Genetic Liability to Posttraumatic Stress Disorder Symptoms and Its Association With Cardiometabolic and Respiratory Outcomes.

Importance: Posttraumatic stress disorder (PTSD) has been reported to be a risk factor for several physical and somatic symptoms. However, the genetics of PTSD and its potential association with medical outcomes remain unclear. Objective: To examine disease categories and laboratory tests from electronic health records (EHRs) that are associated with PTSD polygenic scores. Design, Setting, and Participants: This genetic association study was conducted from July 15, 2021, to January 24, 2023, using EHR data from participants across 4 biobanks. The polygenic scores of PTSD symptom severity (PGS-PTSD) were tested with all available phecodes in Vanderbilt University Medical Center's biobank (BioVU), Mass General Brigham (MGB), Michigan Genomics Initiative (MGI), and UK Biobank (UKBB). The significant medical outcomes were tested for overrepresented disease categories and subsequently tested for genetic correlation and 2-sample mendelian randomization (MR) to determine genetically informed associations. Multivariable MR was conducted to assess whether PTSD associations with health outcomes were independent of the genetic effect of body mass index and tobacco smoking. Exposures: Polygenic score of PTSD symptom severity. Main Outcomes and Measures: A total of 1680 phecodes (ie, International Classification of Diseases, Ninth Revision- and Tenth Revision-based phenotypic definitions of health outcomes) across 4 biobanks and 490 laboratory tests across 2 biobanks (BioVU and MGB). Results: In this study including a total of 496 317 individuals (mean [SD] age, 56.8 [8.0] years; 263 048 female [53%]) across the 4 EHR sites, meta-analyzing associations of PGS-PTSD with 1680 phecodes from 496 317 individuals showed significant associations to be overrepresented from mental health disorders (fold enrichment = 3.15; P = 5.81 × 10-6), circulatory system (fold enrichment = 3.32; P = 6.39 × 10-12), digestive (fold enrichment = 2.42; P = 2.16 × 10-7), and respiratory outcomes (fold enrichment = 2.51; P = 8.28 × 10-5). The laboratory measures scan with PGS-PTSD in BioVU and MGB biobanks revealed top associations in metabolic and immune domains. MR identified genetic liability to PTSD symptom severity as an associated risk factor for 12 health outcomes, including alcoholism (ß = 0.023; P = 1.49 × 10-4), tachycardia (ß = 0.045; P = 8.30 × 10-5), cardiac dysrhythmias (ß = 0.016, P = 3.09 × 10-5), and acute pancreatitis (ß = 0.049, P = 4.48 × 10-4). Several of these associations were robust to genetic effects of body mass index and smoking. We observed a bidirectional association between PTSD symptoms and nonspecific chest pain and C-reactive protein. Conclusions and Relevance: Results of this study suggest the broad health repercussions associated with the genetic liability to PTSD across 4 biobanks. The circulatory and respiratory systems association was observed to be overrepresented in all 4 biobanks.

Multi-ancestry genome-wide association study of cannabis use disorder yields insight into disease biology and public health implications.

As recreational use of cannabis is being decriminalized in many places and medical use widely sanctioned, there are growing concerns about increases in cannabis use disorder (CanUD), which is associated with numerous medical comorbidities. Here we performed a genome-wide association study of CanUD in the Million Veteran Program (MVP), followed by meta-analysis in 1,054,365 individuals (ncases = 64,314) from four broad ancestries designated by the reference panel used for assignment (European n = 886,025, African n = 123,208, admixed American n = 38,289 and East Asian n = 6,843). Population-specific methods were applied to calculate single nucleotide polymorphism-based heritability within each ancestry. Statistically significant single nucleotide polymorphism-based heritability for CanUD was observed in all but the smallest population (East Asian). We discovered genome-wide significant loci unique to each ancestry: 22 in European, 2 each in African and East Asian, and 1 in admixed American ancestries. A genetically informed causal relationship analysis indicated a possible effect of genetic liability for CanUD on lung cancer risk, suggesting potential unanticipated future medical and psychiatric public health consequences that require further study to disentangle from other known risk factors such as cigarette smoking.

Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent.

