Genetic Architecture of Suicidal Ideation Continuum: Latent Profile Analysis of Data using the Million Veteran Program Cohort

Genetic Architecture of Suicidal Ideation Continuum: Latent Profile Analysis of Data using the Million Veteran Program Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genetic Architecture of Suicidal Ideation Continuum: Latent Profile Analysis of Data using the Million Veteran Program Cohort Fatemeh Haghighi, Shengnan Sun, Daniel Dochtermann, Saiju Pyarajan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6066944/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Molecular Psychiatry → Version 1 posted 12 You are reading this latest preprint version Abstract Suicidal ideation (SI) and behavior are complex phenotypes, with multiple contributing risk-factors. This study used longitudinal data from the Million Veteran Program Mental Health Survey to identify SI profiles among Veterans based on trajectories of ideation and depression severity, and compared them to a non-suicidal (no-SI) control group. Latent profile analysis (LPA) was performed to identify SI profiles using data from Veterans (n = 34,322) endorsing SI in their electronic health record. LPA identified four highly reproducible SI profiles: mild ideators with and without depression, variable ideators, and persistent ideators. Veterans across the SI profiles were significantly more likely to have diagnoses of suicidal ideation or behavior, mental disorders, and TBI compared to Veterans with no-SI. The variable ideators showed higher rates of comorbid conditions. The mild ideators without depression and persistent ideators had a significantly higher proportion of deaths by suicide than the no-SI Veterans. European and African American GWAS and pan-ancestry meta-analyses of SI profiles compared to no-SI controls were also performed, which identified genome-wide significant loci across all SI profiles proximal to genes implicated in auditory and vestibular functioning, Alzheimer’s, Diabetes, and Asthma. In summary, SI profiles identified were associated with novel genetic variants not identified by previous suicide GWAS studies. Additionally, Veterans within the mild SI profile that did not present with high-risk comorbidities had the highest rate of suicide deaths, indicating the need for upstream suicide risk prevention interventions across the SI risk continuum. Biological sciences/Genetics Biological sciences/Psychology suicidal ideation suicidal behavior million veteran program depression latent profile analysis Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION Suicide is a major public health concern, representing the 11th highest cause of death in the United States with approximately 48,000 individuals dying by suicide in 2021. 1 Among US Veterans, suicide rates are alarmingly higher than among civilians: in 2021 the age- and sex-adjusted rate for Veterans was 71.8% greater than that of non-Veteran adults. 2 In 2020, 4.9% of US adults reported suicide ideation, 1.3% suicide planning, and 0.5% attempting suicide. 3 Prior history of suicide ideation and attempt is a predictor of future death by suicide. Death by suicide is 30–40 times greater in those who have previously attempted suicide relative to the general population 4 , 5 with population based studies showing that the majority of suicide attempters endorse suicidal ideation. 6 Individuals endorsing suicidal ideation exhibit a 4-fold greater risk for death by suicide compared to non-ideators. 7 Thus, a better understanding of the suicidal ideation risk continuum is crucial to improving prediction and may aid in prospectively identifying those at risk. A confluence of factors can contribute to suicide risk, including the interaction of a variety of biological, clinical, psychological, social, cultural and environmental factors, which has led to the observed etiological heterogeneity of suicidality. 8 The presence of a psychiatric disorder including serious mental illness (e.g., Schizophrenia and Bipolar disorder), anxiety and PTSD, and particularly Major Depressive disorder are well-established risk factors for suicide. 9 , 10 Indeed, 90% of suicides have a mental disorder, 11 with those with depressive disorders being at 70% greater risk of death by suicide than the general population, 12 , 13 as corroborated by psychological autopsy studies showing 60% of suicides occurred in Major Depressive episode. 14 , 15 Also, patients with depression are more likely to attempt suicide than patients with other psychiatric disorders, 13 , 16 underscoring the importance of investigating suicidality in the context of depression. Overwhelming evidence, however, indicates that suicidal thoughts and behavior are transdiagnostic phenomena that can manifest in the absence of psychiatric disorders. 17 While neuropsychiatric, neurobiological, and genetic factors have been identified that are associated with increased risk, these risk factors, or their combination, have only modest statistical and limited clinical utility, 18 owing to the heterogeneous nature of suicidality. 19 Reported observations of higher frequency of suicidal behavior in monozygotic vs. dizygotic twins across suicide twin survivors but not non-suicide twin survivors suggests potential for genetic susceptibility for suicidal behavior, with twin and family studies indicating heritability estimates for suicidal ideation and behavior ranging from 30–55% 20–22 . Large-scale genome-wide association studies (GWASs) of suicidal ideation, behavior, or death by suicide phenotypes as dichotomized traits have identified putative suicide risk loci for both civilians and military populations 23 – 38 . Of the genome-wide significant loci identified, only a limited number have been replicated to date, namely chromosome 7 as well as genetic variants proximal to the genes LDHB and FAH within European and African American ancestries, respectively 39 . The limited convergence of these GWAS findings is likely owed to the heterogeneity of suicide phenotypes. Identification of more homogeneous suicide phenotypes may potentially improve detection of genetic and etiological factors underpinning suicidal ideation and behavior, which may ultimately aid in the prediction and prevention of future suicide. As suicidal ideation is often the first step in the path toward death by suicide, 40 emerging research on suicide prevention has focused on characterizing various aspects and patterns of suicidal ideation, ranging from transient thoughts that life is not worth living 41 to persistent rumination about dying 42 or preoccupations with self-destruction. 43 Recent studies that combine suicide risk factors with patterns and dynamics of suicidal ideation have revealed distinct suicide risk profiles or subtypes, 44 – 50 though limited in scope due to involving small sample sizes and short timeframes (i.e., days to weeks). These studies adopt approaches such as Latent Profile Analysis (LPA) that consider multiple risk factors at once rather than individual risk factors that contribute to suicide risk as used in traditional statistical approaches. Such approaches are likely to be more informative for suicide risk characterization and assessment, since it is likely that an individual factor predicts relatively little in the way of suicide risk, as many of the associated suicide risk factors are highly correlated and involve complex interactions that contribute to suicidality. 18 From a clinical perspective, as a person-centered rather than variable‐centered statistical approach, probability of group membership can be determined for use in characterization and assessment of suicide risk profiles and subtypes. In the present study we applied LPA using the electronic health records (EHR) data from a large cohort of Veterans who participated in the Million Veteran Program (MVP), representing one of the largest, most diverse biorepositories to identify: (1) distinct suicide ideation (SI) profiles and their clinical and genetic correlates, and (2) test the association of the SI profiles with risk of death by suicide. We hypothesized that different profiles of suicidal ideation differ by disease comorbidities, as well as the risk of subsequent death by suicide. We also hypothesized that the identified SI profiles would be associated with genetic biomarkers that differentiate them from non-suicidal patients and conducted GWAS studies across SI profiles in comparison to non-suicidal patients as controls to identify contributing genetic risk variants associated with specific ideation profiles. These findings can potentially aid in improvements in future suicide risk prediction efforts. 2. MATERIALS AND METHODS All study participants provided informed consent as part of the MVP study. This study was reviewed and approved by the Department of Veterans Affairs (VA) Central Institutional Review Board (Central VA IRB number 18–25). 2.1 Study Participants : MVP study enrolment procedures involving consenting participants, blood collection, linking participants laboratory and genetic information with the VA’s longitudinal EHR, and Baseline and Lifestyle surveys is previously described 51 , 52 . This study used the MVP cohort V18.2 data built including 702,740 participants. 2.2 Suicidal Behavior and Ideation Phenotypes and Associated Comorbid Conditions : For determination of SI profiles, data on suicidal ideation for multiple timepoints were extracted from the mental Health Survey (MHS) derived from the items in the PHQ-9 (MHS Patient Health Questionnaire-9) 53 instrument. Item level scores of PHQ9 for all the participants were extracted, in which item 9 of the PHQ9 (PHQ I9): “thoughts that you would be better off dead, or thoughts of hurting yourself in some way?”, was used to identify subjects endorsing SI. Answers are on a 4-point scale; any nonzero value was used as a positive endorsement of SI for inclusion in analyses. For measure of depression severity, the total score sum of items 1-through-8 was used. Depression severity was coded as missing data if any of these items were missing. Veterans were classified with SI (n = 34,322) if they had at least three observations or time points for PHQ-9 (MHS Patient Health Questionnaire-9) instrument item-9, with nonzero value(s) corresponding to endorsement of suicidal ideation) and at least one observation for PHQ8 as a measure of depression severity and were then used in LPA for identification of SI profiles. Veterans (n = 56,320) were classified as controls referred to as reference group (No-SI), if they had at least three item-9 PHQ I9 observations with no endorsement of SI (i.e., PHQ item-9 = 0). Participant characteristics, and suicide diagnostics and comorbid conditions used in this study are provided in Supplemental Materials. 2.3 Identification of SI Profiles : Statistical analyses for SI subtypes were performed using R 4.0.2 (R Core Team, 2020). The sample consisted of MVP participants (with or without genotype data) with ≥ 3 PHQ-9 observations. Longitudinal PHQ item 9 data were aggregated by subject into four summaries statistics: median ideation, maximum ideation, proportion of zero values, and the root mean successive squared deviation (RMSSD). RMSSD measures of the amplitude of assessment-to-assessment deviations and combines the variance and the autocorrelation of within-subject data. Additionally, median depression severity (sum of PHQ items 1–8) was also calculated. It should be noted that the number of assessment points with PHQ9 data calculated for each subject are not independent from those for SI or behavior, since, in this study, EHR attempters had significantly higher number of PHQ-9 assessment points than EHR non-attempters. The participant cohort was randomly split into training and test sets by 2:1 ratio. Latent Profile Analysis (LPA) was performed on the training sample, then cluster/SI profile reproducibility, measured by in-group proportion (IGP), was calculated within the test sample, using the R libraries “mclust” 54 and “clusterRepro” 55 (see Supplemental Materials for LPA procedure details). The training and testing sets were then combined for all the downstream analyses. The multidimensional cluster centroids of the SI profiles identified via LPA were visualized using the radarchart function from the “fmsb” R package. 