirsson, Toma, Tooney, Tsermpini, Vawter, Vedder, Walters, Witt, Xi, Xu, Kay Yang, Young, Young, Zandi, Zhou, Zillich, Adolfsson, Agartz, Alda, Alfredsson, Babadjanova, Backlund, Baune, Bellivier, Bengesser, Berrettini, Blackwood, Boehnke, Børglum, Breen, Carr, Catts, Corvin, Craddock, Dannlowski, Dikeos, Esko, Etain, Ferentinos, Frye, Fullerton, Gawlik, Gershon, Goes, Green, Grigoroiu-Serbanescu, Hauser, Henskens, Hillert, Hong, Hougaard, Hultman, Hveem, Iwata, Jablensky, Jones, Jones, Kahn, Kelsoe, Kirov, Landén, Leboyer, Lewis, Li, Lissowska, Lochner, Loughland, Martin, Mathews, Mayoral, McElroy, McIntosh, McMahon, Melle, Michie, Milani, Mitchell, Morken, Mors, Mortensen, Mowry, Müller-Myhsok, Myers, Neale, Nievergelt, Nordentoft, Nöthen, O’Donovan, Oedegaard, Olsson, Owen, Paciga, Pantelis, Pato, Pato, Patrinos, Perlis, Posthuma, Ramos-Quiroga, Reif, Reininghaus, Ribasés, Rietschel, Ripke, Rouleau, Saito, Schall, Schalling, Schofield, Schulze, Scott, Scott, Serretti, Weickert, Smoller, Stefansson, Stefansson, Stordal, Streit, Sullivan, Turecki, Vaaler, Vieta, Vincent, Waldman, Weickert, Werge, Wray, Zwart, Biernacka, Nurnberger, Cichon, Edenberg, Stahl, McQuillin, Di Florio, Ophoff, Andreassen, Adan, Alfredsson, Ando, Andreassen, Aschauer, Baker, Bencko, Bergen, Berrettini, Birgegård, Boden, Boehm, Boni, Perica, Brandt, Breen, Bryois, Buehren, Bulik, Burghardt, Carlberg, Cassina, Cichon, Clementi, Coleman, Cone, Courtet, Crawford, Crow, Crowley, Danner, Davis, de Zwaan, Dedoussis, Degortes, DeSocio, Dick, Dikeos, Dina, Dmitrzak-Weglarz, Martinez, Duncan, Egberts, Marshall, Martin, Mattheisen, Mattingsdal, McDevitt, McGuffin, Medland, Metspalu, Meulenbelt, Micali, Mitchell, Mitchell, Monteleone, Monteleone, Montgomery, Mortensen, Munn-Chernoff, Nacmias, Navratilova, Ntalla, Olsen, Ophoff, O’Toole, Padyukov, Palotie, Pantel, Papezova, Parker, Pearson, Pedersen, Ehrlich, Escaramís, Esko, Espeseth, Estivill, Farmer, Favaro, Fernández-Aranda, Fichter, Fischer, Floyd, Föcker, Foretova, Forstner, Forzan, Franklin, Gallinger, Gambaro, Gaspar, Giegling, Giuranna, Giusti-Rodríquez, Gonidakis, Gordon, Gorwood, Mayora, Grove, Guillaume, Guo, Hakonarson, Halmi, Hanscombe, Hatzikotoulas, Hauser, Hebebrand, Helder, Henders, Herms, Herpertz-Dahlmann, Herzog, Hinney, Horwood, Hübel, Petersen, Pinto, Purves, Raevuori, Ramoz, Reichborn-Kjennerud, Ricca, Ripatti, Ripke, Ritschel, Roberts, Rujescu, Rybakowski, Santonastaso, Scherag, Scherer, Schmidt, Schork, Schosser, Seitz, Slachtova, Slagboom, Slof-Op ‘t Landt, Slopien, Soranzo, Sorbi, Southam, Steen, Strober, Huckins, Hudson, Imgart, Inoko, Janout, Jiménez-Murcia, Johnson, Jordan, Julià, Kalsi, Kaminská, Kaplan, Kaprio, Karhunen, Karwautz, Kas, Kaye, Kennedy, Kennedy, Keski-Rahkonen, Kiezebrink, Kim, Kirk, Klareskog, Klump, Knudsen, Landén, Larsen, Le Hellard, Leppä, Li, Lichtenstein, Lilenfeld, Lin, Lissowska, Lundervold, Luykx, Magistretti, Maj, Mannik, Marsal, Stuber, Sullivan, Świątkowska, Szatkiewicz, Tachmazidou, Tenconi, Thornton, Tortorella, Tozzi, Treasure, Tsitsika, Tyszkiewicz-Nwafor, Tziouvas, van Elburg, van Furth, Wade, Wagner, Walton, Watson, Werge, Whiteman, Wichmann, Widen, Woodside, Yao, Yilmaz, Zeggini, Zerwas, Zipfel, Witt, Streit, Jungkunz, Frank, Awasthi, Treutlein, Dietl, Schwarze, Dahmen, Schott, Nöthen, Ripke, Mobascher, Rujescu, Lieb, Roepke, Schmahl, Bohus, Rietschel, Crivelli, Dennis, Harvey, Carter, Huffman, Jacobson, Madduri, Olsen, Pestian, Gaziano, Muralidhar, Ramoni, Beckham, Chang, O’Donnell, Tsao, Breeling, Huang, Romero, Muralidhar, Moser, Whitbourne, Brewer, Aslan, Connor, Argyres, Tsao, Gaziano, Stephens, Brophy, Humphries, Selva, Do, Shayan, Cho, O’Donnell, O’Donnell, Pyarajan, Tsao, Cho, Pyarajan, Hauser, Sun, Zhao, Wilson, McArdle, Dellitalia, Mattocks, Harley, Zablocki, Whittle, Jacono, Beckham, Gutierrez, Gibson, Hammer, Kaminsky, Villareal, Kinlay, Xu, Hamner, Mathew, Bhushan, Iruvanti, Godschalk, Ballas, Ivins, Mastorides, Moorman, Gappy, Klein, Ratcliffe, Florez, Okusaga, Murdoch, Sriram, Yeh, Tandon, Jhala, Aguayo, Cohen, Sharma, Liangpunsakul, Oursler, Whooley, Ahuja, Constans, Meyer, Greco, Rauchman, Servatius, Gaddy, Wallbom, Morgan, Stapley, Sherman, Ross, Tsao, Strollo, Boyko, Meyer, Gupta, Huq, Fayad, Hung, Lichy, Hurley, Robey and Striker2022).
Neuroimaging studies have indicated associations between changes in subcortical structures and suicidality risk (Campos et al., Reference Campos, Thompson, Veltman, Pozzi, van Veltzen, Jahanshad, Adams, Baune, Berger, Brosch, Bülow, Connolly, Dannlowski, Davey, de Zubicaray, Dima, Erwin-Grabner, Evans, Fu, Gotlib, Goya-Maldonado, Grabe, Grotegerd, Harris, Harrison, Hatton, Hermesdorf, Hickie, Ho, Kircher, Krug, Lagopoulos, Lemke, McMahon, MacMaster, Martin, McIntosh, Medland, Meinert, Meller, Nenadic, Opel, Redlich, Reneman, Repple, Sacchet, Schmitt, Schrantee, Sim, Singh, Stein, Strike, van der Wee, van der Werff, Völzke, Waltemate, Whalley, Wittfeld, Wright, Yang, Zarate, Schmaal and Rentería2021; Kim et al., Reference Kim, Bartlett, DeLorenzo, Parsey, Kilts and Cáceda2021; Yin et al., Reference Yin, Tong, Huang, Tian, Chen, Tan, Wang, Yang, Tong, Fan, Kochunov, Jahanshad, Li, Hong and Tan2022). These findings align with the brain-centric diathesis-stress model of suicidal behaviour (Mann et al., Reference Mann and Rizk2020), suggesting brain changes contribute to suicide risk. However, conflicting reports exist, with some studies finding no significant association between suicidality and subcortical brain volume (Rentería et al., Reference Rentería, Schmaal, Hibar, Couvy-Duchesne, Strike, Mills, de Zubicaray, McMahon, Medland, Gillespie, Hatton, Lagopoulos, Veltman, van der Wee, van Erp, Wittfeld, Grabe, Block, Hegenscheid, Völzke, Veer, Walter, Schnell, Schramm, Normann, Schoepf, Konrad, Zurowski, Godlewska, Cowen, Penninx, Jahanshad, Thompson, Wright, Martin, Christensen and Hickie2017). For instance, the ENIGMA-MDD consortium found no significant differences in subcortical regions among individuals with or without suicidal ideation or behaviour. Another study involving adolescents with major depressive disorder did not find a link between suicide attempts and subcortical alterations (Gifuni et al., Reference Gifuni, Chakravarty, Lepage, Ho, Geoffroy, Lacourse, Gotlib, Turecki, Renaud and Jollant2021). Such discrepancies may stem from sample heterogeneity and the acute nature of suicidal behaviour.