To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (π c ). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for π c differences related to hematological and metabolic traits (hematological fold-enrichment=4.45, p=2.15×10 -7 ; metabolic fold-enrichment=4.05, p=4.01×10 -6 ). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median π c =0.15%, EAS-metabolic median π c =0.18%) with the strongest π c difference with respect to respiratory traits (EAS-respiratory median π c =0.50%; Hematological-p=2.26×10 -3 ; Metabolic-p=3.48×10 -3 ). In EUR, pairwise comparisons showed multiple π c differences related to the endocrine category (fold-enrichment=5.83, p=4.76×10 -6 ), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median π c =0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median π c =0.50%; p=1.19×10 -4 ). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p=2.18×10 -4 ; EUR endocrine-gastrointestinal p=6.80×10 -4 ). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.

Characterizing the polygenic architecture of complex traits in populations of East Asian and European descent.

To investigate the polygenicity of complex traits in populations of East Asian (EAS) and European (EUR) descents, we leveraged genome-wide data from Biobank Japan, UK Biobank, and FinnGen cohorts. Specifically, we analyzed up to 215 outcomes related to 18 health domains, assessing their polygenic architecture via descriptive statistics, such as the proportion of susceptibility SNPs per trait (πc). While we did not observe EAS-EUR differences in the overall distribution of polygenicity parameters across the phenotypes investigated, there were ancestry-specific patterns in the polygenicity differences between health domains. In EAS, pairwise comparisons across health domains showed enrichment for πc differences related to hematological and metabolic traits (hematological fold-enrichment = 4.45, p = 2.15 × 10-7; metabolic fold-enrichment = 4.05, p = 4.01 × 10-6). For both categories, the proportion of susceptibility SNPs was lower than that observed for several other health domains (EAS-hematological median πc = 0.15%, EAS-metabolic median πc = 0.18%) with the strongest πc difference with respect to respiratory traits (EAS-respiratory median πc = 0.50%; hematological-p = 2.26 × 10-3; metabolic-p = 3.48 × 10-3). In EUR, pairwise comparisons showed multiple πc differences related to the endocrine category (fold-enrichment = 5.83, p = 4.76 × 10-6), where these traits showed a low proportion of susceptibility SNPs (EUR-endocrine median πc = 0.01%) with the strongest difference with respect to psychiatric phenotypes (EUR-psychiatric median πc = 0.50%; p = 1.19 × 10-4). Simulating sample sizes of 1,000,000 and 5,000,000 individuals, we also showed that ancestry-specific polygenicity patterns translate into differences across health domains in the genetic variance explained by susceptibility SNPs projected to be genome-wide significant (e.g., EAS hematological-neoplasm p = 2.18 × 10-4; EUR endocrine-gastrointestinal p = 6.80 × 10-4). These findings highlight that traits related to the same health domains may present ancestry-specific variability in their polygenicity.

Sex differences in the polygenic architecture of hearing problems in adults.

BACKGROUND: Hearing problems (HP) in adults are common and are associated with several comorbid conditions. Its prevalence increases with age, reflecting the cumulative effect of environmental factors and genetic predisposition. Although several risk loci have been already identified, HP biology and epidemiology are still insufficiently investigated by large-scale genetic studies. METHODS: Leveraging the UK Biobank, the Nurses' Health Studies (I and II), the Health Professionals Follow-up Study, and the Million Veteran Program, we conducted a comprehensive genome-wide investigation of HP in 748,668 adult participants (discovery N = 501,825; replication N = 226,043; cross-ancestry replication N = 20,800). We leveraged the GWAS findings to characterize HP polygenic architecture, exploring sex differences, polygenic risk across ancestries, tissue-specific transcriptomic regulation, cause-effect relationships with genetically correlated traits, and gene interactions with HP environmental risk factors. RESULTS: We identified 54 risk loci and demonstrated that HP polygenic risk is shared across ancestry groups. Our transcriptomic regulation analysis highlighted the potential role of the central nervous system in HP pathogenesis. The sex-stratified analyses showed several additional associations related to peripheral hormonally regulated tissues reflecting a potential role of estrogen in hearing function. This evidence 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 HP associations. Additionally, the genetically informed causal inference analysis showed that HP is linked to many physical and mental health outcomes. CONCLUSIONS: The results provide many novel insights into the biology and epidemiology of HP in adults. Our sex-specific analyses and transcriptomic associations highlighted molecular pathways that may be targeted for drug development or repurposing. Additionally, the potential causal relationships identified may support novel preventive screening programs to identify individuals at risk.

An Atlas of Genetic Correlations and Genetically Informed Associations Linking Psychiatric and Immune-Related Phenotypes.