2.4 Comparison of disease comorbidity and risk for suicide death across SI profiles : SI profiles were compared based on demographic and diagnostic characteristics with each other and the reference non-ideator group. For continuous variables ANOVA followed by post-hoc Tukey’s HSD test, and for categorical data chi-squared test followed by post-hoc pairwise chi-square test were used with Bonferroni correction to adjust for multiple comparisons. Lifetime Charlson Comorbidity index (CCI) for all participants were computed with both ICD10 and ICD9 codes using the “comorbidity” package in R and compared across the SI profiles and No-SI reference group using robust regression (“rlm” from MASS package) where baseline age and sex were adjusted as covariates. Mortality information for participants across the SI profiles was extracted from the NDI (National Death Index) MVP data release (V23.1). Participants were classified into three groups: Living at the time of data extraction, dead by suicide or dead by other causes than suicide. To assess differences in death across SI profiles, we compared across all groups with age and sex adjusted: (1) in a two-level logistic regression model we the odds of death by suicide vs. being alive or dead by other causes, and (2) in a multinomial logistic regression model, we used a three-level variable describing cause of death and death status variable defined above as the response. 2.5 GWAS analyses of suicidal subtypes : MVP GRCh37 data release was used for the GWAS analyses with 79,105 (87%) available genotypes for the participants in the identified SI profiles. GWAS analyses were performed by regenie v2.2.4 56 for the European (EUR) and African American (AFR) ancestries defined by HARE (Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity 57 within the MVP cohort) separately, and across ancestries via meta-analyses using METAL 58 . Findings were confirmed with previously reported GWAS study from the International Suicide Genetic Consortium (ISGC) suicide attempt 39 on an independent cohort. See Supplementary Information for additional details. GWAS results were summarized and reported using Manhattan plots, and FUMA 59 to identify SNPs to the nearest gene. 3. RESULTS 3.1 Identification and characterization of SI profiles : Age at time of entry into the MVP study for participants included in the present study ranged from 19 to 104, with 13% female Veterans (Table 1 ). Using data from the PHQ-9 instrument that included data on suicidal ideation and depression severity trajectories, LPA identified four ideation profiles. The ideation profiles in order of suicide ideation and secondarily depression severities consisted of mild ideators without depression (M-SI/ND, n = 7,053), mild ideators with depression (M-SI/WD, n = 6,761), variable ideators (V-SI, n = 7,207), and persistent ideators (P-SI, n = 13,301) depicted in Fig. 1 . These ideation profiles were highly reproducible, evaluated using an independent testing dataset measured by in-group proportion scores of > 0.99 for all ideation profiles (Table S2 ). The persistent ideators exhibited the highest suicide risk and depression severity across all dimensions relative to other ideation profiles (Fig. 1 & Table S3 ). The variable ideators had higher maximum ideation score and highest variability for suicidal ideation scores as compared to the persistent ideators, with lower depression severity (Fig. 1 & Table S3 ). The prevalence of suicidal ideation and behavior indicated in the EHR of the variable and persistent ideators were 4.5-and- 7-fold greater compared to the non-ideators reference group respectively (Table 1 ). Also, mild ideators with or without depression both showed approximately 3-fold greater prevalence of suicide ideation and 3.5-fold greater prevalence of suicidal behavior compared to the non-ideator reference group (Table 1 ). Note that the non-ideator reference group itself also included participants with suicidal ideation and behavior indicated on their EHR (Table 1 ). Table 1 Demographic and diagnostic characteristics. Shown are MVP participants who had at least 3 none-missing PHQ i9 records, mean ± sd or count (percentage), by ideation subtype. 247 participants did not have baseline age information. *EHR Comorbid Disorders were extracted from MVP V20.1 where 60 subjects from the SI subtyping cohort were excluded. Statistics for pairwise comparisons with Bonferroni corrected p values are reported in Table S4 . Total No-SI M-SI/ND M-SI/WD V-SI P-SI P-value N 90,642 56,320 7,053 6,761 7,207 13,301 Baseline Age (Year) 1 58.24 ± 13.92 60.40 ± 13.89 58.16 ± 12.92 52.55 ± 13.31 55.24 ± 12.92 53.64 ± 13.12 < .0001 Sex: Female 11,630 (13%) 6,616 (12%) 823 (12%) 1,280 (19%) 1,095 (15%) 1,816 (14%) < .0001 EHR Suicide Diagnosis Attempt/intentional self-harm (SA/SH) 5,205 (6%) 1,338 (2%) 510 (7%) 475 (7%) 922 (13%) 1,960 (15%) < .0001 Ideation (SI) 9, 358 (10%) 2,689 (5%) 990 (14%) 917 (14%) 1,540 (21%) 3,168 (24%) No Indication (No SI/SA) 76,079 (84%) 52,293 (93%) 5,553 (79%) 5,315 (79%) 4,745 (66%) 8,173 (61%) EHR Comorbid Disorders* MDD 54,657 (60%) 26,101 (64%) 5,080 (72%) 5,786 (86%) 6,042 (84%) 11,648 (88%) < .0001 PTSD 44,512 (49%) 20,428 (36%) 3,939 (56%) 5,012 (74%) 4,964 (69%) 10,169 (77%) < .0001 BIP 11,924 (13%) 4,805 (8.5%) 1,127 (16%) 1,273 (19%) 1,687 (23%) 3,032 (23%) < .0001 SCZ 4,602 (5.1%) 1,868 (3.3%) 512 (7.3%) 361 (5.3%) 686 (9.5%) 1,175 (8.8%) < .0001 Psychosis 4,075 (4.5%) 1,697 (3.0%) 425 (6.0%) 327 (4.8%) 580 (8.1%) 1,046 (7.9%) < .0001 Paranoid Disorder 794 (0.9%) 399 (0.6%) 88 (1.2%) 80 (1.2%) 111 (1.5%) 176 (1.3%) < .0001 TBI 3,221 (3.6%) 1,401 (2.5%) 230 (3.3%) 399 (5.9%) 394 (5.5%) 797 (6.0%) < .0001 3.2 Comparison of disease comorbidity and mortality burden for SI subtypes : Prevalence of psychiatric disorders including MDD, PTSD, Schizophrenia, Psychosis, and Bipolar disorder between the four ideation profiles, and compared to the non-ideator reference group were significant, except for the persistent and variable ideators where the prevalence of serious mental illness (i.e., Schizophrenia, Psychosis, and Bipolar disorder) were comparable (Table 1 and Table S4 ). Post-hoc comparisons showed that amongst the SI profiles the mild ideators without depression exhibited the lowest prevalence of MDD and PTSD, followed by variable ideators, and mild ideators with depression, with highest prevalence of MDD and PTSD observed amongst persistent ideators. Also, the prevalence of TBI was significantly lower in the mild ideators without depression than all other ideator subtypes (Table 1 & Table S4 ). Assessments of disease comorbidity using the lifetime Charlson Comorbidity index (CCI) revealed significant differences across the ideation profiles with robust regression adjusting for age and sex (F 4,90329 = 110.42, p < 0.0001). Age and sex adjusted least-squares means of CCI scores for the four ideation profiles corresponded to the following, No-SI = 3.32 (SE = 0.02), M-SI/ND = 3.73 (SE = 0.04), M-SI/WD = 3.73 (SE = 0.04), V-SI = 3.92 (SE = 0.04), and P-SI = 3.72 (SE = 0.03). Post-hoc pairwise analyses showed that all ideation profiles had significantly higher CCI scores compared to the non-ideator reference group (Table S5 ). Age and sex adjusted CCI scores between the ideation profiles also showed that the variable ideators had significantly greater disease burden with higher CCI scores (Table S5 ) than all other SI subtypes. Investigation of ideation profiles by mortality information, specifically death by suicide, identified a total of 212 participants. Across the four ideation profiles, 23 mild ideators without depression (0.40%), 19 mild ideators with depression (0.28%), 22 variable ideators (0.31%), 57 persistent ideators (0.43%) died by suicide. Additionally, a total of 86 participants in the non-ideator reference group (0.15%) died by suicide. Significant differences were detected among the SI profiles with respect to the proportion of suicide deaths (χ² = 33.88, df = 4, p < 0.0001). Specifically, the odds of death by suicide were found to be significantly higher for the mild ideators without depression (OR = 2.49, 95%CI = 1.35–4.59, p = 0.0003) and the persistent ideators (OR = 2.46, 95%CI = 1.51-4.00, p < 0.0001), vs. the No-SI group (Table S6 ). More generally, both suicide and non-suicide death rates differed among subtypes (likelihood ratio test: χ² = 72.31, df = 8, p < 0.0001), adjusted for age and sex. Only variable ideators showed higher odds of death by other causes than suicide compared to non-ideators in post-hoc comparisons (OR = 1.22, 95%CI:1.15–1.31, p < 0.0001, Table S7 ). When excluding deaths by other causes, participants belonging to the variable (OR = 1.86, 95%CI:1.16–2.97, p = 0.0010), persistent (OR = 2.41, 95%CI:1.72–3.39, p < 0.0001), and mild ideation without depression (OR = 2.47, 95%CI:1.62–3.78, p < 0.0001) profiles, but not the mild ideators with depression (OR = 1.52, 95%CI:0.90–2.53, p = 0.1124) showed higher odds of dying by suicide compared to the non-ideator reference group (Table S7 ). 3.3 GWAS analyses and functional mapping across SI profiles : GWAS analyses were performed on the EUR (n = 49,522) and the AFR (n = 16,910) ancestries (Table S8 ) for all ideation profiles. The EUR ancestry GWAS analyses identified genome-wide significant associations for the persistent and mild ideation with depression profiles with SNPs rs72946414 (p = 4.52e-08) and rs1539829 (p = 2.13e-09) respectively in comparison to the non-ideator reference group (Figs. 2 A & B, Table 2 ). Genome-wide significant associations were detected across all SI profiles within the AFR ancestry, including SNPs rs536574477 (p = 5.03e-09, P-SI), rs12062434 (p = 1.50e-08, M-SI/WD), rs139686905 (p = 4.94e-08, V-SI), and rs3127082 (p = 4.42e-08, M-SI/ND), shown in Figs. 3 A-D and Table 2 . Also, GWAS meta-analyses across EUR and AFR ancestries identified two SNPs with genome-wide significance for the variable and mild ideation with depression profiles (V-SI: rs200126785 with p meta = 4.84e-08, and M-SI/WD: rs1539829 with p meta =2.71e-09, see Table 2 ). None of these associations were corroborated by the ISGC GWAS study of suicidal behavior phenotype (using p < 1e-5 for significance threshold). Table 2 Genome-wide significant GWAS results. Shown are Annotations of SI subtyping GWAS hits with p < 5e-8 for EUR, AFR, and meta-analysis. Distance: Distance to the nearest gene. SNPs which are locating in the gene body or 1kb up- or down-stream of TSS or TES have 0. GWAS CHR BP A1:A2 SNP p-value Beta (SE) Nearest gene Function Distance EUR P-SI 3 102039837 A:G rs72946414 4.52e-08 -0.80 (0.15) ZPLD1 intronic 0 M-SI/WD 18 22557164 C:T rs1539829 2.13e-09 0.20 (0.03) LINC01894 (RP11-958F21.1) ncRNA intronic 0 AFR P-SI 10 68343057 TTTG:TTTGTTG rs536574477 5.03e-09 0.21 (0.04) CTNNA3 intronic 0 M-SI/WD 1 165374688 A:G rs12062434 1.50e-08 -1.07 (0.21) RXRG intronic 0 V-SI 19 54252936 A:G rs139686905 4.94e-08 0.58 (0.10) RNU6-751P downstream 168 M-SI/ND 10 115510667 C:G rs3127082 4.42e-08 0.28 (0.05) PLEKHS1 upstream 545 Meta-analysis M-SI/WD 18 22557164 C:T rs1539829 2.71e-09 -0.20 (0.03) LINC01894 (RP11-958F21.1) ncRNA intronic 0 V-SI 9 132912691 G:GC rs200126785 4.84e-08 0.28 (0.05) AL360004.1 intergenic 6,192 4. DISCUSSION In the present study we examined suicidal ideation patterns within a large cohort of Veterans who participated in the MVP. We found evidence of four distinct ideation profiles with differential associated clinical presentations including comorbid psychiatric and medical conditions, history of suicidal behavior, and, most importantly, differential proportion for dying by suicide. Specifically, we found two SI profiles (persistent and variable ideator profiles) with the highest maximum suicidal ideation as well as elevated risk for comorbid conditions including serious mental illness, with persistent ideators also having the highest prevalence of depression and PTSD. Participants belonging to the variable ideators profile also had higher risk for other comorbid conditions as measured by the CCI, and, importantly, higher odds of dying by other causes than suicide. Compared to the non-ideator reference group, history of suicidal ideation and behavior were significantly greater amongst all SI profiles regardless of depression severity. Surprisingly, participants with mild ideation without depression had the highest odds of death by suicide. Our study used similar summary SI indices as a previous studies. 