Posttraumatic stress disorder (PTSD) is a psychiatric disorder that may arise in response to severe traumatic event and is diagnosed based on three main symptom clusters (reexperiencing, avoidance, ...and hyperarousal) per the Diagnostic Manual of Mental Disorders (version DSM-IV-TR). In this study, we characterized the biological heterogeneity of PTSD symptom clusters by performing a multi-omics investigation integrating genetically regulated gene, splicing, and protein expression in dorsolateral prefrontal cortex tissue within a sample of US veterans enrolled in the Million Veteran Program (N
= 186,689). We identified 30 genes in 19 regions across the three PTSD symptom clusters. We found nine genes to have cell-type specific expression, and over-representation of miRNA-families - miR-148, 30, and 8. Gene-drug target prioritization approach highlighted cyclooxygenase and acetylcholine compounds. Next, we tested molecular-profile based phenome-wide impact of identified genes with respect to 1678 phenotypes derived from the Electronic Health Records of the Vanderbilt University biorepository (N = 70,439). Lastly, we tested for local genetic correlation across PTSD symptom clusters which highlighted metabolic (e.g., obesity, diabetes, vascular health) and laboratory traits (e.g., neutrophil, eosinophil, tau protein, creatinine kinase). Overall, this study finds comprehensive genomic evidence including clinical and regulatory profiles between PTSD, hematologic and cardiometabolic traits, that support comorbidities observed in epidemiologic studies of PTSD.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
To identify novel phenotypic associations related to Cytochrome P450 Family 2 Subfamily A Member 6 (CYP2A6), we investigated the human phenome in a total of 11,271 individuals. Initially, we ...conducted a phenome-wide association study in 3,401 nicotine-exposed elderly subjects considering 358 phenotypic traits. We identified a significant association between CYP2A6 rs113288603 and hearing loss symptoms (p = 5.75 × 10
). No association was observed in a sample of 3,245 nicotine-unexposed individuals from the same discovery cohort, consistent with the conclusion that the finding is related to CYP2A6 involvement in nicotine metabolism. Consistent results were obtained (p < 0.1) in an independent sample of 2,077 nicotine-exposed elderly subjects, and similarly, no significance was observed in the nicotine-unexposed sample (n = 2,548) of the replication cohort. Additional supporting evidence for this association was provided by gene expression data: rs113288603 is associated with increased CYP2A6 expression in cerebellar hemispheres (p = 7.8 × 10
). There is a well-known correlation between smoking and age-related hearing loss. Cigarette smoking is associated with structural changes in the brain and CYP2A6 mediates these changes. In this context, the regulatory role of rs113288603 in cerebellum appears to be consistent with the known involvement of this brain region in auditory function.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide ...Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (nrs495828 = 53 and nrs505922 = 59); strongest association with venous embolism, odds ratio (ORrs495828 1.33 (p = 1.32 x 10-199), and thrombosis ORrs505922 1.33, p = 2.2 x10-265. Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10-191; CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10-12. The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10-23, lupus OR 0.84, p = 3.97 x 10-06. PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10-13 among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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.
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).
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.
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.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Risk factors and long-term consequences of COVID-19 infection are unclear but can be investigated with large-scale genomic data. To distinguish correlation from causation, we performed
analyses of ...three COVID-19 outcomes (N > 1,000,000). We show genetic correlation and putative causality with depressive symptoms, metformin use (genetic causality proportion (gĉp) with severe respiratory COVID-19 = 0.576,
= 1.07 × 10
and hospitalized
= 0.713,
= 0.003), and alcohol drinking status (gĉp with severe respiratory
= 0.633,
= 7.04 × 10
and hospitalized COVID-19 = 0.848,
= 4.13 × 10
). COVID-19 risk loci associated with several hematologic biomarkers. Comprehensive findings inform genetic contributions to COVID-19 epidemiology, molecular mechanisms, and risk factors and potential long-term health effects of severe response to infection.
Coding mutations in the Transthyretin (TTR) gene cause a hereditary form of amyloidosis characterized by a complex genotype-phenotype correlation with limited information regarding differences among ...worldwide populations.