Importance: Certain psychiatric and immune-related disorders are reciprocal risk factors. However, the nature of these associations is unclear. Objective: To characterize the pleiotropy between psychiatric and immune-related traits, as well as risk factors of hypothesized relevance. Design, Setting, and Participants: This genetic association study was conducted from July 10, 2020, to January 15, 2022. Analyses used genome-wide association (GWA) statistics related to 14 psychiatric traits; 13 immune-related phenotypes, ie, allergic, autoimmune, and inflammatory disorders; and 15 risk factors related to health-related behaviors, social determinants of health, and stress response. Genetically correlated psychiatric-immune pairs were assessed using 2-sample mendelian randomization (MR) with sensitivity analyses and multivariable adjustment for genetic associations of third variables. False discovery rate correction (Q value < .05) was applied for each analysis. Exposures: Genetic associations. Main Outcomes and Measures: Genetic correlations and MR association estimates with SEs and P values. A data-driven approach was used that did not test a priori planned hypotheses. Results: A total of 44 genetically correlated psychiatric-immune pairs were identified, including 31 positive correlations (most consistently involving asthma, Crohn disease, hypothyroidism, and ulcerative colitis) and 13 negative correlations (most consistently involving allergic rhinitis and type 1 diabetes). Correlations with third variables were especially strong for psychiatric phenotypes. MR identified 7 associations of psychiatric phenotypes on immune-related phenotypes that were robust to multivariable adjustment, including the positive association of (1) the psychiatric cross-disorder phenotype with asthma (odds ratio [OR], 1.04; 95% CI, 1.02-1.06), Crohn disease (OR, 1.09; 95% CI, 1.05-1.14), and ulcerative colitis (OR, 1.09; 95% CI, 1.05-1.14); (2) major depression with asthma (OR, 1.25; 95% CI, 1.13-1.37); (3) schizophrenia with Crohn disease (OR, 1.12; 95% CI, 1.05-1.18) and ulcerative colitis (OR, 1.14; 95% CI, 1.07-1.21); and a negative association of risk tolerance with allergic rhinitis (OR, 0.77; 95% CI, 0.67-0.92). Conclusions and Relevance: Results of this genetic association study suggest that genetic liability for psychiatric disorders was associated with liability for several immune disorders, suggesting that vertical pleiotropy related to behavioral traits (or correlated third variables) contributes to clinical associations observed in population-scale data.

The integration of genetically-regulated transcriptomics and electronic health records highlights a pattern of medical outcomes related to increased hepatic transthyretin expression.

Transthyretin (TTR) is the precursor of the fibrils that compromise organ function in hereditary and sporadic systemic amyloidoses (ATTR). RNA-interference and anti-sense therapeutics targeting TTR hepatic transcription have been shown to reduce TTR amyloid formation. In the present study, we leveraged genetic and phenotypic information from the UK Biobank and transcriptomic profiles from the Genotype-Tissue Expression project to test the association of genetically regulated TTR gene expression with 7149 traits assessed in 420,531 individuals. We conducted a multi-tissue analysis of TTR transcription and identified an association with a operational procedure related to bone fracture (p = 5.46×10-6). Using tissue-specific TTR expression information, we demonstrated that the association is driven by the genetic regulation of TTR hepatic expression (odds ratio [OR] = 3.46, p = 9.51×10-5). Using the UK Biobank electronic health records (EHRs), we investigated the comorbidities affecting individuals undergoing this surgical procedure. Excluding bone fracture EHRs, we identified a pattern of health outcomes previously associated with ATTR manifestations. These included osteoarthritis (OR = 3.18, p = 9.18×10-8), carpal tunnel syndrome (OR = 2.15, p = .002), and a history of gastrointestinal diseases (OR = 2.01, p = 8.07×10-4). In conclusion, our study supports that TTR hepatic expression can affect health outcomes linked to physiological and pathological processes presumably related to the encoded protein.

Drinking and smoking polygenic risk is associated with childhood and early-adulthood psychiatric and behavioral traits independently of substance use and psychiatric genetic risk.