48 , 60 that used LPA for both short- and long-term data collected on suicidal ideation, which have identified similar ideation profiles as the present study including persistent, variable, and mild. 44 – 50 , 61 The persistent and variable ideation profiles are in line with suicidal subtypes initially suggested by Bernanke et al. 61 According to their model there are at least two suicidal subtypes: the variable subtype that is characterized by large fluctuations in severity of suicidal thoughts in response to life stress, and the persistent subtype that is characterized by persistent levels of suicidal thinking that do not fluctuate in response to life events. The present study did not assess associations in suicidal ideation variability with stress, since we did not have concomitant data on suicidal ideation and life stressors amongst the MVP participants. Nevertheless, it is likely that the participants belonging to the variable ideation profile identified herein may have higher life stressors, attributed to their higher prevalence of comorbid serious mental illness and disease burden compared to the other ideation profiles. Given the high co-morbidity between stress and psychiatric disorders, 62 – 66 it is likely that life stressors (though not measured directly herein) contribute to fluctuations in suicidal ideation. In contrast we found that the persistent SI subtype, the non-stress responsive type according to Bernanke et al., 61 was similarly characterized by persistent and more severe suicidal ideation and worse depression as compared to other ideation profiles. Previous studies investigating the trajectories of SI using ecological momentary assessment have also identified ideation profiles that differed with respect to severity and variability of real‐time suicidal ideation, 47 , 49 including subtypes with mild ideation consistent with our findings of mild ideators with or without depression. Kleiman et al. 49 described these ideation profiles as potential subtypes of the two superordinate SI subtypes i.e., the variable and persistent subtypes. Investigations of the suicidal ideation profiles with regards to death by suicide revealed a surprising finding. Extending our analysis in linking the SI profiles to long-term data on suicide death from the National Death Index showed that participants with mild ideation and no depression were at the highest odds of death by suicide. 67 It is likely that these non-depressed Veterans with mild ideation are not identified to be at-risk in the VA, and are not flagged as such in suicide screenings via the VHA universal Risk ID suicide screening mandate using the Columbia Suicide Severity Rating Scale (CSSRS) screener. 68 , 69 Indeed, a study that analyzed item level CSSRS surveys from patients’ electronic health records showed that positive endorsement of suicidal ideation was an indicator of risk of future suicidal behavior over the next 30 days, specifically > 8-fold for passive and > 2-fold for active ideation 70 , in line with previous findings showing majority of patients report only non-acute indicators of risk that are not indicative of preparatory acts or suicidal behavior 71 . Our GWAS analyses identified novel genetic variants associated with specific ideation profiles, spanning both coding and non-coding regions of the genome. For both the EUR and AFR ancestries, we found potential risk variants for the persistent and mild with depression profiles including, for EUR, rs72946414 and rs1539289, localized within the gene ZPLD1 and the long non-coding transcript LINC01894 respectively, and for AFR, rs536574477 and rs12062434, localized within the genes CTNNA3 and RXRG respectively (Table 2 ). The ZPLD1 (zona pellucida like domain containing 1) gene product is a glycoprotein expressed in the cupula, a gelatinous structure localized in the inner ear that is involved in vestibular functioning. 72 It is possible that participating Veterans with potential risk variants in the ZPLD1 gene may be more susceptible to adverse outcomes following injuries to the cupula, specifically auditory and vestibular injuries incurred during military operations, attributed to excessive exposures to noise and blast overpressure that often result in tinnitus, 73 , 74 associated with suicidality. 75 – 77 The CTNNA (catenin alpha 3) gene product is a cell-cell adhesion protein expressed in a variety of tissues including the brain. GWAS studies have identified genetic variants across the CTNNA3 gene that associate with Autism, 78 type 2 diabetes, 79 Alzheimer’s disease, 80 and Asthma, 81 disorders that contribute to increased risk for suicide. 82 – 84 GWAS studies have also identified genetic variants within the RXRG (retinoid X receptor gamma) gene with type 2 diabetes. 85 , 86 The gene RXRG is a member of the retinoid X receptor family of nuclear receptors that mediates proliferation/accumulation of retinoic acid and is expressed in the brain. It plays a critical role in white matter repair and remyelination and has been shown to be involved in oligodendrocyte precursor cell differentiation. 87 This is particularly interesting given that oligodendrocytes play a key role in myelination 88 and CNS inflammation, wherein in postmortem suicide studies oligodendrocytes have been shown to exhibit functional impairments as well as depletion in cell population. 89 90, 91 Oligodendrocytes represent an important neurobiological substrate, contributing to the altered connectivity and white matter integrity, supported also by findings from imaging studies in suicide including in vivo studies of the frontal lobe in depression and suicidality revealing disrupted brain connectivity in grey and white matter of those who attempt or die by suicide 92 . Moreover, for the AFR ancestry, we identified risk variants associated with variable and mild without depression ideation profiles, consisting of rs139686905, downstream of the RNA U6 small nuclear gene ( RNU6-751P ) and rs3127082, upstream of the pleckstrin homology domain containing S1 gene ( PLEKHS1 ), both loci implicated in cancer. 93 – 96 Finally, meta-analyses of the EUR and AFR ancestries identified risk variants for the mild with depression and variable ideation profiles consisting of rs1539829 and rs200126785, proximal to two noncoding transcripts, LINC01894 and AL360004 .1, respectively. Converging evidence from post-mortem brains and clinical samples points to the importance of non-coding RNA in Depression 97 and, of relevance to the present study, suicidality. 98 – 100 This study has several limitations: the PHQ-9 instrument from which the depression and suicide severity variables were derived for LPA is a self-report assessment instrument, thus findings may be affected by under reporting related to sensitivity concerns in endorsing suicidal thoughts and behavior. Data on depression and suicidal ideation severity were aggregated rather than examined longitudinally, for example via data collections using ecological momentary assessments to track daily fluctuations in computation of summary indices. Future investigations of clinical symptoms over time longitudinally would allow for studying trajectories of symptom severity related to suicide risk over time and in identifying key constructs that affect these trajectories. Although the MVP data provides opportunity to examine long periods instead of daily symptom changes, this may still be highly valuable to identify ideation patterns with clinical relevance, as longer periods match real-life assessment opportunities better, in line with medical appointment visits. Finally, the study population are military Veterans, and as such it is possible that findings will not generalize to the civilian population. In conclusion, the ideation profiles identified in the present study may provide useful information for testing whether different profiles would have different characteristics, courses, and genetic signatures, important in future investigations of targeted interventions; Thus, fostering development of novel personalized interventions across the suicide risk continuum. Declarations CONFLICT OF INTEREST: Dr. Haghighi, Dr. Pyarajan, Mr. Dochtermann, and Ms. Sun reported no biomedical financial interests or potential conflicts of interest. Dr. Galfalvy and her family own stocks in Illumina, Inc. ACKNOWLEDGEMENTS: This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by MVP000, award #MVP023 to F.H, and #MVP000 supporting S.P. and D.D.’s work. Fatemeh Haghighi, PhD., a recipient of the VA CSR&D Research Career Scientist Award (CX002074) and her laboratory and work is supported by CX001728, BX006069, BX003794, and RX003818 at the James J. Peters VA Medical Center. This publication does not represent the views of the Department of Veteran Affairs or the United States Government. DATA AVAILABILITY The data underlying this publication are accessible to researchers with Million Veteran Program (MVP) data access. Summary statistics will be made publicly available on dbGAP. References Suicide. https://www.nimh.nih.gov/health/statistics/suicide, 2024, Accessed Date Accessed 2024 Accessed. 