Similarly, brain volume has been shown to possess a heritable component (Blokland et al., Reference Blokland, de Zubicaray, McMahon and Wright2012). Twin studies have revealed genetic influences on both overall brain and subcortical volumes (Tramo et al., Reference Tramo, Loftus, Stukel, Green, Weaver and Gazzaniga1998; Pfefferbaum et al., Reference Pfefferbaum, Sullivan, Swan and Carmelli2000). Notably, GWAS have identified five genetic variants associated with the sizes of the putamen and caudate nucleus among seven subcortical brain regions (Hibar et al., Reference Hibar, Stein, Renteria, Arias-Vasquez, Desrivières, Jahanshad, Toro, Wittfeld, Abramovic, Andersson, Aribisala, Armstrong, Bernard, Bohlken, Boks, Bralten, Brown, Mallar Chakravarty, Chen, Ching, Cuellar-Partida, den Braber, Giddaluru, Goldman, Grimm, Guadalupe, Hass, Woldehawariat, Holmes, Hoogman, Janowitz, Jia, Kim, Klein, Kraemer, Lee, Olde Loohuis, Luciano, Macare, Mather, Mattheisen, Milaneschi, Nho, Papmeyer, Ramasamy, Risacher, Roiz-Santiañez, Rose, Salami, Sämann, Schmaal, Schork, Shin, Strike, Teumer, van Donkelaar, van Eijk, Walters, Westlye, Whelan, Winkler, Zwiers, Alhusaini, Athanasiu, Ehrlich, Hakobjan, Hartberg, Haukvik, Heister, Hoehn, Kasperaviciute, Liewald, Lopez, Makkinje, Matarin, Naber, Reese McKay, Needham, Nugent, Pütz, Royle, Shen, Sprooten, Trabzuni, van der Marel, van Hulzen, Walton, Wolf, Almasy, Ames, Arepalli, Assareh, Bastin, Brodaty, Bulayeva, Carless, Cichon, Corvin, Curran, Czisch, de Zubicaray, Dillman, Duggirala, Dyer, Erk, Fedko, Ferrucci, Foroud, Fox, Fukunaga, Raphael Gibbs, Göring, Green, Guelfi, Hansell, Hartman, Hegenscheid, Heinz, Hernandez, Heslenfeld, Hoekstra, Holsboer, Homuth, Hottenga, Ikeda, Jack, Jenkinson, Johnson, Kanai, Keil, Kent, Kochunov, Kwok, Lawrie, Liu, Longo, McMahon, Meisenzahl, Melle, Mohnke, Montgomery, Mostert, Mühleisen, Nalls, Nichols, Nilsson, Nöthen, Ohi, Olvera, Perez-Iglesias, Bruce Pike, Potkin, Reinvang, Reppermund, Rietschel, Romanczuk-Seiferth, Rosen, Rujescu, Schnell, Schofield, Smith, Steen, Sussmann, Thalamuthu, Toga, Traynor, Troncoso, Turner, Valdés Hernández, van ‘t Ent, van der Brug, van der Wee, van Tol, Veltman, Wassink, Westman, Zielke, Zonderman, Ashbrook, Hager, Lu, McMahon, Morris, Williams, Brunner, Buckner, Buitelaar, Cahn, Calhoun, Cavalleri, Crespo-Facorro, Dale, Davies, Delanty, Depondt, Djurovic, Drevets, Espeseth, Gollub, Ho, Hoffmann, Hosten, Kahn, Le Hellard, Meyer-Lindenberg, Müller-Myhsok, Nauck, Nyberg, Pandolfo, Penninx, Roffman, Sisodiya, Smoller, van Bokhoven, van Haren, Völzke, Walter, Weiner, Wen, White, Agartz, Andreassen, Blangero, Boomsma, Brouwer, Cannon, Cookson, de Geus, Deary, Donohoe, Fernández, Fisher, Francks, Glahn, Grabe, Gruber, Hardy, Hashimoto, Hulshoff Pol, Jönsson, Kloszewska, Lovestone, Mattay, Mecocci, McDonald, McIntosh, Ophoff, Paus, Pausova, Ryten, Sachdev, Saykin, Simmons, Singleton, Soininen, Wardlaw, Weale, Weinberger, Adams, Launer, Seiler, Schmidt, Chauhan, Satizabal, Becker, Yanek, van der Lee, Ebling, Fischl, Longstreth, Greve, Schmidt, Nyquist, Vinke, van Duijn, Xue, Mazoyer, Bis, Gudnason, Seshadri, Ikram, Martin, Wright, Schumann, Franke, Thompson and Medland2015). More recent GWAS have discovered numerous genetic variants linked to brain morphometry (Satizabal et al., Reference Satizabal, Adams, Hibar, White, Knol, Stein, Scholz, Sargurupremraj, Jahanshad, Roshchupkin, Smith, Bis, Jian, Luciano, Hofer, Teumer, van der Lee, Yang, Yanek, Lee, Li, Hu, Koh, Eicher, Desrivières, Arias-Vasquez, Chauhan, Athanasiu, Rentería, Kim, Hoehn, Armstrong, Chen, Holmes, den Braber, Kloszewska, Andersson, Espeseth, Grimm, Abramovic, Alhusaini, Milaneschi, Papmeyer, Axelsson, Ehrlich, Roiz-Santiañez, Kraemer, Håberg, Jones, Pike, Stein, Stevens, Bralten, Vernooij, Harris, Filippi, Witte, Guadalupe, Wittfeld, Mosley, Becker, Doan, Hagenaars, Saba, Cuellar-Partida, Amin, Hilal, Nho, Mirza-Schreiber, Arfanakis, Becker, Ames, Goldman, Lee, Boomsma, Lovestone, Giddaluru, Le Hellard, Mattheisen, Bohlken, Kasperaviciute, Schmaal, Lawrie, Agartz, Walton, Tordesillas-Gutierrez, Davies, Shin, Ipser, Vinke, Hoogman, Jia, Burkhardt, Klein, Crivello, Janowitz, Carmichael, Haukvik, Aribisala, Schmidt, Strike, Cheng, Risacher, Pütz, Fleischman, Assareh, Mattay, Buckner, Mecocci, Dale, Cichon, Boks, Matarin, Penninx, Calhoun, Chakravarty, Marquand, Macare, Kharabian Masouleh, Oosterlaan, Amouyel, Hegenscheid, Rotter, Schork, Liewald, de Zubicaray, Wong, Shen, Sämann, Brodaty, Roffman, de Geus, Tsolaki, Erk, van Eijk, Cavalleri, van der Wee, McIntosh, Gollub, Bulayeva, Bernard, Richards, Himali, Loeffler, Rommelse, Hoffmann, Westlye, Valdés Hernández, Hansell, van Erp, Wolf, Kwok, Vellas, Heinz, Olde Loohuis, Delanty, Ho, Ching, Shumskaya, Singh, Hofman, van der Meer, Homuth, Psaty, Bastin, Montgomery, Foroud, Reppermund, Hottenga, Simmons, Meyer-Lindenberg, Cahn, Whelan, van Donkelaar, Yang, Hosten, Green, Thalamuthu, Mohnke, Hulshoff Pol, Lin, Jack, Schofield, Mühleisen, Maillard, Potkin, Wen, Fletcher, Toga, Gruber, Huentelman, Davey Smith, Launer, Nyberg, Jönsson, Crespo-Facorro, Koen, Greve, Uitterlinden, Weinberger, Steen, Fedko, Groenewold, Niessen, Toro, Tzourio, Longstreth, Ikram, Smoller, van Tol, Sussmann, Paus, Lemaître, Schroeter, Mazoyer, Andreassen, Holsboer, Depondt, Veltman, Turner, Pausova, Schumann, van Rooij, Djurovic, Deary, McMahon, Müller-Myhsok, Brouwer, Soininen, Pandolfo, Wassink, Cheung, Wolfers, Martinot, Zwiers, Nauck, Melle, Martin, Kanai, Westman, Kahn, Sisodiya, White, Saremi, van Bokhoven, Brunner, Völzke, Wright, van ‘t Ent, Nöthen, Ophoff, Buitelaar, Fernández, Sachdev, Rietschel, van Haren, Fisher, Beiser, Francks, Saykin, Mather, Romanczuk-Seiferth, Hartman, DeStefano, Heslenfeld, Weiner, Walter, Hoekstra, Nyquist, Franke, Bennett, Grabe, Johnson, Chen, van Duijn, Lopez, Fornage, Wardlaw, Schmidt, DeCarli, De Jager, Villringer, Debette, Gudnason, Medland, Shulman, Thompson, Seshadri and Ikram2019). Despite these insights, the extent of shared genetic loci between suicidality and subcortical brain volume remains underexplored, and the common underlying features are not fully understood. Moreover, the genetic overlaps at the polygenic level are still inadequately comprehended. Genetic investigations may provide a clearer understanding of the overlapping psychopathology between suicidality and brain volume than imaging studies alone. In this study, we hypothesised that there may be a shared genetic aetiology underlying suicidality and altered subcortical brain volumes from a genome-wide perspective.