Alcohol drinking and tobacco smoking are hazardous behaviors associated with a wide range of adverse health outcomes. In this study, we explored the association of polygenic risk scores (PRS) related to drinks per week, age of smoking initiation, smoking initiation, cigarettes per day, and smoking cessation with 433 psychiatric and behavioral traits in 4498 children and young adults (aged 8-21) of European ancestry from the Philadelphia neurodevelopmental cohort. After applying a false discovery rate multiple testing correction accounting for the number of PRS and traits tested, we identified 36 associations related to psychotic symptoms, emotion and age recognition social competencies, verbal reasoning, anxiety-related traits, parents' education, and substance use. These associations were independent of the genetic correlations among the alcohol-drinking and tobacco-smoking traits and those with cognitive performance, educational attainment, risk-taking behaviors, and psychopathology. The removal of participants endorsing substance use did not affect the associations of each PRS with psychiatric and behavioral traits identified as significant in the discovery analyses. Gene-ontology enrichment analyses identified several neurobiological processes underlying mechanisms of the PRS associations we report. In conclusion, we provide novel insights into the genetic overlap of smoking and drinking behaviors in children and young adults, highlighting their independence from psychopathology and substance use.

Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example.

BACKGROUND: Machine learning (ML) models are beginning to proliferate in psychiatry, however machine learning models in psychiatric genetics have not always accounted for ancestry. Using an empirical example of a proposed genetic test for OUD, and exploring a similar test for tobacco dependence and a simulated binary phenotype, we show that genetic prediction using ML is vulnerable to ancestral confounding. METHODS: We utilize five ML algorithms trained with 16 brain reward-derived "candidate" SNPs proposed for commercial use and examine their ability to predict OUD vs. ancestry in an out-of-sample test set (N = 1000, stratified into equal groups of n = 250 cases and controls each of European and African ancestry). We rerun analyses with 8 random sets of allele-frequency matched SNPs. We contrast findings with 11 genome-wide significant variants for tobacco smoking. To document generalizability, we generate and test a random phenotype. RESULTS: None of the 5 ML algorithms predict OUD better than chance when ancestry was balanced but were confounded with ancestry in an out-of-sample test. In addition, the algorithms preferentially predicted admixed subpopulations. Random sets of variants matched to the candidate SNPs by allele frequency produced similar bias. Genome-wide significant tobacco smoking variants were also confounded by ancestry. Finally, random SNPs predicting a random simulated phenotype show that the bias attributable to ancestral confounding could impact any ML-based genetic prediction. CONCLUSIONS: Researchers and clinicians are encouraged to be skeptical of claims of high prediction accuracy from ML-derived genetic algorithms for polygenic traits like addiction, particularly when using candidate variants.

Epigenomic Profiles of African-American Transthyretin Val122Ile Carriers Reveals Putatively Dysregulated Amyloid Mechanisms.

BACKGROUND: The Val122Ile mutation in Transthyretin (TTR) gene causes a rare, difficult to diagnose hereditary form of cardiac amyloidosis. This mutation is most common in the United States and mainly present in people of African descent. The carriers have an increased risk of congestive heart failure, peripheral edema, and several other noncardiac phenotypes such as carpal tunnel syndrome, and arthroplasty which are top reasons for ambulatory/outpatient surgeries (OSs) in the country. METHODS: We conducted first-ever epigenome-wide association study using the Illumina's EPIC array, in Val122Ile carriers of African descent for heart disease and multiple OSs-an early disease indicator. Differential methylation across genome wide cytosine-phosphate guanine (CpG) sites was tested between carriers with and without heart disease and OS. Significant CpG sites were investigated for cis-mQTLs loci, followed by gene ontology and protein-protein interaction network. We also investigated the significant CpG sites in a secondary cohort of carriers for replication. RESULTS: Five differentially methylated sites (P≤2.1×10-8) in genes-FAM129B, SKI, WDR27, GLS, and an intergenic site near RP11-550A5.2, and one differentially methylated region containing KCNA6 and GALNT3 (P=1.1×10-12) were associated with heart disease. For OS, we observe 4 sites-2 sites in UBE2E3 and SEC14L5, and other 2 in intergenic regions (P≤1.8×10-7) and 3 regions overlapping SH3D21, EVA1B, LTB4R2, and CIDEB (P≤3.9×10-7). Functional protein-interaction module analysis identified ABCA1 (P=0.001) for heart disease. Six cis-mQTLs were associated with one of the significant CpG sites (FAM129B; P=4.1×10-24). We replicated 2 CpG sites (cg18546846 and cg06641417; P<0.05) in an external cohort of biopsy-confirmed cases of TTR (transthyretin) amyloidosis. The genes identified are involved in transport and clearance of amyloid deposits (GLS, ABCA1, FAM129B); cardiac fibrosis (SKI); and muscle tissue regulation (SKI, FAM129B). CONCLUSIONS: These findings highlight the link between a complex amyloid circuit and diverse symptoms of Val122Ile.