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Additional Declarations Yes Dr. Galfalvy discloses owning stock in Illumina, Inc. Supplementary Files MVPSuicideSubtypeSupplementalInformation.docx Supplemental Information TableS1.xlsx Table S1 TableS2.xlsx Table S2 TableS3.xlsx Table S3 TableS4.xlsx Table S4 TableS5.xlsx Table S5 TableS6.xlsx Table S6 TableS7.xlsx Table S7 MVPSuicideSubtypesupplementarytablesss.docx Supplementary Table Legends Cite Share Download PDF Status: Published Journal Publication published 02 Dec, 2025 Read the published version in Molecular Psychiatry → Version 1 posted Editorial decision: revise 11 Apr, 2025 Review # 3 received at journal 24 Mar, 2025 Review # 1 received at journal 24 Mar, 2025 Reviewer # 4 agreed at journal 12 Mar, 2025 Reviewer # 3 agreed at journal 10 Mar, 2025 Reviewer # 2 agreed at journal 04 Mar, 2025 Reviewer # 1 agreed at journal 03 Mar, 2025 Reviewers invited by journal 03 Mar, 2025 Editor assigned by journal 21 Feb, 2025 Submission checks completed at journal 21 Feb, 2025 First submitted to journal 20 Feb, 2025 Unknown event 20 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6066944","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441592886,"identity":"662cd01b-4463-42bd-be8f-2e62050bc9ce","order_by":0,"name":"Fatemeh Haghighi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBAC9gYwdYDBgIGBjSGB4QAPP1SGsQGHFp4DzGhaJBtI0gJmHCCkhf38wUc3Ku4wmLMfv/bgwa87MsbXjj/+dIPBRnbDARxaeJKZjXPOPGOw7MkpN0jse8ZjdjvHTDqHIc0YlxZ7hmQ26dy2w0D35KRJJPYcBmlhY85hOJyI0xb+x+y/c/8BtZx/A9FiPDv98ecchv+4tUgkszHnNgC13Eg/JpHw4zCPgXSCAdBhB/BoeWwsnXMMqPLGGzaJxIbDPBJgvxgkG8/E6bDEh59zag7LGZxPfyb5489he36wwyrsZPtwaIFrBSIDBsY2GN8Av3IoYH/AwPCHKJWjYBSMglEwwgAAqkpnfUKldn4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0920-6956","institution":"Icahn School of Medicine at Mount Sinai/James J. 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The five cluster centroids statistics including: median ideation (SI Median), maximum ideation (SI Max), proportion of none-zero values (SI Frequency), the root mean successive squared deviation (SI Variability), and median depression severity (Depression Median). SI Subtypes were colored in green (Mild ideators without depression, M-SI/ND), blue (Mild ideators with depression, M-SI/WD), red (variable ideators, V-SI), and yellow (persistent ideators, P-SI) correspondingly.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/791cdff23eeb217b081ab22b.jpg"},{"id":83215390,"identity":"845212dc-317c-4b98-b56e-ab974b3e690e","added_by":"auto","created_at":"2025-05-21 09:00:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":523707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEUR GWAS Manhattan plots.\u003c/strong\u003e Plots show results of the EUR ancestry comparing each SI subtype with the No-SI reference group including: A. P-SI vs. No-SI, B. M-SI/WD vs. No-SI, C. V-SI vs. No-SI, and D. M-SI/ND vs. No-SI. The red line corresponds to the genome-wide significant threshold(p \u0026lt; 5e-8).\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/52ce3633f00a4bfc7873ab28.jpg"},{"id":83215395,"identity":"5ec6e525-43fb-47eb-aa3f-cca2c8a9ee68","added_by":"auto","created_at":"2025-05-21 09:00:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":559679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAFR GWAS Manhattan plots.\u003c/strong\u003e Plots show results of AFR ancestry comparing each SI subtype with the No-SI reference group including: A. P-SI vs. No-SI, B. M-SI/WD vs. No-SI, C. V-SI vs. No-SI, and D. M-SI/ND vs. No-SI. The red line corresponds to the genome-wide significant threshold (p \u0026lt; 5e-8).\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/419e02a9ec0b060cd6d5ac73.jpg"},{"id":97322799,"identity":"70559223-570a-430e-ac18-d2fda8230178","added_by":"auto","created_at":"2025-12-03 08:12:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2357098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/f98d7ae6-7805-44aa-8aa3-5a244f88d06b.pdf"},{"id":83215384,"identity":"e80a2108-ec68-44d0-8fec-0c4d7c086eff","added_by":"auto","created_at":"2025-05-21 09:00:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":48591,"visible":true,"origin":"","legend":"Supplemental Information","description":"","filename":"MVPSuicideSubtypeSupplementalInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/96075b3bbcc7f74348dd1edc.docx"},{"id":83215385,"identity":"41b342d1-314a-4459-a23d-73b25bea7392","added_by":"auto","created_at":"2025-05-21 09:00:35","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13858,"visible":true,"origin":"","legend":"Table S1","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/60ea5438528210f724a571ab.xlsx"},{"id":83215387,"identity":"e5032dc7-80c7-421c-be1b-2c112dac297d","added_by":"auto","created_at":"2025-05-21 09:00:35","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9443,"visible":true,"origin":"","legend":"Table S2","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/850c5f97d23e2bccbb321e90.xlsx"},{"id":83216164,"identity":"3f2bc999-fc57-44bd-ac70-57b9759e3ef2","added_by":"auto","created_at":"2025-05-21 09:08:35","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10321,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/fb7a938974906d80d32606d9.xlsx"},{"id":83216166,"identity":"2e9a55b0-7f5d-4c8d-88b0-2f5c68f5daa5","added_by":"auto","created_at":"2025-05-21 09:08:35","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10032,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4\u003c/p\u003e","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/d137ff9b0d40e699805cf1f2.xlsx"},{"id":83216163,"identity":"762e7113-6b89-473f-a260-be078d34a73c","added_by":"auto","created_at":"2025-05-21 09:08:35","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9713,"visible":true,"origin":"","legend":"\u003cp\u003eTable S5\u003c/p\u003e","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/3bc887cb9e16ed9c73257d76.xlsx"},{"id":83216165,"identity":"8f4a213d-a38e-442e-b64c-73a19e021a37","added_by":"auto","created_at":"2025-05-21 09:08:35","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":9702,"visible":true,"origin":"","legend":"\u003cp\u003eTable S6\u003c/p\u003e","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/3e9d02823da4aa39cafb1a90.xlsx"},{"id":83215410,"identity":"9f9b9afe-3250-4bdf-bbe6-3a23c3c0d92e","added_by":"auto","created_at":"2025-05-21 09:00:35","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10414,"visible":true,"origin":"","legend":"\u003cp\u003eTable S7\u003c/p\u003e","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/d598a50ad8f2f5c6f77d970a.xlsx"},{"id":83215399,"identity":"a2109d23-6f86-4a43-bc4f-0b3436f5435c","added_by":"auto","created_at":"2025-05-21 09:00:35","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":20000,"visible":true,"origin":"","legend":"Supplementary Table Legends","description":"","filename":"MVPSuicideSubtypesupplementarytablesss.docx","url":"https://assets-eu.researchsquare.com/files/rs-6066944/v1/a676cae69b66db343335085f.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e\nDr. Galfalvy discloses owning stock in Illumina, Inc.","formattedTitle":"Genetic Architecture of Suicidal Ideation Continuum: Latent Profile Analysis of Data using the Million Veteran Program Cohort","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSuicide is a major public health concern, representing the 11th highest cause of death in the United States with approximately 48,000 individuals dying by suicide in 2021.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Among US Veterans, suicide rates are alarmingly higher than among civilians: in 2021 the age- and sex-adjusted rate for Veterans was 71.8% greater than that of non-Veteran adults.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In 2020, 4.9% of US adults reported suicide ideation, 1.3% suicide planning, and 0.5% attempting suicide.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Prior history of suicide ideation and attempt is a predictor of future death by suicide. Death by suicide is 30\u0026ndash;40 times greater in those who have previously attempted suicide relative to the general population\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e with population based studies showing that the majority of suicide attempters endorse suicidal ideation.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Individuals endorsing suicidal ideation exhibit a 4-fold greater risk for death by suicide compared to non-ideators.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Thus, a better understanding of the suicidal ideation risk continuum is crucial to improving prediction and may aid in prospectively identifying those at risk.\u003c/p\u003e \u003cp\u003eA confluence of factors can contribute to suicide risk, including the interaction of a variety of biological, clinical, psychological, social, cultural and environmental factors, which has led to the observed etiological heterogeneity of suicidality.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The presence of a psychiatric disorder including serious mental illness (e.g., Schizophrenia and Bipolar disorder), anxiety and PTSD, and particularly Major Depressive disorder are well-established risk factors for suicide.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Indeed, 90% of suicides have a mental disorder,\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e with those with depressive disorders being at 70% greater risk of death by suicide than the general population,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e as corroborated by psychological autopsy studies showing 60% of suicides occurred in Major Depressive episode.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Also, patients with depression are more likely to attempt suicide than patients with other psychiatric disorders,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e underscoring the importance of investigating suicidality in the context of depression. Overwhelming evidence, however, indicates that suicidal thoughts and behavior are transdiagnostic phenomena that can manifest in the absence of psychiatric disorders.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e While neuropsychiatric, neurobiological, and genetic factors have been identified that are associated with increased risk, these risk factors, or their combination, have only modest statistical and limited clinical utility,\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e owing to the heterogeneous nature of suicidality.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eReported observations of higher frequency of suicidal behavior in monozygotic vs. dizygotic twins across suicide twin survivors but not non-suicide twin survivors suggests potential for genetic susceptibility for suicidal behavior, with twin and family studies indicating heritability estimates for suicidal ideation and behavior ranging from 30\u0026ndash;55%\u003csup\u003e20\u0026ndash;22\u003c/sup\u003e. Large-scale genome-wide association studies (GWASs) of suicidal ideation, behavior, or death by suicide phenotypes as dichotomized traits have identified putative suicide risk loci for both civilians and military populations\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Of the genome-wide significant loci identified, only a limited number have been replicated to date, namely chromosome 7 as well as genetic variants proximal to the genes \u003cem\u003eLDHB\u003c/em\u003e and \u003cem\u003eFAH\u003c/em\u003e within European and African American ancestries, respectively\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The limited convergence of these GWAS findings is likely owed to the heterogeneity of suicide phenotypes. Identification of more homogeneous suicide phenotypes may potentially improve detection of genetic and etiological factors underpinning suicidal ideation and behavior, which may ultimately aid in the prediction and prevention of future suicide.\u003c/p\u003e \u003cp\u003eAs suicidal ideation is often the first step in the path toward death by suicide,\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e emerging research on suicide prevention has focused on characterizing various aspects and patterns of suicidal ideation, ranging from transient thoughts that life is not worth living\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e to persistent rumination about dying\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e or preoccupations with self-destruction.