We compared 676 diverse individuals carrying TTR amyloidogenic mutations (rs138065384, Phe44Leu; rs730881165, Ala81Thr; rs121918074, His90Asn; rs76992529, Val122Ile) to 12,430 non-carriers matched by age, sex, and genetically-inferred ancestry to assess their clinical presentations across 1,693 outcomes derived from electronic health records in UK biobank.
In individuals of African descent (AFR), Val122Ile mutation was linked to multiple outcomes related to the circulatory system (fold-enrichment = 2.96, p = 0.002) with the strongest associations being cardiac congenital anomalies (phecode 747.1, p = 0.003), endocarditis (phecode 420.3, p = 0.006), and cardiomyopathy (phecode 425, p = 0.007). In individuals of Central-South Asian descent (CSA), His90Asn mutation was associated with dermatologic outcomes (fold-enrichment = 28, p = 0.001). The same TTR mutation was linked to neoplasms in European-descent individuals (EUR, fold-enrichment = 3.09, p = 0.003). In EUR, Ala81Thr showed multiple associations with respiratory outcomes related (fold-enrichment = 3.61, p = 0.002), but the strongest association was with atrioventricular block (phecode 426.2, p = 2.81 × 10
). Additionally, the same mutation in East Asians (EAS) showed associations with endocrine-metabolic traits (fold-enrichment = 4.47, p = 0.003). In the cross-ancestry meta-analysis, Val122Ile mutation was associated with peripheral nerve disorders (phecode 351, p = 0.004) in addition to cardiac congenital anomalies (fold-enrichment = 6.94, p = 0.003).
Overall, these findings highlight that TTR amyloidogenic mutations present ancestry-specific and ancestry-convergent associations related to a range of health domains. This supports the need to increase awareness regarding the range of outcomes associated with TTR mutations across worldwide populations to reduce misdiagnosis and delayed diagnosis of TTR-related amyloidosis.
To investigate the causal relationship between blood metabolites and traits related to trauma-response, we combined genome-wide and metabolome-wide datasets generated from large-scale cohorts. Five ...trauma-response traits ascertained in the UK Biobank (52,816 <
N
< 117,900 individuals) were considered: (i) “Avoided activities/situations because of previous stressful experience” (
Avoidance
); (ii) “Felt distant from other people” (
Distant
); (iii) “Felt irritable/had angry outbursts” (
Irritable
); (iv) “Felt very upset when reminded of stressful experience” (
Upset
); (v) “Repeated disturbing thoughts of stressful experience”. These were investigated with respect to 52 blood metabolites tested in a previous genome-wide-association study (
N
= 24,925 European-ancestry individuals). Linkage disequilibrium score regression, polygenic risk scoring (PRS), and Mendelian randomization were applied to the datasets. We observed that 14 metabolites were genetically correlated with trauma-response traits (
p
< 0.05). High-resolution PRS of 4 metabolites (citrate; glycoprotein acetyls; concentration of large very-low-density lipoproteins (VLDL) particles (LVLDLP); total cholesterol in medium particles of VLDL (MVLDLC)) were associated with trauma-response traits (false discovery rate Q < 10%). These genetic associations were partially due to causal relationships (Citrate→Upset
β
= − 0.058,
p
= 9.1 × 10
-4
; Glycoproteins→Avoidance
β
= 0.008,
p
= 0.003; LVLDLP→Distant
β
= 0.008,
p
= 0.022; MVLDLC→Avoidance
β
= 0.019,
p
= 3 × 10
-4
). No reverse associations were observed. In conclusion, our study supports causal relationships between certain blood metabolites and emotional and behavioral responses to traumatic experiences.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The original version of this Article omitted the following from the Acknowledgements: 'Supported by the Mental Illness Research, Education and Clinical Center of the Veterans Integrated Service ...Network 4 of the Department of Veterans Affairs.' This has now been corrected in both the PDF and HTML versions of the Article.
Antipsychotic-induced weight gain is a contributing factor in the reduced life expectancy reported amongst people with psychotic disorders. CYP2D6 is a liver enzyme involved in the metabolism of many ...commonly used antipsychotic medications. We investigated if
genetic variation influenced weight or BMI among people taking antipsychotic treatment.
We conducted a systematic review and a random effects meta-analysis of publications in Pubmed, Embase, PsychInfo, and CENTRAAL that had BMI and/or weight measurements of patients on long-term antipsychotics by their CYP2D6-defined metabolic groups (poor, intermediate, normal/extensive, and ultra-rapid metabolizers, UMs).
