Resilience following combat exposure is an important factor in understanding posttraumatic stress disorder (PTSD), associated risk, and potentially resilience more generally. Identifying underlying ge Show more
Resilience following combat exposure is an important factor in understanding posttraumatic stress disorder (PTSD), associated risk, and potentially resilience more generally. Identifying underlying genetic factors requires large samples; most biobanks lack extensive resilience assessments, although data regarding trauma and psychiatric symptoms are frequently present that allow computation of a resilience measure. We leveraged the Million Veteran Program (MVP) cohort to calculate discrepancy-based psychiatric resilience (DBPR) scores by regressing PTSD symptoms (PCL-17) onto combat exposure (Deployment Risk and Resilience Inventory-Combat Experiences Scale). We conducted a genome-wide association study (GWAS) of DBPR among European-ancestry (EUR) (n=94,360) and African-ancestry (AFR) participants (n=10,339). We performed conditional analyses with disorders frequently comorbid with PTSD (major depressive disorder, generalized anxiety), examined genetic correlations (r SNP-based heritability was 0.079 (SE=0.007) and three independent genome-wide significant loci were associated with DBPR in EUR; no significant loci were identified in AFR. Trans-ancestry meta-analysis revealed three significant SNPs mapping to RN7SKPP19*rs4650199, MAD1L1*rs12669370, and KANSL1:KANSL1-AS1*rs62060955. In EUR, eight genes were identified in TWAS. One gene (C7orf50) reached a posterior probability >0.90 in TWAS fine mapping. Significant correlations were observed between DBPR and other variables including neuroticism (-0.61), participation in religious groups (0.29) and engaging in sports (0.39, SE = 0.05). The r These findings extend the literature regarding DBPR as a resilience measure and help inform our understanding of the underlying biological mechanisms. Show less
While previous genome-wide association studies (GWAS) identified multiple risk loci for suicide ideation (SI) and suicide attempt (SA), there is still a limited understanding of the genetic predisposi Show more
While previous genome-wide association studies (GWAS) identified multiple risk loci for suicide ideation (SI) and suicide attempt (SA), there is still a limited understanding of the genetic predisposition underlying suicidal behaviors in diverse populations. This study aimed to conduct a large-scale investigation of the suicidality spectrum (SP) to generate new insights into its biology and epidemiology. Leveraging ancestrally diverse participants (SI N This study provides convergent genetic evidence for both shared and phenotype-specific components of suicidal behaviors and delineates their associated factors spanning from proximal clinical and behavioral traits to more distal social determinants. These findings refine our understanding of the etiology of suicidal behaviors and may inform targeted strategies for suicide prevention in both clinical and public health settings. Show less
Post-traumatic stress disorder (PTSD) is a major problem among military veterans and civilians alike, yet its pathophysiology remains poorly understood. We performed a genome-wide association study an Show more
Post-traumatic stress disorder (PTSD) is a major problem among military veterans and civilians alike, yet its pathophysiology remains poorly understood. We performed a genome-wide association study and bioinformatic analyses, which included 146,660 European Americans and 19,983 African Americans in the US Million Veteran Program, to identify genetic risk factors relevant to intrusive reexperiencing of trauma, which is the most characteristic symptom cluster of PTSD. In European Americans, eight distinct significant regions were identified. Three regions had values of P < 5 × 10 Show less
Renato Polimanti, Hongyu Zhao, Lindsay A Farrer+2 more · 2017 · American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · Wiley · added 2026-04-24
We previously mapped loci for the genome-wide association studies (GWAS) and genome-wide gene-by-alcohol dependence interaction (GW-GxAD) analyses of risky sexual behaviors (RSB). This study extends t Show more
We previously mapped loci for the genome-wide association studies (GWAS) and genome-wide gene-by-alcohol dependence interaction (GW-GxAD) analyses of risky sexual behaviors (RSB). This study extends those findings by analyzing the ancestry- and sex-specific AD-stratified effects on RSB. We examined the concordance of findings for the AD-stratified GWAS and the GW-GxAD analysis of RSB, with concordance defined as genome-wide significance in one analysis and at least nominal significance in the second analysis. A total of 2,173 African-American (AA) and 1,751 European-American (EA) subjects were investigated. Information regarding RSB (lifetime experiences of unprotected sex and multiple sexual partners) and DSM-IV diagnosis of lifetime AD were derived from the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA). In our ancestry- and sex-specific analyses, we identified four independent genome-wide significant (GWS) loci (p < 5*10 Show less
Outcomes related to disordered metabolism are common in alcohol dependence (AD). To investigate alterations in the regulation of body mass that occur in the context of AD, we performed a genome-wide a Show more
Outcomes related to disordered metabolism are common in alcohol dependence (AD). To investigate alterations in the regulation of body mass that occur in the context of AD, we performed a genome-wide association study (GWAS) of body mass index (BMI) in African Americans (AAs) and European Americans (EAs) with AD. Subjects were recruited for genetic studies of AD or drug dependence and evaluated using the Semi-structured Assessment for Drug Dependence and Alcoholism. We investigated a total of 2587 AAs and 2959 EAs with DSM-IV AD diagnosis. In the stage 1 sample (N = 4137), we observed three genome-wide significant (GWS) single-nucleotide polymorphism associations, rs200889048 (P = 8.98 * 10 Show less
Cardiovascular and metabolic traits (CMT) are influenced by complex interactive processes including diet, lifestyle, and genetic predisposition. The present study investigated the interactions of thes Show more
Cardiovascular and metabolic traits (CMT) are influenced by complex interactive processes including diet, lifestyle, and genetic predisposition. The present study investigated the interactions of these risk factors in relation to CMTs in the Turkish population. We applied bootstrap agglomerative hierarchical clustering and Bayesian network learning algorithms to identify the causative relationships among genes involved in different biological mechanisms (i.e., lipid metabolism, hormone metabolism, cellular detoxification, aging, and energy metabolism), lifestyle (i.e., physical activity, smoking behavior, and metropolitan residency), anthropometric traits (i.e., body mass index, body fat ratio, and waist-to-hip ratio), and dietary habits (i.e., daily intakes of macro- and micronutrients) in relation to CMTs (i.e., health conditions and blood parameters). We identified significant correlations between dietary habits (soybean and vitamin B12 intakes) and different cardiometabolic diseases that were confirmed by the Bayesian network-learning algorithm. Genetic factors contributed to these disease risks also through the pleiotropy of some genetic variants (i.e., F5 rs6025 and MTR rs180508). However, we also observed that certain genetic associations are indirect since they are due to the causative relationships among the CMTs (e.g., APOC3 rs5128 is associated with low-density lipoproteins cholesterol and, by extension, total cholesterol). Our study applied a novel approach to integrate various sources of information and dissect the complex interactive processes related to CMTs. Our data indicated that complex causative networks are present: causative relationships exist among CMTs and are affected by genetic factors (with pleiotropic and non-pleiotropic effects) and dietary habits. Show less