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Recent studies that combine suicide risk factors with patterns and dynamics of suicidal ideation have revealed distinct suicide risk profiles or subtypes,\u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47 CR48 CR49\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e though limited in scope due to involving small sample sizes and short timeframes (i.e., days to weeks). These studies adopt approaches such as Latent Profile Analysis (LPA) that consider multiple risk factors at once rather than individual risk factors that contribute to suicide risk as used in traditional statistical approaches. Such approaches are likely to be more informative for suicide risk characterization and assessment, since it is likely that an individual factor predicts relatively little in the way of suicide risk, as many of the associated suicide risk factors are highly correlated and involve complex interactions that contribute to suicidality.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e From a clinical perspective, as a person-centered rather than variable‐centered statistical approach, probability of group membership can be determined for use in characterization and assessment of suicide risk profiles and subtypes. In the present study we applied LPA using the electronic health records (EHR) data from a large cohort of Veterans who participated in the Million Veteran Program (MVP), representing one of the largest, most diverse biorepositories to identify: (1) distinct suicide ideation (SI) profiles and their clinical and genetic correlates, and (2) test the association of the SI profiles with risk of death by suicide.\u003c/p\u003e \u003cp\u003eWe hypothesized that different profiles of suicidal ideation differ by disease comorbidities, as well as the risk of subsequent death by suicide. We also hypothesized that the identified SI profiles would be associated with genetic biomarkers that differentiate them from non-suicidal patients and conducted GWAS studies across SI profiles in comparison to non-suicidal patients as controls to identify contributing genetic risk variants associated with specific ideation profiles. These findings can potentially aid in improvements in future suicide risk prediction efforts.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003eAll study participants provided informed consent as part of the MVP study. This study was reviewed and approved by the Department of Veterans Affairs (VA) Central Institutional Review Board (Central VA IRB number 18\u0026ndash;25).\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.1 Study Participants\u003c/b\u003e: MVP study enrolment procedures involving consenting participants, blood collection, linking participants laboratory and genetic information with the VA\u0026rsquo;s longitudinal EHR, and Baseline and Lifestyle surveys is previously described\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. This study used the MVP cohort V18.2 data built including 702,740 participants.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Suicidal Behavior and Ideation Phenotypes and Associated Comorbid Conditions\u003c/b\u003e: For determination of SI profiles, data on suicidal ideation for multiple timepoints were extracted from the mental Health Survey (MHS) derived from the items in the PHQ-9 (MHS Patient Health Questionnaire-9)\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e instrument. Item level scores of PHQ9 for all the participants were extracted, in which item 9 of the PHQ9 (PHQ I9): \u0026ldquo;thoughts that you would be better off dead, or thoughts of hurting yourself in some way?\u0026rdquo;, was used to identify subjects endorsing SI. Answers are on a 4-point scale; any nonzero value was used as a positive endorsement of SI for inclusion in analyses. For measure of depression severity, the total score sum of items 1-through-8 was used. Depression severity was coded as missing data if any of these items were missing. Veterans were classified with SI (n\u0026thinsp;=\u0026thinsp;34,322) if they had at least three observations or time points for PHQ-9 (MHS Patient Health Questionnaire-9) instrument item-9, with nonzero value(s) corresponding to endorsement of suicidal ideation) and at least one observation for PHQ8 as a measure of depression severity and were then used in LPA for identification of SI profiles. Veterans (n\u0026thinsp;=\u0026thinsp;56,320) were classified as controls referred to as reference group (No-SI), if they had at least three item-9 PHQ I9 observations with no endorsement of SI (i.e., PHQ item-9\u0026thinsp;=\u0026thinsp;0). Participant characteristics, and suicide diagnostics and comorbid conditions used in this study are provided in Supplemental Materials.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Identification of SI Profiles\u003c/b\u003e: Statistical analyses for SI subtypes were performed using R 4.0.2 (R Core Team, 2020). The sample consisted of MVP participants (with or without genotype data) with \u0026ge;\u0026thinsp;3 PHQ-9 observations. Longitudinal PHQ item 9 data were aggregated by subject into four summaries statistics: median ideation, maximum ideation, proportion of zero values, and the root mean successive squared deviation (RMSSD). RMSSD measures of the amplitude of assessment-to-assessment deviations and combines the variance and the autocorrelation of within-subject data. Additionally, median depression severity (sum of PHQ items 1\u0026ndash;8) was also calculated. It should be noted that the number of assessment points with PHQ9 data calculated for each subject are not independent from those for SI or behavior, since, in this study, EHR attempters had significantly higher number of PHQ-9 assessment points than EHR non-attempters. The participant cohort was randomly split into training and test sets by 2:1 ratio. Latent Profile Analysis (LPA) was performed on the training sample, then cluster/SI profile reproducibility, measured by in-group proportion (IGP), was calculated within the test sample, using the R libraries \u0026ldquo;mclust\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e and \u0026ldquo;clusterRepro\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e (see Supplemental Materials for LPA procedure details). The training and testing sets were then combined for all the downstream analyses. The multidimensional cluster centroids of the SI profiles identified via LPA were visualized using the radarchart function from the \u0026ldquo;fmsb\u0026rdquo; R package.\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.4 Comparison of disease comorbidity and risk for suicide death across SI profiles\u003c/b\u003e: SI profiles were compared based on demographic and diagnostic characteristics with each other and the reference non-ideator group. For continuous variables ANOVA followed by post-hoc Tukey\u0026rsquo;s HSD test, and for categorical data chi-squared test followed by post-hoc pairwise chi-square test were used with Bonferroni correction to adjust for multiple comparisons. Lifetime Charlson Comorbidity index (CCI) for all participants were computed with both ICD10 and ICD9 codes using the \u0026ldquo;comorbidity\u0026rdquo; package in R and compared across the SI profiles and No-SI reference group using robust regression (\u0026ldquo;rlm\u0026rdquo; from MASS package) where baseline age and sex were adjusted as covariates. Mortality information for participants across the SI profiles was extracted from the NDI (National Death Index) MVP data release (V23.1). Participants were classified into three groups: Living at the time of data extraction, dead by suicide or dead by other causes than suicide. To assess differences in death across SI profiles, we compared across all groups with age and sex adjusted: (1) in a two-level logistic regression model we the odds of death by suicide vs. being alive or dead by other causes, and (2) in a multinomial logistic regression model, we used a three-level variable describing cause of death and death status variable defined above as the response.\u003c/p\u003e\u003cp\u003e \u003cb\u003e2.5 GWAS analyses of suicidal subtypes\u003c/b\u003e: MVP GRCh37 data release was used for the GWAS analyses with 79,105 (87%) available genotypes for the participants in the identified SI profiles. GWAS analyses were performed by regenie v2.2.4\u003csup\u003e56\u003c/sup\u003e for the European (EUR) and African American (AFR) ancestries defined by HARE (Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity\u003csup\u003e57\u003c/sup\u003e within the MVP cohort) separately, and across ancestries via meta-analyses using METAL\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Findings were confirmed with previously reported GWAS study from the International Suicide Genetic Consortium (ISGC) suicide attempt\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e on an independent cohort. See Supplementary Information for additional details. GWAS results were summarized and reported using Manhattan plots, and FUMA\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e to identify SNPs to the nearest gene.\u003c/p\u003e"},{"header":"3. RESULTS","content":" \u003cp\u003e \u003cb\u003e3.1 Identification and characterization of SI profiles\u003c/b\u003e: Age at time of entry into the MVP study for participants included in the present study ranged from 19 to 104, with 13% female Veterans (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Using data from the PHQ-9 instrument that included data on suicidal ideation and depression severity trajectories, LPA identified four ideation profiles. The ideation profiles in order of suicide ideation and secondarily depression severities consisted of mild ideators without depression (M-SI/ND, n\u0026thinsp;=\u0026thinsp;7,053), mild ideators with depression (M-SI/WD, n\u0026thinsp;=\u0026thinsp;6,761), variable ideators (V-SI, n\u0026thinsp;=\u0026thinsp;7,207), and persistent ideators (P-SI, n\u0026thinsp;=\u0026thinsp;13,301) depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These ideation profiles were highly reproducible, evaluated using an independent testing dataset measured by in-group proportion scores of \u0026gt;\u0026thinsp;0.99 for all ideation profiles (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The persistent ideators exhibited the highest suicide risk and depression severity across all dimensions relative to other ideation profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The variable ideators had higher maximum ideation score and highest variability for suicidal ideation scores as compared to the persistent ideators, with lower depression severity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). The prevalence of suicidal ideation and behavior indicated in the EHR of the variable and persistent ideators were 4.5-and- 7-fold greater compared to the non-ideators reference group respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Also, mild ideators with or without depression both showed approximately 3-fold greater prevalence of suicide ideation and 3.5-fold greater prevalence of suicidal behavior compared to the non-ideator reference group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Note that the non-ideator reference group itself also included participants with suicidal ideation and behavior indicated on their EHR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e \u003cb\u003eDemographic and diagnostic characteristics.