Recent research has proposed the existence of a genetic ‘p factor’, indicating shared genetic variance across various disorders, particularly psychiatric symptoms (Caspi et al., Reference Caspi, Houts, Belsky, Goldman-Mellor, Harrington, Israel, Meier, Ramrakha, Shalev, Poulton and Moffitt2014); Sprooten et al., Reference Sprooten, Franke and Greven2022). This conceptualisation suggests shared components in the underlying pathophysiology of mental disorders, potentially explaining their comorbidity. Utilizing large-scale GWAS datasets on suicidality and subcortical brain volume, this study aims to elucidate the shared genetic architecture between these phenotypes. We introduce a common factor model extending the genomic ‘p factor’ to include suicidality and subcortical brain volume through Genomic Structural Equation Modelling (Genomic Structural Equation Modeling (SEM)). We conducted variant-based and gene/pathway-specific GWAS meta-analyses to identify loci significantly associated with this common factor. Furthermore, we sought to uncover cross-disorder risk loci between subcortical brain volume and suicidality using our common factor-informed approach, aiming to elucidate shared molecular mechanisms.
Materials and methods
GWAS summary data
We acquired Genome-Wide Association Study (GWAS) summary statistics pertaining to Suicide or other intentional self-harm (SUIC) from the FinnGen Biobank (https://www.finngen.fi/en/access_results), as well as data on Thought Life Not Worth Living (TLNWL) and Ever Self-Harmed (ESH) from the United Kingdom Biobank/Neale lab, Attempted suicide (SA) from the study led by Erlangsen et al. (Reference Erlangsen, Appadurai, Wang, Turecki, Mors, Werge, Mortensen, Starnawska, Børglum, Schork, Nudel, Bækvad-Hansen, Bybjerg-Grauholm, Hougaard, Thompson, Nordentoft and Agerbo2020) which can be retrieved within the iPSYCH Biobank (Erlangsen et al., Reference Erlangsen, Appadurai, Wang, Turecki, Mors, Werge, Mortensen, Starnawska, Børglum, Schork, Nudel, Bækvad-Hansen, Bybjerg-Grauholm, Hougaard, Thompson, Nordentoft and Agerbo2020). Additionally, summary-level data on seven subcortical brain volumes including Amygdala (AMY), Accumbens (ACC), Caudate (CAU), Hippocampus (HIP), Pallidum (PAL), Putamen (PUT), and Thalamus (THA) with the Intracranial Volume (ICV) were sourced from the ENIGMA2 study, accessible via the public database (http://enigma.ini.usc.edu/research/download-enigma-gwas-results/). All samples were of European ancestry, and comprehensive details regarding sample collection, genotyping, processing, quality control, and imputation procedures for each GWAS have been previously documented and briefly outlined (Hibar et al., Reference Hibar, Stein, Renteria, Arias-Vasquez, Desrivières, Jahanshad, Toro, Wittfeld, Abramovic, Andersson, Aribisala, Armstrong, Bernard, Bohlken, Boks, Bralten, Brown, Mallar Chakravarty, Chen, Ching, Cuellar-Partida, den Braber, Giddaluru, Goldman, Grimm, Guadalupe, Hass, Woldehawariat, Holmes, Hoogman, Janowitz, Jia, Kim, Klein, Kraemer, Lee, Olde Loohuis, Luciano, Macare, Mather, Mattheisen, Milaneschi, Nho, Papmeyer, Ramasamy, Risacher, Roiz-Santiañez, Rose, Salami, Sämann, Schmaal, Schork, Shin, Strike, Teumer, van Donkelaar, van Eijk, Walters, Westlye, Whelan, Winkler, Zwiers, Alhusaini, Athanasiu, Ehrlich, Hakobjan, Hartberg, Haukvik, Heister, Hoehn, Kasperaviciute, Liewald, Lopez, Makkinje, Matarin, Naber, Reese McKay, Needham, Nugent, Pütz, Royle, Shen, Sprooten, Trabzuni, van der Marel, van Hulzen, Walton, Wolf, Almasy, Ames, Arepalli, Assareh, Bastin, Brodaty, Bulayeva, Carless, Cichon, Corvin, Curran, Czisch, de Zubicaray, Dillman, Duggirala, Dyer, Erk, Fedko, Ferrucci, Foroud, Fox, Fukunaga, Raphael Gibbs, Göring, Green, Guelfi, Hansell, Hartman, Hegenscheid, Heinz, Hernandez, Heslenfeld, Hoekstra, Holsboer, Homuth, Hottenga, Ikeda, Jack, Jenkinson, Johnson, Kanai, Keil, Kent, Kochunov, Kwok, Lawrie, Liu, Longo, McMahon, Meisenzahl, Melle, Mohnke, Montgomery, Mostert, Mühleisen, Nalls, Nichols, Nilsson, Nöthen, Ohi, Olvera, Perez-Iglesias, Bruce Pike, Potkin, Reinvang, Reppermund, Rietschel, Romanczuk-Seiferth, Rosen, Rujescu, Schnell, Schofield, Smith, Steen, Sussmann, Thalamuthu, Toga, Traynor, Troncoso, Turner, Valdés Hernández, van ‘t Ent, van der Brug, van der Wee, van Tol, Veltman, Wassink, Westman, Zielke, Zonderman, Ashbrook, Hager, Lu, McMahon, Morris, Williams, Brunner, Buckner, Buitelaar, Cahn, Calhoun, Cavalleri, Crespo-Facorro, Dale, Davies, Delanty, Depondt, Djurovic, Drevets, Espeseth, Gollub, Ho, Hoffmann, Hosten, Kahn, Le Hellard, Meyer-Lindenberg, Müller-Myhsok, Nauck, Nyberg, Pandolfo, Penninx, Roffman, Sisodiya, Smoller, van Bokhoven, van Haren, Völzke, Walter, Weiner, Wen, White, Agartz, Andreassen, Blangero, Boomsma, Brouwer, Cannon, Cookson, de Geus, Deary, Donohoe, Fernández, Fisher, Francks, Glahn, Grabe, Gruber, Hardy, Hashimoto, Hulshoff Pol, Jönsson, Kloszewska, Lovestone, Mattay, Mecocci, McDonald, McIntosh, Ophoff, Paus, Pausova, Ryten, Sachdev, Saykin, Simmons, Singleton, Soininen, Wardlaw, Weale, Weinberger, Adams, Launer, Seiler, Schmidt, Chauhan, Satizabal, Becker, Yanek, van der Lee, Ebling, Fischl, Longstreth, Greve, Schmidt, Nyquist, Vinke, van Duijn, Xue, Mazoyer, Bis, Gudnason, Seshadri, Ikram, Martin, Wright, Schumann, Franke, Thompson and Medland2015; Kurki et al., Reference Kurki, Karjalainen, Palta, Sipilä, Kristiansson, Donner, Reeve, Laivuori, Aavikko, Kaunisto, Loukola, Lahtela, Mattsson, Laiho, Della Briotta Parolo, Lehisto, Kanai, Mars, Rämö, Kiiskinen, Heyne, Veerapen, Rüeger, Lemmelä, Zhou, Ruotsalainen, Pärn, Hiekkalinna, Koskelainen, Paajanen, Llorens, Gracia-Tabuenca, Siirtola, Reis, Elnahas, Sun, Foley, Aalto-Setälä, Alasoo, Arvas, Auro, Biswas, Bizaki-Vallaskangas, Carpen, Chen, Dada, Ding, Ehm, Eklund, Färkkilä, Finucane, Ganna, Ghazal, Graham, Green, Hakanen, Hautalahti, Hedman, Hiltunen, Hinttala, Hovatta, Hu, Huertas-Vazquez, Huilaja, Hunkapiller, Jacob, Jensen, Joensuu, John, Julkunen, Jung, Junttila, Kaarniranta, Kähönen, Kajanne, Kallio, Kälviäinen, Kaprio, Kettunen, Kilpeläinen, Kilpi, Klinger, Kosma, Kuopio, Kurra, Laisk, Laukkanen, Lawless, Liu, Longerich, Mägi, Mäkelä, Mäkitie, Malarstig, Mannermaa, Maranville, Matakidou, Meretoja, Mozaffari, Niemi, Niemi, Niiranen, O´Donnell, Obeidat, Okafo, Ollila, Palomäki, Palotie, Partanen, Paul, Pelkonen, Pendergrass, Petrovski, Pitkäranta, Platt, Pulford, Punkka, Pussinen, Raghavan, Rahimov, Rajpal, Renaud, Riley-Gillis, Rodosthenous, Saarentaus, Salminen, Salminen, Salomaa, Schleutker, Serpi, Shen, Siegel, Silander, Siltanen, Soini, Soininen, Sul, Tachmazidou, Tasanen, Tienari, Toppila-Salmi, Tukiainen, Tuomi, Turunen, Ulirsch, Vaura, Virolainen, Waring, Waterworth, Yang, Nelis, Reigo, Metspalu, Milani, Esko, Fox, Havulinna, Perola, Ripatti, Jalanko, Laitinen, Mäkelä, Plenge, McCarthy, Runz, Daly and Palotie2023). Details regarding the number of samples are outlined in Table 1.