Twelve studies were included in the systematic review. All cohort studies suggested that the presence of reduced-function or non-functional alleles for
was associated with greater antipsychotic-induced weight gain, whereas most cross-sectional studies did not find any significant associations. Seventeen studies were included in the meta-analysis with clinical data of 2,041 patients, including 93 poor metabolizers (PMs), 633 intermediate metabolizers (IMs), 1,272 normal metabolizers (NMs), and 30 UMs. Overall, we did not find associations in any of the comparisons made. The estimated pooled standardized differences for the following comparisons were (i) PM versus NM; weight = -0.07 (95%CI: -0.49 to 0.35,
= 0.74), BMI = 0.40 (95%CI: -0.19 to 0.99,
= 0.19). (ii) IM versus NM; weight = 0.09 (95% CI: -0.04 to 0.22,
= 0.16) and BMI = 0.09 (95% CI: -0.24 to 0.41,
= 0.60). (iii) UM versus EM; weight = 0.01 (95% CI: -0.37 to 0.40,
= 0.94) and BMI = -0.08 (95%CI: -0.57 to 0.42,
= 0.77).
Our systematic review of cohort studies suggested that CYP2D6 poor metabolizers have higher BMI than normal metabolizers, but the data of cross-sectional studies and the meta-analysis did not show this association. Although our review and meta-analysis constitutes one of the largest studies with comprehensively genotyped samples, the literature is still limited by small numbers of participants with genetic variants resulting in poor or UMs status. We need further studies with larger numbers of extreme metabolizers to establish its clinical utility in antipsychotic treatment.
is a key gene for personalized prescribing in mental health.
Schizophrenia (SCZ) is a severe psychiatric disorder associated with large health and societal costs. Affected individuals suffer from psychiatric and somatic comorbidities that substantially reduce ...their life expectancy. Several negative health outcomes in SCZ patients are consistently associated with socioeconomic inequalities. However, the ways in which socioeconomic status (SES) affect psychiatric and somatic comorbidities of SCZ remains unclear. In this study, we use genetically informed causal inference methods to estimate whether SES partially contributes to the association of schizophrenia with a range of negative health outcomes.
Genetic correlation analysis was performed using LD Score Regression analysis between the Psychiatric Genomic Consortium SCZ genome-wide association summary statistics results and a range of psychiatric and somatic traits. Information regarding psychiatric and somatic traits and diseases was derived from genome-wide association statistics available from the UK Biobank (7,222 traits and diseases assessed in up to 484,267 participants), and FinnGen (2,803 traits and diseases assessed in up to 218,792 participants). To account for the potential causal effect of SES on schizophrenia comorbidities, we used the multi-trait conditioning and joint analysis (mtCOJO) that permitted us to correct the SCZ per-SNP effects with respect to SES. Specifically, the genetic effects were corrected with respect to the causal relationship of household income and Townsend deprivation index on SCZ inferred by Mendelian randomization (MR).
After Bonferroni correction accounting for the number of traits tested, SCZ was genetically correlated with a wide range of health outcomes in both UKB (N=344) and FinnGen (N=100). These included both psychiatric comorbidities such as depression (rg=0.43, p=4.66e-63, anxiety (rg=0.49, p=2.81e-79, mood disorders (rg=0.51, p=6.49e-99), and addiction (rg=0.4, p=4.45e-11), and somatic illnesses such as gastrointestinal (e.g., viral hepatitis: rg=0.34, p=9.82e-7), hematological (e.g., anemia: rg=0.2, p= 10e-7), respiratory (e.g., COPD: rg=0.33, p=6e-3) and urinogenital (e.g., cancer of urinary organs: rg=0.23, p=3.92e-17). MR analyses showed a putative causal effect of household income variables on SCZ (odds ratio=0.93, 95% confidence interval=0.87-0.99). However, conditioning genome-wide association statistics of SCZ for SES variables did not have a substantial impact on the genetic correlation of SCZ with comorbid conditions.
Although SES could have a causal effect on SCZ, this relationship does not appear to significantly affect the pleiotropy of SCZ with psychiatric and somatic comorbidities. Further multivariable analyses will be needed to confirm these initial findings.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