\u003c/b\u003e Shown are MVP participants who had at least 3 none-missing PHQ i9 records, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd or count (percentage), by ideation subtype. 247 participants did not have baseline age information. *EHR Comorbid Disorders were extracted from MVP V20.1 where 60 subjects from the SI subtyping cohort were excluded. Statistics for pairwise comparisons with Bonferroni corrected p values are reported in Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo-SI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM-SI/ND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eM-SI/WD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV-SI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-SI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90,642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13,301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Age (Year)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.24\u0026thinsp;\u0026plusmn;\u0026thinsp;13.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.40\u0026thinsp;\u0026plusmn;\u0026thinsp;13.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.16 \u0026plusmn;\u003c/p\u003e \u003cp\u003e12.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.55\u0026thinsp;\u0026plusmn;\u0026thinsp;13.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.24\u0026thinsp;\u0026plusmn;\u0026thinsp;12.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.64\u0026thinsp;\u0026plusmn;\u0026thinsp;13.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex: Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,630\u003c/p\u003e \u003cp\u003e(13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,616\u003c/p\u003e \u003cp\u003e(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e823\u003c/p\u003e \u003cp\u003e(12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,280 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,095\u003c/p\u003e \u003cp\u003e(15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,816 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eEHR Suicide Diagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttempt/intentional self-harm (SA/SH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,205\u003c/p\u003e \u003cp\u003e(6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,338\u003c/p\u003e \u003cp\u003e(2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e510\u003c/p\u003e \u003cp\u003e(7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e475\u003c/p\u003e \u003cp\u003e(7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e922\u003c/p\u003e \u003cp\u003e(13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,960 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdeation (SI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9, 358\u003c/p\u003e \u003cp\u003e(10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,689\u003c/p\u003e \u003cp\u003e(5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e990\u003c/p\u003e \u003cp\u003e(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e917\u003c/p\u003e \u003cp\u003e(14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003cp\u003e(21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,168 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Indication\u003c/p\u003e \u003cp\u003e(No SI/SA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76,079\u003c/p\u003e \u003cp\u003e(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52,293\u003c/p\u003e \u003cp\u003e(93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,553\u003c/p\u003e \u003cp\u003e(79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,315 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,745\u003c/p\u003e \u003cp\u003e(66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8,173 (61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eEHR Comorbid Disorders*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54,657 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,101 (64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,080\u003c/p\u003e \u003cp\u003e(72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,786 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6,042\u003c/p\u003e \u003cp\u003e(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,648 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,512 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,428 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,939\u003c/p\u003e \u003cp\u003e(56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,012 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,964\u003c/p\u003e \u003cp\u003e(69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10,169 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,924 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,805 (8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,127\u003c/p\u003e \u003cp\u003e(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,273 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,687\u003c/p\u003e \u003cp\u003e(23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,032 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,602\u003c/p\u003e \u003cp\u003e(5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,868 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e512\u003c/p\u003e \u003cp\u003e(7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e361\u003c/p\u003e \u003cp\u003e(5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e686\u003c/p\u003e \u003cp\u003e(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,175 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,075\u003c/p\u003e \u003cp\u003e(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,697\u003c/p\u003e \u003cp\u003e(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e425\u003c/p\u003e \u003cp\u003e(6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e327\u003c/p\u003e \u003cp\u003e(4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e580\u003c/p\u003e \u003cp\u003e(8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,046\u003c/p\u003e \u003cp\u003e(7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParanoid Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e794\u003c/p\u003e \u003cp\u003e(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e399\u003c/p\u003e \u003cp\u003e(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003cp\u003e(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003cp\u003e(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111\u003c/p\u003e \u003cp\u003e(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e176\u003c/p\u003e \u003cp\u003e(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,221\u003c/p\u003e \u003cp\u003e(3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,401 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e230\u003c/p\u003e \u003cp\u003e(3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e399\u003c/p\u003e \u003cp\u003e(5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e394\u003c/p\u003e \u003cp\u003e(5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e797 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Comparison of disease comorbidity and mortality burden for SI subtypes\u003c/b\u003e: Prevalence of psychiatric disorders including MDD, PTSD, Schizophrenia, Psychosis, and Bipolar disorder between the four ideation profiles, and compared to the non-ideator reference group were significant, except for the persistent and variable ideators where the prevalence of serious mental illness (i.e., Schizophrenia, Psychosis, and Bipolar disorder) were comparable (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eand Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Post-hoc comparisons showed that amongst the SI profiles the mild ideators without depression exhibited the lowest prevalence of MDD and PTSD, followed by variable ideators, and mild ideators with depression, with highest prevalence of MDD and PTSD observed amongst persistent ideators. Also, the prevalence of TBI was significantly lower in the mild ideators without depression than all other ideator subtypes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u0026amp; Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Assessments of disease comorbidity using the lifetime Charlson Comorbidity index (CCI) revealed significant differences across the ideation profiles with robust regression adjusting for age and sex (F\u003csub\u003e4,90329\u003c/sub\u003e = 110.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Age and sex adjusted least-squares means of CCI scores for the four ideation profiles corresponded to the following, No-SI\u0026thinsp;=\u0026thinsp;3.32 (SE\u0026thinsp;=\u0026thinsp;0.02), M-SI/ND\u0026thinsp;=\u0026thinsp;3.73 (SE\u0026thinsp;=\u0026thinsp;0.04), M-SI/WD\u0026thinsp;=\u0026thinsp;3.73 (SE\u0026thinsp;=\u0026thinsp;0.04), V-SI\u0026thinsp;=\u0026thinsp;3.92 (SE\u0026thinsp;=\u0026thinsp;0.04), and P-SI\u0026thinsp;=\u0026thinsp;3.72 (SE\u0026thinsp;=\u0026thinsp;0.03). Post-hoc pairwise analyses showed that all ideation profiles had significantly higher CCI scores compared to the non-ideator reference group (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Age and sex adjusted CCI scores between the ideation profiles also showed that the variable ideators had significantly greater disease burden with higher CCI scores (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e) than all other SI subtypes.\u003c/p\u003e \u003cp\u003eInvestigation of ideation profiles by mortality information, specifically death by suicide, identified a total of 212 participants. Across the four ideation profiles, 23 mild ideators without depression (0.40%), 19 mild ideators with depression (0.28%), 22 variable ideators (0.31%), 57 persistent ideators (0.43%) died by suicide. Additionally, a total of 86 participants in the non-ideator reference group (0.15%) died by suicide. Significant differences were detected among the SI profiles with respect to the proportion of suicide deaths (χ\u0026sup2; = 33.88, df\u0026thinsp;=\u0026thinsp;4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Specifically, the odds of death by suicide were found to be significantly higher for the mild ideators without depression (OR\u0026thinsp;=\u0026thinsp;2.49, 95%CI\u0026thinsp;=\u0026thinsp;1.35\u0026ndash;4.59, p\u0026thinsp;=\u0026thinsp;0.0003) and the persistent ideators (OR\u0026thinsp;=\u0026thinsp;2.46, 95%CI\u0026thinsp;=\u0026thinsp;1.51-4.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), vs. the No-SI group (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). More generally, both suicide and non-suicide death rates differed among subtypes (likelihood ratio test: χ\u0026sup2; = 72.31, df\u0026thinsp;=\u0026thinsp;8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), adjusted for age and sex. Only variable ideators showed higher odds of death by other causes than suicide compared to non-ideators in post-hoc comparisons (OR\u0026thinsp;=\u0026thinsp;1.22, 95%CI:1.15\u0026ndash;1.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). When excluding deaths by other causes, participants belonging to the variable (OR\u0026thinsp;=\u0026thinsp;1.86, 95%CI:1.16\u0026ndash;2.97, p\u0026thinsp;=\u0026thinsp;0.0010), persistent (OR\u0026thinsp;=\u0026thinsp;2.41, 95%CI:1.72\u0026ndash;3.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and mild ideation without depression (OR\u0026thinsp;=\u0026thinsp;2.47, 95%CI:1.62\u0026ndash;3.