Summary information of the phenotypes of our study
Upon retrieving data from the FinnGen database, we initiated a meticulous process of data refinement. Initially, we conducted data cleaning to ensure its quality and reliability. Duplicate Single Nucleotide Polymorphisms (SNPs) were removed, and we extracted SNPs with a Minor Allele Frequency (MAF) exceeding 0.01. Additionally, SNPs with conflicting alleles and those with missing information within the Genome-Wide Association Study (GWAS) summary statistics for each disorder were excluded from further analysis.
SNP-based heritability and genome-wide genetic correlation
To gauge the portion of phenotypic variance attributable to common genetic variants, known as SNP-based heritability (h2 SNP), we employed univariate LD-score regression (LDSC) (Bulik-Sullivan et al., Reference Bulik-Sullivan, Finucane, Anttila, Gusev, Day, Loh, Duncan, Perry, Patterson, Robinson, Daly, Price and Neale2015). This method was implemented using the Genomic SEM R package (Grotzinger et al., Reference Grotzinger, Rhemtulla, de Vlaming, Ritchie, Mallard, Hill, Ip, Marioni, McIntosh, Deary, Koellinger, Harden, Nivard and Tucker-Drob2019). We adhered to default LDSC settings for quality control processes, which involved filtering SNPs to HapMap3, excluding SNPs within the major histocompatibility complex (MHC) region, and removing SNPs with a MAF less than 1%. The defaults in LDSC were followed in the quality control (QC) processes for creating the genetic covariance (S) and sampling covariance (V) matrices. The MHC region, characterised by a complex gene network, often contains SNPs with disproportionately large effect sizes, thus necessitating its exclusion to prevent skewing results from heritability and genetic correlation studies, as well as in the genomic SEM analyses (Grotzinger et al., Reference Grotzinger, Rhemtulla, de Vlaming, Ritchie, Mallard, Hill, Ip, Marioni, McIntosh, Deary, Koellinger, Harden, Nivard and Tucker-Drob2019). LD scores used in the analysis were derived from the 1000 Genomes European sample, limited to HapMap3 SNPs for reliable heritability estimates.
Genomic structural equation modelling analysis
Genomic factor analysis was conducted using the Genomic SEM R package. Initially, a genomic exploratory factor analysis was performed to determine the optimal number of factors describing shared genetic variation. This informed subsequent genomic confirmatory factor analysis to estimate model parameters for fitting. We employed diagonally weighted least squares estimation due to its robustness when modelling traits with varying characteristics. Model fit was evaluated using established criteria for absolute fit, including the standardised root mean square residual (SRMR) with values ≤ 0.10 indicating moderate fit and SRMR ≤ 0.05 indicating good fit; comparative fit index (CFI) with values≥0.90 indicating moderate fit and CFI≥0.95 indicating good fit; and lower chi-square statistic with p-value less than 0.05 suggesting a precise match and greater fit (Grotzinger et al., Reference Grotzinger, Rhemtulla, de Vlaming, Ritchie, Mallard, Hill, Ip, Marioni, McIntosh, Deary, Koellinger, Harden, Nivard and Tucker-Drob2019). We use this approach to derive a potential common factor model and thereafter perform GWAS meta-analyses at SNP, gene and sub-network levels encompassing the phenotypes detected within the potential common factor.
Results
SNP-based heritability and genome-wide genetic correlations
The heritability estimates presented in Table 2 were derived from our analysis and are expressed on the observed scale. For traits with lower-than-usual heritability estimates and higher standard errors compared to other studies, the SNP-based heritability could not be identified due to insufficient statistical power. Using bivariate LDSC (Bulik-Sullivan et al., Reference Bulik-Sullivan, Finucane, Anttila, Gusev, Day, Loh, Duncan, Perry, Patterson, Robinson, Daly, Price and Neale2015) implemented in the R package Genomic SEM, we estimated genetic correlations (rg) among the twelve traits. It is important to note that LDSC can sometimes provide estimates outside the range of -1 to + 1, particularly under conditions of large standard errors or highly significant genetic correlations between studies. Additionally, we were unable to generate a genetic correlation estimate for the amygdala with the other phenotypes due to its negative heritability estimate.
Heritability estimates from our analysis
Our study identified a marginal positive genetic correlation between SUIC and intracranial volume (ICV) (rg = 0.47; p-value = 0.024) and a negative genetic correlation (however not significant) between SUIC and accumbens (rg = -0.52; p-value = 0.93). (see Fig. 1A). There are notable positive genetic correlations between several subcortical brain volumes (e.g., accumbens, putamen, caudate, pallidum, thalamus). We found significant genetic correlations between the putamen (PUT) and accumbens (ACC) (rg = 0.51; p-value = 0.043), caudate (CAU) and accumbens (ACC) (rg = 0.56; p-value = 0.0168), thalamus (THA) and accumbens (ACC) (rg = 0.52; p-value = 0.022), and a strong positive genetic correlation between the pallidum (PAL) and thalamus (THA) (rg = 0.6; p-value = 0.02). Other Suicidality-Related Traits (ESH, SA, TLNWL) exhibit various correlations with each other and non-significant correlations with brain structures, with ESH and SA showing high significant correlations with each other (rg = 1.02; p-value = 2.45 × 10-14). The pattern of correlations highlights potential shared genetic underpinnings between certain brain volumes and suicidality, warranting further investigation into the underlying mechanisms. This analysis provides insight into the genetic architecture connecting brain structures and suicidality-related traits, suggesting both shared and unique genetic factors across these phenotypes.
