Abstract Background and Aims Substance dependence diagnoses (SDs) are important risk factors for suicidal behaviors. We investigated the associations of multiple SDs with different suicidal behaviors and tested how genetic background moderates these associations. Design Multivariate logistic regression to investigate the associations of SDs with suicidal behaviors; structured linear mixed model to study multivariate gene– environment interactions. Setting The Yale-Penn cohort was recruited to investigate the genetics of SDs. The Army STARRS (Study to Assess Risk and Resilience in Servicemembers) cohort was recruited to evaluate mental health risk and resilience for suicidal behaviors among Army personnel. Participants Yale-Penn participants (N=15,557) were assessed via the Semi-Structured Assessment for Drug Dependence and Alcoholism. Army STARRS participants (N=11,236) were evaluated using the self-administered Composite International Diagnostic Interview Screening Scales. Measurement Lifetime self-reported suicidal behaviors (ideation, SI; planning; attempt, SA); Lifetime DSM-IV diagnoses and criteria for dependence on alcohol, cannabis, cocaine (CoD), opioid (OD), and nicotine (ND) (Yale-Penn); substance use disorder (SUD) (Army STARRS). Findings In Yale-Penn, lifetime polysubstance dependence was strongly associated with lifetime suicidal behaviors: individuals with five SDs showed increased odds ranging from OR=6.77 (95%CI=5.74-7.99) for SI to OR=3.61 (95%CI=2.7-4.86) for SA. In Army STARRS, SUD was associated with increased odds ranging from OR=2.88 (95%CI=2.6-3.19) for SI to OR=3.92 (95%CI=3.19-4.81) for SA. In Yale-Penn, we identified multivariate gene–environment interactions (Bayes factors, BF > 0) of SI with respect to a gene cluster on chromosome 16 ( LCAT , p=1.82×10 −7 ; TSNAXIP1 , p=2.13×10 −7 ; CENPT , p=2.32×10 −7 ; PARD6A , p=5.57×10 −7 ) for OD (BF=12.2), CoD (BF=12.1), ND (BF=9.2), and polysubstance dependence (BF=2.1). Conclusions Comorbidity of multiple SDs is a significant suicide risk factor and heritability of suicidal behaviors is partially moderated by multivariate gene interactions.
Abstract UK Biobank (UKB) is a key contributor in mental health genome-wide association studies (GWAS) but only ~31% of participants completed the Mental Health Questionnaire (“MHQ responders”). We predicted generalized anxiety disorder (GAD), posttraumatic stress disorder (PTSD), and major depression symptoms using elastic net regression in the ~69% of UKB participants lacking MHQ data (“MHQ non-responders”; N Training =50%; N Test =50%), maximizing the informative sample for these traits. MHQ responders were more likely to be female, from higher socioeconomic positions, and less anxious than non-responders. Genetic correlation of GAD and PTSD between MHQ responders and non-responders ranged from 0.636-1.08; both were predicted by polygenic scores generated from independent cohorts. In meta-analyses of GAD ( N =489,579) and PTSD ( N =497,803), we discovered many novel genomic risk loci (13 for GAD and 40 for PTSD). Transcriptomic analyses converged on altered regulation of prenatal dorsolateral prefrontal cortex in these disorders.
Several studies have reported association between leukocyte telomere length (LTL) and neuropsychiatric disorders. Although telomere length is affected by environmental factors, genetic variants in certain loci are strongly associated with LTL. Thus, we aimed to identify the genomic relationship between genetic variants of LTL with brain-based regulatory changes and brain volume. We tested genetic colocalization of seven and nine LTL loci in two ancestry groups, European (EUR) and East-Asian (EAS), respectively, with brain morphology measures for 101 T1-magnetic resonance imaging-based region of interests (n = 21 821). The posterior probability (>90%) was observed for 'fourth ventricle', 'gray matter' and 'cerebellar vermal lobules I-IV' volumes. We then tested causal relationship using LTL loci for gene and methylation expression. We found causal pleiotropy for gene (EAS = four genes; EUR = five genes) and methylation expression (EUR = 17 probes; EAS = 4 probes) of brain tissues (P ≤ 2.47 × 10-6). Integrating chromatin profiles with LTL-single nucleotide polymorphisms identified 45 genes (EUR) and 79 genes (EAS) (P ≤ 9.78×10-7). We found additional 38 LTL-genes using chromatin-based gene mapping for EUR ancestry population. Gene variants in three LTL-genes-GPR37, OBFC1 and RTEL1/RTEL1-TNFRSF6B-show convergent evidence of pleiotropy with brain morphology, gene and methylation expression and chromatin association. Mapping gene functions to drug-gene interactions, we identified process 'transmission across chemical synapses' (P < 2.78 × 10-4). This study provides evidence that genetic variants of LTL have pleiotropic roles with brain-based effects that could explain the phenotypic association of LTL with several neuropsychiatric traits.
Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrate a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n = 18,502). We identify 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterize the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (n = 85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicate these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in Vanderbilt Biobank, pan-UK Biobank, and Biobank Japan. Our study highlights and reconfirms putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.
Abstract We conducted a comprehensive genome-wide investigation of hearing loss (HL) in 748,668 adult participants of the UK Biobank, the Nurses’ Health Studies (I and II), the Health Professionals Follow-up Study, and the Million Veteran Program. We identified 54 risk loci and characterized HL polygenic architecture, exploring sex differences, polygenic risk across ancestries, tissue-specific transcriptomic regulation, cause-effect relationships with genetically-correlated traits, and gene interactions with HL environmental risk factors. Our transcriptomic regulation analysis highlighted the potential role of the central nervous system in HL pathogenesis. This was supported by the multivariate interaction analysis that showed how genes involved in brain development interact with sex, noise pollution, and tobacco smoking in relation to their HL associations. Additionally, the genetically-informed causal inference analysis showed that HL is linked to many physical and mental health outcomes. These results provide many novel insights into the complex biology and epidemiology of HL in adults.
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