Multi-environment gene interactions linked to the interplay between polysubstance dependence and suicidality.

Substance dependence diagnoses (SDs) are important risk factors for suicidality. We investigated the associations of multiple SDs with different suicidality outcomes, testing how genetic background moderates these associations. The Yale-Penn cohort (N = 15,557) was recruited to investigate the genetics of SDs. The Army STARRS (Study to Assess Risk and Resilience in Servicemembers) cohort (N = 11,236) was recruited to evaluate mental health risk and resilience among Army personnel. We applied multivariate logistic regression to investigate the associations of SDs with suicidality and, in the Yale-Penn cohort, we used the structured linear mixed model (StructLMM) to study multivariate gene-environment interactions. In Yale-Penn, lifetime polysubstance dependence was strongly associated with lifetime suicidality: having five SDs showed an association with suicidality, from odds ratio (OR) = 6.77 (95% confidence interval, CI = 5.74-7.99) for suicidal ideation (SI) to OR = 3.61 (95% CI = 2.7-4.86) for suicide attempt (SA). In Army STARRS, having multiple substance use disorders for alcohol and/or drugs was associated with increased suicidality 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 opioid dependence (BF = 12.2), cocaine dependence (BF = 12.1), nicotine dependence (BF = 9.2), and polysubstance dependence (BF = 2.1). Comorbidity of multiple SDs is a significant associated with suicidality and heritability of suicidality is partially moderated by multivariate gene interactions.

A large-scale genome-wide association study meta-analysis of cannabis use disorder.

BACKGROUND: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. METHODS: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. FINDINGS: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. INTERPRETATION: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. FUNDING: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Genetically-regulated transcriptomics & copy number variation of proctitis points to altered mitochondrial and DNA repair mechanisms in individuals of European ancestry.

BACKGROUND: Proctitis is an inflammation of the rectum and may be induced by radiation treatment for cancer. The genetic heritability of developing radiotoxicity and prior role of genetic variants as being associated with side-effects of radiotherapy necessitates further investigation for underlying molecular mechanisms. In this study, we investigated gene expression regulated by genetic variants, and copy number variation in prostate cancer survivors with radiotoxicity. METHODS: We investigated proctitis as a radiotoxic endpoint in prostate cancer patients who received radiotherapy (n = 222). We analyzed the copy number variation and genetically regulated gene expression profiles of whole-blood and prostate tissue associated with proctitis. The SNP and copy number data were genotyped on Affymetrix® Genome-wide Human SNP Array 6.0. Following QC measures, the genotypes were used to obtain gene expression by leveraging GTEx, a reference dataset for gene expression association based on genotype and RNA-seq information for prostate (n = 132) and whole-blood tissue (n = 369). RESULTS: In prostate tissue, 62 genes were significantly associated with proctitis, and 98 genes in whole-blood tissue. Six genes - CABLES2, ATP6AP1L, IFIT5, ATRIP, TELO2, and PARD6G were common to both tissues. The copy number analysis identified seven regions associated with proctitis, one of which (ALG1L2) was also associated with proctitis based on transcriptomic profiles in the whole-blood tissue. The genes identified via transcriptomics and copy number variation association were further investigated for enriched pathways and gene ontology. Some of the enriched processes were DNA repair, mitochondrial apoptosis regulation, cell-to-cell signaling interaction processes for renal and urological system, and organismal injury. CONCLUSIONS: We report gene expression changes based on genetic polymorphisms. Integrating gene-network information identified these genes to relate to canonical DNA repair genes and processes. This investigation highlights genes involved in DNA repair processes and mitochondrial malfunction possibly via inflammation. Therefore, it is suggested that larger studies will provide more power to infer the extent of underlying genetic contribution for an individual's susceptibility to developing radiotoxicity.

A genome-wide association study of tramadol metabolism from post-mortem samples.

Phase I tramadol metabolism requires cytochrome p450 family 2, subfamily D, polypeptide 6 (CYP2D6) to form O-desmethyltramadol (M1). CYP2D6 genetic variants may infer metabolizer phenotype; however, drug ADME (absorption, distribution, metabolism, and excretion) and response depend on protein pathway(s), not CYP2D6 alone. There is a paucity of data regarding the contribution of trans-acting proteins to idiosyncratic phenotypes following drug exposure. A genome-wide association study identified five markers (rs79983226/kgp11274252, rs9384825, rs62435418/kgp10370907, rs72732317/kgp3743668, and rs184199168/exm1592932) associated with the conversion of tramadol to M1 (M1:T). These SNPs reside within five genes previously implicated with adverse reactions. Analysis of accompanying toxicological meta-data revealed a significant positive linear relationship between M1:T and degree of sample polypharmacy. Taken together, these data identify candidate loci for potential clinical inferences of phenotype following exposure to tramadol and highlight sample polypharmacy as a possible diagnostic covariate in post-mortem genetic studies.