(A) The heatmap shows the genetic correlations (rg) between various brain structures and suicidality-related traits. The values represent the strength and direction of the genetic correlations, with significant (P-values less than 0.05) correlations indicated by asterisks (*). The colour scale ranges from blue (positive correlations) to red (negative correlations), with darker shades representing stronger correlations. (B) Path diagram for the single common factor model. This figure illustrates the overall common variance among all included traits. Ellipses represent latent variables, rectangles represent observed variables/traits, numbers on arrows are standardised factor loadings, and numbers at the ends of arrows are residual variances. (C) Path diagram of the revised common factor (Labelled ‘REV_F1’). This diagram illustrates the overall common variance among all included traits, representing observed variables with ‘heart’ shapes and the unobserved (latent) variable with a ’star’ shape. It suggests two groups of disorders sharing the same common factor: the first group in red and the second group in green. One-headed arrows represent regression connections between variables, while two-headed arrows indicate the variance of a variable or the covariance between a variable and itself. This analysis aimed to identify overlapping genetic factors and elucidate potential shared molecular mechanisms across the included traits.
Genomic structural equation modeling analysis
First, we assessed the extent of common genetic variance among all included traits by evaluating the performance of a common genetic factor model. Although the model with freely determined loadings converged, it did not fit well (chisq(44) = 215.0768, Pchisq = 2.21 × 10-24, AIC = 259.0768, CFI = 0.696, SRMR = 0.194) (Fig. 1B).
Our genomic SEM analysis reveals an intriguing revised common factor model that fits the data well, with the best-fit statistics (chisq(15) = 25.69, Pchisq = 0.04, AIC = 127.69, CFI = 0.981, SRMR = 0.047) (Fig. 1C). This model identifies a common latent factor divided into two distinct groups of phenotypes. The first group includes the suicidal traits from the UK Biobank (ESH, SA, and TLNWL) highlighted in red, while the second group includes SUIC, ICV, Accumbens, Caudate, Hippocampus, Pallidum, Thalamus, and Putamen highlighted in green. This suggests that SUIC has a closer genomic link with subcortical brain volume and ICV compared to the suicidal traits ESH, SA, and TLNWL. However, both groups of traits exhibit the same genomic common factor, indicating the presence of a shared molecular mechanism.
GWAS meta-analysis at variant and gene level
We conducted a variant-based GWAS meta-analysis using RE2C (v1.06) (Lee et al., Reference Lee, Eskin and Han2017) to account for sample overlap among GWAS summary data. Significant variants were identified based on the RE2C P-value statistic (RE2C*P < 5 × 10-8). Variants that became significant after meta-analysis but did not reach genome-wide significance in individual trait GWAS datasets were considered novel (Kanai et al., Reference Kanai, Tanaka and Okada2016). In addition to the RE2C model, we performed cross-trait GWAS meta-analyses using both fixed effect (FE) and modified random effects (RE2) models (Han and Eskin, Reference Han and Eskin2011), integrated into the METASOFT software (http://genetics.cs.ucla.edu/meta/). The FE model, which assumes the GWAS traits examined the same (fixed) effect, used the inverse variance weighted technique to estimate SNP meta-analysis statistics (effect size and p-value). In cases of heterogeneity, indicated by I2 statistics, METASOFT employed the RE2 model to estimate SNP meta-analysis statistics. Gene and subnetwork-specific meta-analyses were conducted using ancMETA (Chimusa and Defo, Reference Chimusa and Defo2022), which incorporates summary GWAS information and aggregates SNPs within nearby genes. ancMETA provides information on significant genes and hub genes based on known biological protein-protein networks, shedding light on potential biological pathways shared across disorders. The meta-analysis GWAS focused on two sets of phenotypes:
Group 1: ESH, SA, and TLNWL.
Group 2: SUIC, ICV, Accumbens, Thalamus, Putamen, Caudate, Pallidum, and Hippocampus.
GWAS meta-analysis at SNP level between ESH, SA, and TLNWL
Our cross-trait meta-analysis using the RE2C model identified 37 significant variants (RE2C*P < 5 × 10-8) (Table 3, Supplementary Table 1), all of which exhibited small effect sizes. Of these, 31 were novel, meaning they were not previously associated with any of the disorders (Peach_study>5 × 10-8).
Top significant variants from cross-trait meta-analysis between each set of phenotypes
Our SNP-level results indicate that the most significant variants are located within the DCC gene. Additionally, associations were found with SNPs in the SH3GL3, STIM2, MEAF6, and RSPO1 genes (Table 3, Supplementary Table 1). The top four significant novel loci are all located within the DCC gene, while other new associations were found within the STIM2, MEAF6, and RSPO1 genes. These findings highlight new genetic loci that add value to previously identified genes in the literature.
GWAS meta-analysis at SNP level between SUIC, ICV, accumbens, caudate, hippocampus, pallidum, thalamus, and putamen
Our GWAS meta-analysis using the RE2C model identified 484 significant variants, all exhibiting low effect sizes (Table 3, Supplementary Table 2). Among these, 64 SNPs showed potential pleiotropic effects, influencing multiple subcortical brain structures simultaneously, including the accumbens, caudate, hippocampus, pallidum, thalamus, and putamen. According to the FUMA analysis, the genes located near these significant loci exhibit enrichment across various brain regions. The highest levels of enrichment were found in the hypothalamus, brain cortex, and frontal cortex (Fig. 2A). However, the genes near the pleiotropic loci showed enrichment in nearly all parts of the brain, with the exception of the putamen basal ganglia and the spinal cord (Fig. 2B).
(A)- Bar plot showing enrichment tissues of all the nearby genes from significant cross-associated SNPs; (B) bar plot showing enrichment tissues of nearby genes from the significant potential pleiotropic (accumbens, caudate, hippocampus, pallidum, thalamus, and putamen combined) SNPs. The red colour speaks for significance and the blue one speaks for non-significance.
These findings suggest widespread genetic influences across various brain regions, emphasising the importance of considering multiple brain structures when studying genetic associations with SUIC and subcortical brain volumes.
GWAS meta-analysis at gene and sub-network level between ESH, SA, and TLNWL
Gene-level analysis
At the gene level, ancMETA identified 893 significant genes (Table 3; Supplementary Table 3) associated with ESH, SA, and TLNWL (overall P < 0.05). The top significant genes include RANBP17 (p-value = 3.25 × 10-05), C6orf89 (p-value = 1.8 × 10-04), and GPHN (p-value = 2.1 × 10-04). RANBP17 is Located on chromosome 5q35.1 and encodes RAN-binding protein 17, a nuclear transport receptor. It has been associated with the severity of suicide attempts in mood disorders at the polymorphism level (Zai et al., Reference Zai, Fabbri, Hosang, Zhang, Koyama, de Luca, Tiwari, King, Strauss, Jones, Jones, Breen, Farmer, McGuffin, Vincent, Kennedy and Lewis2021). C6orf89 which encodes the bombesin receptor-activated protein (BRAP), is associated with allergic rhinitis and asthma and is potentially implicated in the stress response of lung epithelia (Liu et al., Reference Liu, Qin, Weber, Xiang, Liu, Liu, Yang, Jiang and Qu2016; Xu et al., Reference Xu, Chen, Ran, Xie, Li and Zhou2017). Studies in mice suggest that BRAP regulates dendritic spine development and synaptic plasticity in the hippocampus, providing a protective behavioural response to stress (Yao et al., Reference Yao, Qin, Wang, Zheng, Peng, Wang, Weber, Liu, Zhang, Zeng, Zuo, Chen, Xiang, Liu, Liu, Pan and Qu2023). Regarding Gephyrin (GPHN), previous studies have linked exonic microdeletions in this gene to neurodevelopmental issues such as idiopathic generalised epilepsy (Dejanovic et al., Reference Dejanovic, Lal, Catarino, Arjune, Belaidi, Trucks, Vollmar, Surges, Kunz, Motameny, Altmüller, Köhler, Neubauer, Nürnberg, Noachtar, Schwarz and Sander2014), schizophrenia, autism spectrum disorder, and epileptic seizures. These findings suggest that while the effects of variants within these genes differ between studies, the aggregation of variant effects within these genes significantly contributes to the cross-phenotype association of ESH, SA, and TLNWL.