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) profiles, but not the mild ideators with depression (OR\u0026thinsp;=\u0026thinsp;1.52, 95%CI:0.90\u0026ndash;2.53, p\u0026thinsp;=\u0026thinsp;0.1124) showed higher odds of dying by suicide compared to the non-ideator reference group (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3 GWAS analyses and functional mapping across SI profiles\u003c/b\u003e: GWAS analyses were performed on the EUR (n\u0026thinsp;=\u0026thinsp;49,522) and the AFR (n\u0026thinsp;=\u0026thinsp;16,910) ancestries (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e) for all ideation profiles. The EUR ancestry GWAS analyses identified genome-wide significant associations for the persistent and mild ideation with depression profiles with SNPs rs72946414 (p\u0026thinsp;=\u0026thinsp;4.52e-08) and rs1539829 (p\u0026thinsp;=\u0026thinsp;2.13e-09) respectively in comparison to the non-ideator reference group (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA \u0026amp; B, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Genome-wide significant associations were detected across all SI profiles within the AFR ancestry, including SNPs rs536574477 (p\u0026thinsp;=\u0026thinsp;5.03e-09, P-SI), rs12062434 (p\u0026thinsp;=\u0026thinsp;1.50e-08, M-SI/WD), rs139686905 (p\u0026thinsp;=\u0026thinsp;4.94e-08, V-SI), and rs3127082 (p\u0026thinsp;=\u0026thinsp;4.42e-08, M-SI/ND), shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-D and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Also, GWAS meta-analyses across EUR and AFR ancestries identified two SNPs with genome-wide significance for the variable and mild ideation with depression profiles (V-SI: rs200126785 with p\u003csub\u003emeta\u003c/sub\u003e= 4.84e-08, and M-SI/WD: rs1539829 with p\u003csub\u003emeta\u003c/sub\u003e=2.71e-09, see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). None of these associations were corroborated by the ISGC GWAS study of suicidal behavior phenotype (using p\u0026thinsp;\u0026lt;\u0026thinsp;1e-5 for significance threshold).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eGenome-wide significant GWAS results.\u003c/b\u003e Shown are Annotations of SI subtyping GWAS hits with p\u0026thinsp;\u0026lt;\u0026thinsp;5e-8 for EUR, AFR, and meta-analysis. Distance: Distance to the nearest gene. SNPs which are locating in the gene body or 1kb up- or down-stream of TSS or TES have 0.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA1:A2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBeta (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNearest gene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDistance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eEUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102039837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA:G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers72946414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.52e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.80 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eZPLD1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-SI/WD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22557164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers1539829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.13e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLINC01894\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(RP11-958F21.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003encRNA intronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68343057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTTTG:TTTGTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers536574477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.03e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21 (0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eCTNNA3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-SI/WD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165374688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA:G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers12062434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.50e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.07 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eRXRG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54252936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA:G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers139686905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.94e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eRNU6-751P\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003edownstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-SI/ND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115510667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC:G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers3127082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.42e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ePLEKHS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eupstream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eMeta-analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM-SI/WD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22557164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC:T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers1539829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.71e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.20 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eLINC01894\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e(RP11-958F21.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003encRNA intronic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV-SI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132912691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG:GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ers200126785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.84e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eAL360004.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eintergenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6,192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eIn the present study we examined suicidal ideation patterns within a large cohort of Veterans who participated in the MVP. We found evidence of four distinct ideation profiles with differential associated clinical presentations including comorbid psychiatric and medical conditions, history of suicidal behavior, and, most importantly, differential proportion for dying by suicide. Specifically, we found two SI profiles (persistent and variable ideator profiles) with the highest maximum suicidal ideation as well as elevated risk for comorbid conditions including serious mental illness, with persistent ideators also having the highest prevalence of depression and PTSD. Participants belonging to the variable ideators profile also had higher risk for other comorbid conditions as measured by the CCI, and, importantly, higher odds of dying by other causes than suicide. Compared to the non-ideator reference group, history of suicidal ideation and behavior were significantly greater amongst all SI profiles regardless of depression severity. Surprisingly, participants with mild ideation without depression had the highest odds of death by suicide.\u003c/p\u003e \u003cp\u003eOur study used similar summary SI indices as a previous studies.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e that used LPA for both short- and long-term data collected on suicidal ideation, which have identified similar ideation profiles as the present study including persistent, variable, and mild.\u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47 CR48 CR49\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e The persistent and variable ideation profiles are in line with suicidal subtypes initially suggested by Bernanke et al.\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e According to their model there are at least two suicidal subtypes: the variable subtype that is characterized by large fluctuations in severity of suicidal thoughts in response to life stress, and the persistent subtype that is characterized by persistent levels of suicidal thinking that do not fluctuate in response to life events. The present study did not assess associations in suicidal ideation variability with stress, since we did not have concomitant data on suicidal ideation and life stressors amongst the MVP participants. Nevertheless, it is likely that the participants belonging to the variable ideation profile identified herein may have higher life stressors, attributed to their higher prevalence of comorbid serious mental illness and disease burden compared to the other ideation profiles. Given the high co-morbidity between stress and psychiatric disorders,\u003csup\u003e\u003cspan additionalcitationids=\"CR63 CR64 CR65\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e it is likely that life stressors (though not measured directly herein) contribute to fluctuations in suicidal ideation. In contrast we found that the persistent SI subtype, the non-stress responsive type according to Bernanke et al.,\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003ewas similarly characterized by persistent and more severe suicidal ideation and worse depression as compared to other ideation profiles. Previous studies investigating the trajectories of SI using ecological momentary assessment have also identified ideation profiles that differed with respect to severity and variability of real‐time suicidal ideation,\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e including subtypes with mild ideation consistent with our findings of mild ideators with or without depression. Kleiman et al.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e described these ideation profiles as potential subtypes of the two superordinate SI subtypes i.e., the variable and persistent subtypes.\u003c/p\u003e \u003cp\u003eInvestigations of the suicidal ideation profiles with regards to death by suicide revealed a surprising finding. Extending our analysis in linking the SI profiles to long-term data on suicide death from the National Death Index showed that participants with mild ideation and no depression were at the highest odds of death by suicide.\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e It is likely that these non-depressed Veterans with mild ideation are not identified to be at-risk in the VA, and are not flagged as such in suicide screenings via the VHA universal Risk ID suicide screening mandate using the Columbia Suicide Severity Rating Scale (CSSRS) screener.\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e Indeed, a study that analyzed item level CSSRS surveys from patients\u0026rsquo; electronic health records showed that positive endorsement of suicidal ideation was an indicator of risk of future suicidal behavior over the next 30 days, specifically\u0026thinsp;\u0026gt;\u0026thinsp;8-fold for passive and \u0026gt;\u0026thinsp;2-fold for active ideation\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, in line with previous findings showing majority of patients report only non-acute indicators of risk that are not indicative of preparatory acts or suicidal behavior\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur GWAS analyses identified novel genetic variants associated with specific ideation profiles, spanning both coding and non-coding regions of the genome. For both the EUR and AFR ancestries, we found potential risk variants for the persistent and mild with depression profiles including, for EUR, rs72946414 and rs1539289, localized within the gene \u003cem\u003eZPLD1\u003c/em\u003e and the long non-coding transcript \u003cem\u003eLINC01894\u003c/em\u003e respectively, and for AFR, rs536574477 and rs12062434, localized within the genes \u003cem\u003eCTNNA3\u003c/em\u003e and \u003cem\u003eRXRG\u003c/em\u003e respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The \u003cem\u003eZPLD1\u003c/em\u003e (zona pellucida like domain containing 1) gene product is a glycoprotein expressed in the cupula, a gelatinous structure localized in the inner ear that is involved in vestibular functioning.