Showing papers by "Nancy L. Saccone published in 2021"

Review and Consensus on Pharmacogenomic Testing in Psychiatry

Abstract: The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (CYP2D6, CYP2C19). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (HLA-A and HLA-B), oxcarbazepine (HLA-B), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes (POLG, OTC, CSP1) is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.

Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies

Abstract: Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [rg ], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (rg = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (rg = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (rgs = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.

Identification of a novel genetic marker for risk of degenerative rotator cuff disease surgery in the UK Biobank

Abstract: BACKGROUND While evidence indicates that familial predisposition influences the risk of developing degenerative rotator cuff disease (RCD), knowledge of specific genetic markers is limited. We conducted a genome-wide association study of RCD surgery using the UK Biobank, a prospective cohort of 500,000 people (40 to 69 years of age at enrollment) with genotype data. METHODS Cases with surgery for degenerative RCD were identified using linked hospital records. The cases were defined as an International Classification of Diseases, Tenth Revision (ICD-10) code of M75.1 determined by a trauma/orthopaedic specialist and surgery consistent with RCD treatment. Cases were excluded if a diagnosis of traumatic injury had been made during the same hospital visit. For each case, up to 5 controls matched by age, sex, and follow-up time were chosen from the UK Biobank. Analyses were limited to European-ancestry individuals who were not third-degree or closer relations. We used logistic regression to test for genetic association of 674,405 typed and >10 million imputed markers, after adjusting for age, sex, population principal components, and follow-up. RESULTS We identified 2,917 RCD surgery cases and 14,158 matched controls. We observed 1 genome-wide significant signal (p < 5 × 10-8) for a novel locus tagged by rs2237352 in the CREB5 gene on chromosome 7 (odds ratio [OR] = 1.17, 95% confidence interval [CI] = 1.11 to 1.24). The single-nucleotide polymorphism (SNP) rs2237352 was imputed with a high degree of confidence (info score = 0.9847) and is common, with a minor allele frequency of 47%. After expanding the control sample to include additional unmatched non-cases, rs2237352 and another SNP in the CREB5 gene, rs12700903, were genome-wide significant. We did not detect genome-wide significant signals at loci associated with RCD in previous studies. CONCLUSIONS We identified a novel association between a variant in the CREB5 gene and RCD surgery. Validation of this finding in studies with imaging data to confirm diagnoses will be an important next step. CLINICAL RELEVANCE Identification of genetic RCD susceptibility markers can guide understanding of biological processes in rotator cuff degeneration and help inform disease risk in the clinical setting. LEVEL OF EVIDENCE Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Studying the Utility of Using Genetics to Predict Smoking-Related Outcomes in a Population-Based Study and a Selected Cohort.

Abstract: Objective The purpose of this study is to examine the predictive utility of polygenic risk scores (PRSs) for smoking behaviors. Methods Using summary statistics from the GWAS and Sequencing Consortium of Alcohol and Nicotine use consortium, we generated PRSs of ever smoking, age of smoking initiation, cigarettes smoked per day, and smoking cessation for participants in the population-based Atherosclerosis Risk in Communities (ARIC) study (N=8,638), and the Collaborative Genetic Study of Nicotine Dependence (COGEND) (N=1,935). The outcomes were ever smoking, age of smoking initiation, heaviness of smoking, and smoking cessation. Results In the European ancestry cohorts, each PRS was significantly associated with the corresponding smoking behavior outcome. In the ARIC cohort, the z-score ever smoking PRS predicted ever smoking (odds ratio [OR]: 1.37; 95% confidence interval [CI]: 1.31, 1.43); the z-score age of smoking initiation PRS was associated with earlier age of smoking initiation (OR:0.87: 95% CI: 0.82, 0.92); the z-score cigarettes per day PRS was associated with heavier smoking (OR:1.17: 95% CI: 1.11, 1.25); and the z-score smoking cessation PRS predicted with successful cessation (OR: 1.24: 95% CI: 1.17, 1.32). In the African ancestry cohort, the PRSs did not predict smoking behaviors. Conclusion Smoking-related PRSs were associated with smoking-related behaviors in European ancestry populations. This improvement in prediction is greatest in the lowest and highest genetic risk categories. The lack of prediction in African ancestry populations highlights the urgent need to increase diversity in research so that scientific advances can be applied to populations other than those of European ancestry. Implications This study shows that including both genetic ancestry and polygenic risk scores in a single model increases the ability to predict smoking behaviors compared to the model including only demographic characteristics. This finding is observed for every smoking-related outcome. Even though adding genetics is more predictive, the demographics alone confer substantial and meaningful predictive power. However, with increasing work in polygenic risk scores, the predictive ability will continue to improve.