Sub-network level analysis
At the sub-network level, ancMETA identified 50 significant hub genes (Supplementary Table 4). Among these, the top significant genes were GPHN (p-value = 0.00022), RGS2 (p-value = 0.004), and ATP1A1 (p-value = 0.0045). These hub genes indicate that the aggregation’s effect of variants within these genes significantly contributes to the cross-phenotype association at the pathway/gene set level, encompassing the phenotypes of ESH, SA, and TLNWL. Our FUMA analysis revealed that the significant genes and hub genes identified in our ancMETA results showed significant expression enrichment across all brain regions. The top enriched tissues include the brain anterior cingulate cortex, cultured fibroblast cells, brain hippocampus, brain putamen basal ganglia, and brain substantia nigra (Fig. 3B).
(A) The bar plot shows tissue enrichment for all significant genes and hub genes identified through ancMETA analysis at the gene and subnetwork levels, using suicidality data from FinnGen and subcortical brain volume data from ENIGMA. (B) The bar plot displays tissue enrichment for significant genes and hub genes identified through ancMETA analysis at the gene and subnetwork levels, using emotional stability, social anxiety, and tolerance to noise and workload data from the UK Biobank. Red indicates significance, while blue indicates non-significance. (C) This potential subnetwork includes all significant genes and hub genes combined, generated by ancMETA from the two sets of phenotypes.
Previous studies have highlighted the association of numerous variants within the RGS2 gene with a higher risk of successful suicide (Cui et al., Reference Cui, Nishiguchi, Ivleva, Yanagi, Fukutake, Nushida, Ueno, Kitamura, Maeda and Shirakawa2008; Amstadter et al., Reference Amstadter, Koenen, Ruggiero, Acierno, Galea, Kilpatrick and Gelernter2009). ATP1A1, a member of the sodium/potassium pump (Na+/K+-ATPase) family expressed in the brain, regulates the gradient of potassium and sodium across cellular membranes (Richards et al., Reference Richards, Bommert, Szabo and Miles2007). Research has verified the involvement of brain Na+/K+-ATPase α subunit isoforms, particularly the α2 and α3 subunits, in various behavioural features, linking them to mental and behavioural disorders in humans (Lingrel et al., Reference Lingrel, Williams, Vorhees and Moseley2007; Tochigi et al., Reference Tochigi, Iwamoto, Bundo, Sasaki, Kato and Kato2008). Another study has demonstrated the connection between ATP1A1 expression levels and clinical anxiety scores in patients with major depressive disorder (Zhao et al., Reference Zhao, Guo, Du, Han, Wang, Li, Qian, Li, Wu, Golden and Wu2016).
GWAS meta-analysis at gene and sub-network level between suic, icv, accumbens, caudate, hippocampus, pallidum, thalamus, and putamen
Gene-level analysis
In a comprehensive GWAS meta-analysis, ancMETA identified 402 significant genes cross-associated with SUIC, ICV, and various subcortical brain regions, including the accumbens, caudate, hippocampus, pallidum, thalamus, and putamen (overall P < 0.05; Table 4, Supplementary Table 5). The top significant genes were RPL11 (p-value = 1.8 × 10-4), DDX4 (p-value = 4.03 × 10-4), and WDR55 (p-value = 1.06 × 10-3). RPL11 has been previously implicated in the ribosomal pathway, playing a role in the pathogenesis of mild cognitive impairment and Alzheimer’s disease (Qin et al., Reference Qin, Hu, Zhao, Tian and Zhu2023). It is also associated with brain arteriovenous malformations (Zhang et al., Reference Zhang, Ding, Zhang, Liu, Zhang, Zhang, Tian, Li, Zhu, Kang, Wang, Wu, Wang, Yang and Wang2021) and has been proposed as a biomarker for major depressive disorder Zhang et al., Reference Zhang, Xu, Yuan and Shen2020) and low-risk neuroblastoma (Nguyen et al., Reference Nguyễn, Diskin, Capasso, Wang, Diamond, Glessner, Kim, Attiyeh, Mosse, Cole, Iolascon, Devoto, Hakonarson, Li, Maris and Schork2011). WDR55 encodes WD repeat-containing protein 55, which modulates ribosomal RNA biogenesis, cell cycle progression, and organ development. It has been identified as a significant CpG site and methylated region associated with depression risk in Chinese monozygotic twins (Wang et al., Reference Wang, Li, Wu, Tian, Duan, Li, Tan and Zhang2021).
Top 3 significant genes and subnetwork hub genes from cross-trait meta-analysis between each set of phenotypes
Sub-network level analysis
At the sub-network level, ancMETA identified 22 significant hub genes (Supplementary Table 6). The most significant hub genes included NEB (p-value = 0.012), EEF1D (p-value = 0.013), and B2M (p-value = 0.019) (Table 4). These hub genes suggest that the aggregate effect of variants within these genes significantly contributes to the cross-phenotype association risk at the pathway/gene set level, encompassing SUIC and brain structures such as ICV, accumbens, caudate, hippocampus, pallidum, thalamus, and putamen. Our FUMA analysis showed that the significant genes and hub genes identified from ancMETA results exhibited significant expression enrichment in all brain regions, except for the cerebellum and cerebellar hemisphere, where down-regulated expressed genes were specific. The top enriched tissues included the heart and left ventricle, pancreas, putamen basal ganglia, substantia nigra, and hippocampus (Fig. 3A).
NEB (on chromosome 2q23.3) encodes nebulin, a protein extensively expressed in skeletal muscle, known for regulating muscle contraction and stabilising thin filaments (Chandra et al., Reference Chandra, Mamidi, Ford, Hidalgo, Witt, Ottenheijm, Labeit and Granzier2009). Immunohistochemistry has shown nebulin expression predominantly in the cytoplasm of pyramidal neurons and subcortical endothelial cells in the adult brain (Laitila et al., Reference Laitila, Hanif, Paetau, Hujanen, Keto, Somervuo, Huovinen, Udd, Wallgren‐Pettersson, Auvinen, Hackman and Pelin2012). Whole exome sequencing identified two likely pathogenic NEB variants in a patient with cognitive impairment and dysmorphic features (Nóbrega et al., Reference Nóbrega, de Brito de Souza, Maurício, de Paiva, Dias, Camelo, Zanotelli, Schlesinger, Braga-Neto and Moreno2024), suggesting a potential role for nebulin in the central nervous system and suicidality risk.
EEF1D (located on chromosome 8q24.3) undergoes alternative splicing in the brain and testis, affecting its expression. Mutations in EEF1D have been linked to neurodevelopmental disorders, microcephaly, and severe intellectual disability (Kaitsuka and Matsushita, Reference Kaitsuka and Matsushita2015; McLachlan et al., Reference McLachlan, Sires and Abbott2019). B2M (Beta-2-Microglobulin) has been identified as a biomarker for stress-related disorders, including suicide (Le-Niculescu et al., Reference Le-Niculescu, Roseberry, Levey, Rogers, Kosary, Prabha, Jones, Judd, McCormick, Wessel, Williams, Phalen, Mamdani, Sequeira, Kurian and Niculescu2020). Additionally, B2M is associated with various neuropsychiatric phenotypes, such as alcoholism, autism, depression, eating disorders, pain, and ageing, potentially mediating the effects of stress in these conditions (Le-Niculescu et al., Reference Le-Niculescu, Roseberry, Levey, Rogers, Kosary, Prabha, Jones, Judd, McCormick, Wessel, Williams, Phalen, Mamdani, Sequeira, Kurian and Niculescu2020).