A pathway-driven predictive model of tramadol pharmacogenetics.

Predicting metabolizer phenotype (MP) is typically performed using data from a single gene. Cytochrome p450 family 2 subfamily D polypeptide 6 (CYP2D6) is considered the primary gene for predicting MP in reference to approximately 30% of marketed drugs and endogenous toxins. CYP2D6 predictions have proven clinically effective but also have well-documented inaccuracies due to relatively high genotype-phenotype discordance in certain populations. Herein, a pathway-driven predictive model employs genetic data from uridine diphosphate glucuronosyltransferase, family 1, polypeptide B7 (UGT2B7), adenosine triphosphate (ATP)-binding cassette, subfamily B, number 1 (ABCB1), opioid receptor mu 1 (OPRM1), and catechol-O-methyltransferase (COMT) to predict the tramadol to primary metabolite ratio (T:M1) and the resulting toxicologically inferred MP (t-MP). These data were then combined with CYP2D6 data to evaluate performance of a fully combinatorial model relative to CYP2D6 alone. These data identify UGT2B7 as a potentially significant explanatory marker for T:M1 variability in a population of tramadol-exposed individuals of Finnish ancestry. Supervised machine learning and feature selection were used to demonstrate that a set of 16 loci from 5 genes can predict t-MP with over 90% accuracy, depending on t-MP category and algorithm, which was significantly greater than predictions made by CYP2D6 alone.

Supervised Classification of CYP2D6 Genotype and Metabolizer Phenotype With Postmortem Tramadol-Exposed Finns.

Cytochrome p450 family 2, subfamily D, polypeptide 6 (CYP2D6) may be used to infer the metabolizer phenotype (MP) of an individual as poor, intermediate, extensive/normal, or ultrarapid. Metabolizer phenotypes may suggest idiosyncratic drug responses as contributing factors to cause and/or manner of death in postmortem investigations. Application of CYP2D6 has used long-range amplification of the locus and restriction enzyme digestion to detect single-nucleotide variants (SNVs) associated with MPs. This process can be cumbersome and requires knowledge of genotype phase. Phase may be achieved using long-read DNA sequencing and/or computational methods; however, both can be error prone, which may make it difficult or impractical for implementation into medicolegal practice. CYP2D6 was interrogated in postmortem autopsied Finns using supervised machine learning and feature selection to identify SNVs indicative of MP and/or rate of tramadol O-demethylation (T:M1). A subset of 18 CYP2D6 SNVs could predict MP/T:M1 with up to 96.3% accuracy given phased data. These data indicate that phase contributes to classification accuracy when using CYP2D6 data. Of these 18 SNVs, 3 are novel loci putatively associated with T:M1. These findings may enable design of small multiplexes for easy forensic application of MP prediction when cause and/or manner of death is unknown.

Predicted activity of UGT2B7, ABCB1, OPRM1, and COMT using full-gene haplotypes and their association with the CYP2D6-inferred metabolizer phenotype.

The pharmacogene, CYP2D6, is commonly used to infer metabolizer phenotype of many marketed drugs and endogenous toxins in ante- and post-mortem patients but only represents the efficiency of phase 1 metabolism. Downstream metabolic enzymes encoded by UGT2B7, ABCB1, OPRM1, and COMT also have been implicated in variable individual response to drugs due to their activity at different stages of the tramadol ADME (absorption, distribution, metabolism, and excretion) process. While commonly studied as single genes using targeted genotyping approaches, a more comprehensive tramadol metabolism profile has not been evaluated. 1000 Genomes Project data for UGT2B7, ABCB1, OPRM1, and COMT were used to characterize full-gene haplotypes and their effect on protein function using in-house excel-based workbooks, PopART, and TreeView. Population genetic summary statistics and intergenic analyses associated these haplotypes with full-gene CYP2D6-inferred metabolizer phenotype. The findings suggest that UGT2B7, ABCB1, OPRM1, and COMT may contribute to predicted metabolizer phenotype as opposed to relying solely on CYP2D6.