\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e It is possible that participating Veterans with potential risk variants in the \u003cem\u003eZPLD1\u003c/em\u003e gene may be more susceptible to adverse outcomes following injuries to the cupula, specifically auditory and vestibular injuries incurred during military operations, attributed to excessive exposures to noise and blast overpressure that often result in tinnitus,\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e associated with suicidality.\u003csup\u003e\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e The \u003cem\u003eCTNNA\u003c/em\u003e (catenin alpha 3) gene product is a cell-cell adhesion protein expressed in a variety of tissues including the brain. GWAS studies have identified genetic variants across the \u003cem\u003eCTNNA3\u003c/em\u003e gene that associate with Autism,\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e type 2 diabetes,\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e Alzheimer\u0026rsquo;s disease,\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e and Asthma,\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e disorders that contribute to increased risk for suicide.\u003csup\u003e\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e GWAS studies have also identified genetic variants within the \u003cem\u003eRXRG\u003c/em\u003e (retinoid X receptor gamma) gene with type 2 diabetes.\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e The gene \u003cem\u003eRXRG\u003c/em\u003e is a member of the retinoid X receptor family of nuclear receptors that mediates proliferation/accumulation of retinoic acid and is expressed in the brain. It plays a critical role in white matter repair and remyelination and has been shown to be involved in oligodendrocyte precursor cell differentiation.\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e This is particularly interesting given that oligodendrocytes play a key role in myelination\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e and CNS inflammation, wherein in postmortem suicide studies oligodendrocytes have been shown to exhibit functional impairments as well as depletion in cell population.\u003csup\u003e89 90, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e Oligodendrocytes represent an important neurobiological substrate, contributing to the altered connectivity and white matter integrity, supported also by findings from imaging studies in suicide including in vivo studies of the frontal lobe in depression and suicidality revealing disrupted brain connectivity in grey and white matter of those who attempt or die by suicide\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Moreover, for the AFR ancestry, we identified risk variants associated with variable and mild without depression ideation profiles, consisting of rs139686905, downstream of the RNA U6 small nuclear gene (\u003cem\u003eRNU6-751P\u003c/em\u003e) and rs3127082, upstream of the pleckstrin homology domain containing S1 gene (\u003cem\u003ePLEKHS1\u003c/em\u003e), both loci implicated in cancer.\u003csup\u003e\u003cspan additionalcitationids=\"CR94 CR95\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e Finally, meta-analyses of the EUR and AFR ancestries identified risk variants for the mild with depression and variable ideation profiles consisting of rs1539829 and rs200126785, proximal to two noncoding transcripts, \u003cem\u003eLINC01894\u003c/em\u003e and \u003cem\u003eAL360004\u003c/em\u003e.1, respectively. Converging evidence from post-mortem brains and clinical samples points to the importance of non-coding RNA in Depression\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e and, of relevance to the present study, suicidality.\u003csup\u003e\u003cspan additionalcitationids=\"CR99\" citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study has several limitations: the PHQ-9 instrument from which the depression and suicide severity variables were derived for LPA is a self-report assessment instrument, thus findings may be affected by under reporting related to sensitivity concerns in endorsing suicidal thoughts and behavior. Data on depression and suicidal ideation severity were aggregated rather than examined longitudinally, for example via data collections using ecological momentary assessments to track daily fluctuations in computation of summary indices. Future investigations of clinical symptoms over time longitudinally would allow for studying trajectories of symptom severity related to suicide risk over time and in identifying key constructs that affect these trajectories. Although the MVP data provides opportunity to examine long periods instead of daily symptom changes, this may still be highly valuable to identify ideation patterns with clinical relevance, as longer periods match real-life assessment opportunities better, in line with medical appointment visits. Finally, the study population are military Veterans, and as such it is possible that findings will not generalize to the civilian population.\u003c/p\u003e \u003cp\u003eIn conclusion, the ideation profiles identified in the present study may provide useful information for testing whether different profiles would have different characteristics, courses, and genetic signatures, important in future investigations of targeted interventions; Thus, fostering development of novel personalized interventions across the suicide risk continuum.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST:\u003c/h2\u003e \u003cp\u003eDr. Haghighi, Dr. Pyarajan, Mr. Dochtermann, and Ms. Sun reported no biomedical financial interests or potential conflicts of interest. Dr. Galfalvy and her family own stocks in Illumina, Inc.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS:\u003c/h2\u003e \u003cp\u003eThis research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by MVP000, award #MVP023 to F.H, and #MVP000 supporting S.P. and D.D.\u0026rsquo;s work. Fatemeh Haghighi, PhD., a recipient of the VA CSR\u0026amp;D Research Career Scientist Award (CX002074) and her laboratory and work is supported by CX001728, BX006069, BX003794, and RX003818 at the James J. Peters VA Medical Center. This publication does not represent the views of the Department of Veteran Affairs or the United States Government.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eThe data underlying this publication are accessible to researchers with Million Veteran Program (MVP) data access. Summary statistics will be made publicly available on dbGAP.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSuicide. https://www.nimh.nih.gov/health/statistics/suicide, 2024, Accessed Date Accessed 2024 Accessed.\u003c/li\u003e\n\u003cli\u003ePrevention OoMHaS. 2023 National Veteran Suicide Prevention Annual Report: Veterans Affairs; 2023.\u003c/li\u003e\n\u003cli\u003eKey Substance Use and Mental Health Indicators in the United States: Results from the 2020 National Survey on Drug Use and Health. Rockville, MD: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration: Substance Abuse and Mental Health Services Administration; 2021.\u003c/li\u003e\n\u003cli\u003eFazel S, Runeson B. 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This study used longitudinal data from the Million Veteran Program Mental Health Survey to identify SI profiles among Veterans based on trajectories of ideation and depression severity, and compared them to a non-suicidal (no-SI) control group. Latent profile analysis (LPA) was performed to identify SI profiles using data from Veterans (n\u0026thinsp;=\u0026thinsp;34,322) endorsing SI in their electronic health record. LPA identified four highly reproducible SI profiles: mild ideators with and without depression, variable ideators, and persistent ideators. Veterans across the SI profiles were significantly more likely to have diagnoses of suicidal ideation or behavior, mental disorders, and TBI compared to Veterans with no-SI. The variable ideators showed higher rates of comorbid conditions. The mild ideators without depression and persistent ideators had a significantly higher proportion of deaths by suicide than the no-SI Veterans. European and African American GWAS and pan-ancestry meta-analyses of SI profiles compared to no-SI controls were also performed, which identified genome-wide significant loci across all SI profiles proximal to genes implicated in auditory and vestibular functioning, Alzheimer\u0026rsquo;s, Diabetes, and Asthma. In summary, SI profiles identified were associated with novel genetic variants not identified by previous suicide GWAS studies. Additionally, Veterans within the mild SI profile that did not present with high-risk comorbidities had the highest rate of suicide deaths, indicating the need for upstream suicide risk prevention interventions across the SI risk continuum.\u003c/p\u003e","manuscriptTitle":"Genetic Architecture of Suicidal Ideation Continuum: Latent Profile Analysis of Data using the Million Veteran Program Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-21 09:00:30","doi":"10.21203/rs.3.rs-6066944/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-04-11T09:28:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-03-24T21:54:52+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-03-24T18:02:09+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-13T00:57:51+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-10T19:10:41+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-04T19:56:18+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-03-03T20:47:16+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-03-03T20:38:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-21T11:51:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-21T11:45:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2025-02-20T15:49:26+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-02-20T11:15:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5c8aa473-c6a9-4a17-9575-29b9ff9f16dc","owner":[],"postedDate":"May 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47020529,"name":"Biological sciences/Genetics"},{"id":47020530,"name":"Biological sciences/Psychology"}],"tags":[],"updatedAt":"2025-12-03T08:12:05+00:00","versionOfRecord":{"articleIdentity":"rs-6066944","link":"https://doi.org/10.1038/s41380-025-03332-2","journal":{"identity":"molecular-psychiatry","isVorOnly":false,"title":"Molecular Psychiatry"},"publishedOn":"2025-12-02 05:00:00","publishedOnDateReadable":"December 2nd, 2025"},"versionCreatedAt":"2025-05-21 09:00:30","video":"","vorDoi":"10.1038/s41380-025-03332-2","vorDoiUrl":"https://doi.org/10.1038/s41380-025-03332-2","workflowStages":[]},"version":"v1","identity":"rs-6066944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6066944","identity":"rs-6066944","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}