Utilizing network and pathway analysis across two sets of disorders
In this study, we performed a network and pathway analysis involving two distinct sets of disorders. Initially, we used ancMETA to generate subnetworks containing significant genes and hub genes for each set of disorders. These subnetworks were then merged using Cytoscape version 3.7.2 (see Fig. 3C). We conducted pathway enrichment analysis based on Gene Ontology (GO), Reactome pathways, and the Protein-Protein Interaction network and visualised the results with the StringApp plugin in Cytoscape version 3.7.2 (Shannon et al., Reference Shannon, Markiel, Ozier, Baliga, Wang, Ramage, Amin, Schwikowski and Ideker2003; Doncheva et al., Reference Doncheva, Morris, Gorodkin and Jensen2019). The merged subnetwork of genes was assessed for enrichment in pathways and gene ontology using the Cytoscape plugin StringApp.
In the resulting network, we identified a significant number of pathways (FDR<0.05), specifically 132 Reactome pathways, 50 KEGG pathways, and 51 WikiPathways. The most notable KEGG pathways included the Rap1 signalling pathway (FDR = 1.3 × 10-4), osteoclast differentiation (FDR = 1.3 × 10-4), T cell receptor signalling pathway (FDR = 1.3 × 10-4), and viral carcinogenesis (FDR = 3.9 × 10-4). Reactome analysis highlighted significant pathways such as Disease (FDR = 1.12 × 10-10), signalling by receptor tyrosine kinases (FDR = 3.9 × 10-9), signal transduction (FDR = 2.95 × 10-7), adaptive immune system (FDR = 1.5 × 10-6), infectious disease (FDR = 4.01 × 10-6), and the immune system (FDR = 1.1 × 10-5). WikiPathways analysis identified VEGFA-VEGFR2 signalling (FDR = 6.4 × 10-8), RANKL/RANK signalling pathway (FDR = 7.97 × 10-7), and the T-cell receptor signalling pathway (FDR = 5.07 × 10-5) as particularly significant.
A detailed table listing each significant pathway per database, along with all significant GO biological processes, components, and functions, is provided in Supplementary Tables 7-13. Additionally, we compiled a list of pathogenic loci identified from our gene/subnetwork GWAS meta-analysis using ancMETA on the two sets of phenotypes, with pathogenic criteria based on a probability of being ‘loss-of-function Intolerant’ > 0.9 (Lek et al., Reference Lek, Karczewski, Minikel, Samocha, Banks, Fennell, O’Donnell-Luria, Ware, Hill, Cummings, Tukiainen, Birnbaum, Kosmicki, Duncan, Estrada, Zhao, Zou, Pierce-Hoffman, Berghout, Cooper, Deflaux, DePristo, Do, Flannick, Fromer, Gauthier, Goldstein, Gupta, Howrigan, Kiezun, Kurki, Moonshine, Natarajan, Orozco, Peloso, Poplin, Rivas, Ruano-Rubio, Rose, Ruderfer, Shakir, Stenson, Stevens, Thomas, Tiao, Tusie-Luna, Weisburd, Won, Yu, Altshuler, Ardissino, Boehnke, Danesh, Donnelly, Elosua, Florez, Gabriel, Getz, Glatt, Hultman, Kathiresan, Laakso, McCarroll, McCarthy, McGovern, McPherson, Neale, Palotie, Purcell, Saleheen, Scharf, Sklar, Sullivan, Tuomilehto, Tsuang, Watkins, Wilson, Daly and MacArthur2016) (Supplementary Table 14).
Discussion
The findings of this study offer crucial insights into the intricate genetic relationship between suicidality and alterations in brain structure, particularly in subcortical brain regions. This highlights possible shared molecular mechanisms and genetic underpinnings. The discovery of a common genetic factor between suicidality and subcortical brain regions underscores the existence of shared pathways and biological processes. Although we identified a nominal positive genetic correlation between SUIC and ICV, this emphasises the complexity of the relationship and the need for further exploration using diverse methodologies and larger sample sizes (Franke et al., Reference Franke, Stein, Ripke, Anttila, Hibar, van Hulzen, Arias-Vasquez, Smoller, Nichols, Neale, McIntosh, Lee, McMahon, Meyer-Lindenberg, Mattheisen, Andreassen, Gruber, Sachdev, Roiz-Santiañez, Saykin, Ehrlich, Mather, Turner, Schwarz, Thalamuthu, Yao, Ho, Martin, Wright, O’Donovan, Thompson, Neale, Medland and Sullivan2016; Smeland et al., Reference Smeland, Wang, Frei, Li, Hibar, Franke, Bettella, Witoelar, Djurovic, Chen, Thompson, Dale and Andreassen2018). Furthermore, our study demonstrated a common factor emerging from two cohorts: the suicide cohort from the UK Biobank (emotional stability, social anxiety, and tolerance to noise and workload) and the phenotypes, including SUIC from FinnGen and subcortical brain volume data. This suggests a direct overlap between SUIC and subcortical brain regions in the FinnGen cohort, compared to the UK Biobank cohort.
At the SNP level, our comprehensive analysis revealed significant variants within key genes, including DCC, SH3GL3 (rs150002680), STIM2 (rs28592695), MEAF6 (rs6682470), and RSPO1 (rs115632986) from the UK Biobank. This adds to the loci previously reported by Strawbridge and colleagues (2019). The SNP-based GWAS meta-analysis between SUIC and subcortical brain volume identified 484 significant variants with low effects, with 64 SNPs showing potential pleiotropic effects on the accumbens, caudate nucleus, hippocampus, pallidum, thalamus, and putamen. These findings highlight the interconnectedness of genetic factors and support previous research linking suicidality to frontal-subcortical circuits (Tekin and Cummings, Reference Tekin and Cummings2002; Dobbertin et al., Reference Dobbertin, Blair, Carollo, Blair, Dominguez and Bajaj2023).
Beyond individual variants, our gene and subnetwork GWAS meta-analysis unveiled numerous significant genes and hub genes implicated in both SUIC and altered brain volume. Particularly noteworthy are the loss-of-function-related genes, which indicate a pathogenic potential and heightened risk for suicidality (refer to Supplementary Table 14).
The integration of these genetic findings into a comprehensive network analysis revealed enriched functionalities across various biological processes and pathways. Notably, genes related to neuroinflammation were significantly enriched, with pathways involving immune signalling, apoptosis, nervous system, neurodevelopmental disorders (such as Alzheimer’s and Huntington’s Disease), infectious diseases, and neurotrophic factors. These findings suggest potential targets for therapeutic intervention. Several pathways and GO enrichment strategies identified in our study align with previous findings that link the blood-brain barrier and suicidal risk (Mann and Risk, Reference Mann and Rizk2020; Pandey and Dwivedi, Reference Pandey, Dwivedi and Dwivedi2012; Wisłowska-Stanek et al., Reference Wisłowska-Stanek, Kołosowska and Maciejak2021; Bengoechea-Fortes et al., Reference Bengoechea-Fortes, Ramírez-Expósito and Martínez-Martos2023).
Our findings indicate that the presence and severity of suicidality are associated with an inflammatory signature detectable in both blood and brain tissues. This suggests a biological continuity underlying suicidality, potentially indicating a common heritability. These results support the role of brain and peripheral blood inflammation in suicide risk. Our findings suggest that these hub genes or enriched common pathways underlying shared molecular mechanisms between suicidality and altered subcortical brain volume could mean that treatments targeting these biological enriched pathways would have broad-spectrum therapeutic effects, improving precision medicine and personalized therapeutic development in suicidal individuals.
The identification of genes involved in the dysregulation of the blood-brain barrier and immune function underscores the bidirectional communication between the brain and peripheral immune system in the context of suicidal risk. These results are corroborated by previous studies, further strengthening the validity of our findings and highlighting potential translational implications (Sun et al., Reference Sun, Liu, Wang, Huang, Sublette, Dwork, Rosoklija, Ge, Galfalvy, Mann and Haghighi2024). Our findings expand on previous research that identified genes substantially expressed in brain tissue and enriched in pathways related to immunologic markers, cellular stress response, gene regulation, and DNA repair (Docherty et al., Reference Docherty, Mullins, Ashley-Koch, Qin, Coleman, Shabalin, Kang, Murnyak, Wendt, Adams, Campos, DiBlasi, Fullerton, Kranzler, Bakian, Monson, Rentería, Walss-Bass, Andreassen, Behera, Bulik, Edenberg, Kessler, Mann, Nurnberger, Pistis, Streit, Ursano, Polimanti, Dennis, Garrett, Hair, Harvey, Hauser, Hauser, Huffman, Jacobson, Madduri, McMahon, Oslin, Trafton, Awasthi, Berrettini, Bohus, Chang, Chen, Chen, Christensen, Crow, Duriez, Edwards, Fernández-Aranda, Galfalvy, Gandal, Gorwood, Guo, Hafferty, Hakonarson, Halmi, Hishimoto, Jain, Jamain, Jiménez-Murcia, Johnson, Kaplan, Kaye, Keel, Kennedy, Kim, Klump, Levey, Li, Liao, Lieb, Lilenfeld, Marshall, Mitchell, Okazaki, Otsuka, Pinto, Powers, Ramoz, Ripke, Roepke, Rozanov, Scherer, Schmahl, Sokolowski, Starnawska, Strober, Su, Thornton, Treasure, Ware, Watson, Witt, Woodside, Yilmaz, Zillich, Adolfsson, Agartz, Alda, Alfredsson, Appadurai, Artigas, Van der Auwera, Azevedo, Bass, Bau, Baune, Bellivier, Berger, Biernacka, Bigdeli, Binder, Boehnke, Boks, Braff, Bryant, Budde, Byrne, Cahn, Castelao, Cervilla, Chaumette, Corvin, Craddock, Djurovic, Foo, Forstner, Frye, Gatt, Giegling, Grabe, Green, Grevet, Grigoroiu-Serbanescu, Gutierrez, Guzman-Parra, Hamshere, Hartmann, Hauser, Heilmann-Heimbach, Hoffmann, Ising, Jones, Jones, Jonsson, Kahn, Kelsoe, Kendler, Kloiber, Koenen, Kogevinas, Krebs, Landén, Leboyer, Lee, Levinson, Liao, Lissowska, Mayoral, McElroy, McGrath, McGuffin, McQuillin, Mehta, Melle, Mitchell, Molina, Morken, Nievergelt, Nöthen, O’Donovan, Ophoff, Owen, Pato, Pato, Penninx, Potash, Power, Preisig, Quested, Ramos-Quiroga, Reif, Ribasés, Richarte, Rietschel, Rivera, Roberts, Roberts, Rouleau, Rovaris, Sanders, Schofield, Schulze, Scott, Serretti, Shi, Sirignano, Sklar, Smeland, Smoller, Sonuga-Barke, Trzaskowski, Tsuang, Turecki, Vilar-Ribó, Vincent, Völzke, Walters, Weickert, Weickert, Weissman, Williams, Wray, Zai, Agerbo, Børglum, Breen, Demontis, Erlangsen, Gelernter, Glatt, Hougaard, Hwu, Kuo, Lewis, Li, Liu, Martin, McIntosh, Medland, Mors, Nordentoft, Olsen, Porteous, Smith, Stahl, Stein, Wasserman, Werge, Whiteman, Willour, Beckham, Kimbrel and Ruderfer2023; Diblasi et al., Reference DiBlasi, Kang and Docherty2021; Sokolowski and Wasserman, Reference Sokolowski and Wasserman2020).
The involvement of glial cells and microglia in inflammatory responses within the central nervous system (Yang and Zhou, Reference Yang and Zhou2019) provides mechanistic insights into the pathophysiology of suicidality and altered brain volume. Glial cells, the most prevalent cells in the central nervous system, interact with immune system cells, neurons, and brain microvascular endothelial cells. Microglia, in particular, are resident innate immune cells. Studies have shown higher densities of activated microglia (Schnieder et al., Reference Schnieder, Trencevska, Rosoklija, Stankov, Mann, Smiley and Dwork2014) in the white matter of suicide postmortem cases, as well as higher microglial priming and macrophage recruitment (Torres-Platas et al., Reference Torres-Platas, Cruceanu, Chen, Turecki and Mechawar2014). The transmission of inflammatory signals from the periphery to the brain via humoral transmigration or sensory afferent projections through the blood-brain barrier can stimulate microglial activation (Dantzer, Reference Dantzer2009; Serna-Rodríguez et al., Reference Serna-Rodríguez, Bernal-Vega, de la Barquera, Camacho-Morales and Pérez-Maya2022), and suicidality has been linked to anomalies in endothelial cells and the blood-brain barrier (Pantazatos et al., Reference Pantazatos, Huang, Rosoklija, Dwork, Arango and Mann2017; Greene et al., Reference Greene, Hanley and Campbell2020). The identified hub genes and potential significnt pathways related to anti-neuroinflammation and immune regulation offers a promising approach to treating suicidal behaviour with altered subcorticalbrain volume, which is frequently impacted by complicated neuroimmune interactions. However, converting these techniques into clinically effective medicines necessitates overcoming obstacles in gene delivery, safety, and selectivity. Ongoing research in neuroinflammation, immunological signalling, and gene therapy technologies could show promise for more customised and effective therapies for people at risk of suicidality.
Despite these significant findings, several limitations warrant consideration. Generalising our findings to other populations and ethnicities requires replication in diverse cohorts. This study only included individuals of European ancestry, limiting the generalizability of the findings. Expanding future analyses to include diverse populations is essential for broader applicability. Additionally, the reliance on GWAS summary statistics and the inherent statistical power of the original studies necessitates cautious interpretation of null results. Therefore, null conclusions in our research do not always imply a lack of association. The negative heritability of the amygdala did not provide a clear picture of genetic correlation and was not included in our SNPs, gene, and subnetwork GWAS meta-analysis. A well-powered GWAS of altered brain volume and suicidality could improve the detection of significant variants, genes, and pathways shared between these traits. Most of our suicide phenotypes have very low heritability, confirmed by the low effect sizes of relevant loci. Hence, the identified loci, networks, and functional pathways need validation in future studies with additional experiments, either in vivo or in vitro. Our findings are limited to autosomal chromosomal common variants. Copy number variants and other rare variants independently demonstrate strong penetrance for suicidal risk (Gross et al., Reference Gross, Bureau, Croteau, Galfalvy, Oquendo, Haghighi, Mérette, Giegling, Hodgkinson, Goldman, Rujescu, Mann, Turecki and Kaderali2015). Incorporating these in future studies could provide a more comprehensive view of genetic contributions. Known sex-specific effects in individuals with suicidal behaviour (Kia-Keating et al., Reference Kia-Keating, Glatt and Tsuang2007; Powers et al., Reference Powers, Joyce, Kleinman, Hyde, Ajilore, Leow and Sodhi2020) and brain development (Mallard et al., Reference Mallard, Liu, Seidlitz, Ma, Moraczewski, Thomas and Raznahan2021) further necessitate future studies on rare variants and sex-specific shared mechanisms.
In conclusion, this study represents a pioneering effort in elucidating the shared genetic architecture of suicidality and subcortical brain volumes. By uncovering overlapping genetic factors and biological pathways, we provide novel insights into the complex interplay between brain structure and suicidal behaviour. These findings hold promise for developing targeted interventions and personalized treatment strategies aimed at mitigating suicidality in vulnerable individuals. Further research exploring rare variants, sex-specific effects, and functional validations will be crucial for advancing our understanding of these complex phenomena and informing clinical practice.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/neu.2025.12.
Acknowledgements
We also would like to acknowledge the participants and the investigators of the FinnGen study.
Computations were performed using facilities provided by the Cape Town Centre for High
Performance Computing team (https://www.chpc.ac.za/).
Author contribution
Joel Defo and Raj Ramesar conceived and designed the study. Joel Defo acquired, analysed and interpreted the data. Joel Defo drafted the manuscript. Joel Defo and Raj Ramesar revised the manuscript. Joel Defo and Raj Ramesar approved the manuscript.
Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors.
Competing interests
The authors declare none.
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