Pharmacogenomics: A road ahead for precision medicine in psychiatry

Psychiatric genomics is providing insights into the nature of psychiatric conditions that in time should identify new drug targets and improve patient care. Less attention has been paid to psychiatric pharmacogenomics research, despite its potential to deliver more rapid change in clinical practice and patient outcomes. The pharmacogenomics of treatment response encapsulates both pharmacokinetic (“what the body does to a drug”) and pharmacodynamic (“what the drug does to the body”) effects. Despite early optimism and substantial research in both these areas, they have to date made little impact on clinical management in psychiatry. A number of bottlenecks have hampered progress, including a lack of large-scale replication studies, inconsistencies in defining valid treatment outcomes across experiments, a failure to routinely incorporate adverse drug reactions and serum metabolite monitoring in study designs, and inadequate investment in the longitudinal data collections required to demonstrate clinical utility. Nonetheless, advances in genomics and health informatics present distinct opportunities for psychiatric pharmacogenomics to enter a new and productive phase of research discovery and translation

Genome-wide association studies in schizophrenia: Recent advances, challenges and future perspective

Genome-wide association studies (GWAS) have proved to be a powerful approach for gene discovery in schizophrenia; their findings have important implications not just for our understanding of the genetic architecture of the disorder, but for the potential applications of personalised medicine through improved classification and targeted interventions. In this article we review the current status of the GWAS literature in schizophrenia including functional annotation methods and polygenic risk scoring, as well as the directions and challenges of future research. We consider recent findings in East Asian populations and the advancements from trans-ancestry analysis, as well as the insights gained from research looking across psychiatric disorders

Medical diagnostic methods applied to a medieval female with vitamin D deficiency from the North of Spain

Vitamin D deficiency is a pathological condition that affects bone metabolism by preventing proper mineralization, which eventually leads to bone deformities and other pathological conditions such as osteoporosis, increased bone fragility and fractures. The aim of this study is to present a case of vitamin D deficiency, but also to note how the application of several complementary techniques is a fundamental step in the establishing an accurate diagnosis. These techniques range from classical palaeopathological analysis to modern clinical practice. After the macroscopic examination of a medieval female skeleton from Palencia (Spain), where various bone deformations were observed, a differential diagnosis could not establish a definitive cause. Radiological, bone density, and histological studies were carried out, finally allowing to confirm a vitamin D deficiency suffered in both childhood and adulthood. This is a clear example, with practical applications, of the importance of interdisciplinarity to reveal insights about the life history and physical health of ancient individuals

Artificial intelligence for analyzing mental health disorders in social media: a quarter-century narrative review of progress and challenges

Background: Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter-century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. Objective: This study examines the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of AI-driven methods, particularly Natural Language Processing (NLP), for analyzing large volumes of written text. We also detail constraints affecting research in this area. These include the dearth of high-quality public data sets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma and vulnerability of those affected by mental illness. Methods: A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by four primary areas of interest: data sets on social media and mental health, methods for predicting mental health status, longitudinal analyses on mental health, and ethical aspects on the data and analysis of mental health. Selected articles from these groups formed our narrative review. Results: Larger data sets where precise dates of subjects’ diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting participants to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using NLP alone. Conclusions: Multimodal methods for predicting mental health status from social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. For this to be achieved, more high-quality social media data sets need to be made available and privacy concerns regarding the use of this data must be formally addressed. Also, a review of literature studying the effects of social media use on a user’s depression and anxiety is merited

The relationship between common variant schizophrenia liability and number of offspring in the UK Biobank

Objective: Schizophrenia is associated with a marked reduction in reproductive success, yet alleles that are common contribute substantially to the liability of the disorder. Among several possible explanations for this, it has been postulated that individuals who carry risk alleles but are unaffected are at some reproductive advantage, offsetting the effects of negative selection among those who are affected. The authors sought to test this hypothesis, isolating the effects of risk alleles on fecundity from the effects that are contingent on expressing schizophrenia. Methods: The burden of schizophrenia risk alleles, as indexed by a polygenic risk score (PRS), carried by 139,679 participants in the UK Biobank study who did not have schizophrenia was compared with the number of offspring of these individuals. Results: Higher schizophrenia liability in study subjects without manifest disorder was weakly but significantly associated with having more children (B=0.006, 95% CI=0.002, 0.010). The relationship was dependent on sex, with a positive correlation between number of children and liability among females (B=0.011, 95% CI=0.006, 0.016), whereas among males, higher liability was associated with being childless (odds ratio=0.96, 95% CI=0.94, 0.98). The negative effect on number of children associated with schizophrenia itself was twofold to 15-fold greater than the positive effect associated with PRS in unaffected individuals. Conclusions: These findings suggest that a complex relationship between liability and fecundity is consistent with sexual selection. Although the overall pattern of a weak positive correlation with liability may contribute to the persistence of schizophrenia risk alleles, these results indicate that the negative selection acting on individuals affected by schizophrenia in the general population is larger than any advantage conferred by genetic loading in unaffected individuals

Medical consequences of pathogenic CNVs in adults: Analysis of the UK Biobank

Background: Genomic CNVs increase the risk for early-onset neurodevelopmental disorders, but their impact on medical outcomes in later life is still poorly understood. The UK Biobank allows us to study the medical consequences of CNVs in middle and old age in half a million well-phenotyped adults. Methods: We analysed all Biobank participants for the presence of 54 CNVs associated with genomic disorders or clinical phenotypes, including their reciprocal deletions or duplications. After array quality control and exclusion of first-degree relatives, we compared 381 452 participants of white British or Irish origin who carried no CNVs with carriers of each of the 54 CNVs (ranging from 5 to 2843 persons). We used logistic regression analysis to estimate the risk of developing 58 common medical phenotypes (3132 comparisons). Results and conclusions: Many of the CNVs have profound effects on medical health and mortality, even in people who have largely escaped early neurodevelopmental outcomes. Forty-six CNV–phenotype associations were significant at a false discovery rate threshold of 0.1, all in the direction of increased risk. Known medical consequences of CNVs were confirmed, but most identified associations are novel. Deletions at 16p11.2 and 16p12.1 had the largest numbers of significantly associated phenotypes (seven each). Diabetes, hypertension, obesity and renal failure were affected by the highest numbers of CNVs. Our work should inform clinicians in planning and managing the medical care of CNV carriers

Pharmacogenomic variants and drug interactions identified through the genetic analysis of clozapine metabolism

Objective: Clozapine is the only effective medication for treatment-resistant schizophrenia, but its worldwide use is still limited because of its complex titration protocols. While the discovery of pharmacogenomic variants of clozapine metabolism may improve clinical management, no robust findings have yet been reported. This study is the first to adopt the framework of genome-wide association studies (GWASs) to discover genetic markers of clozapine plasma concentrations in a large sample of patients with treatment-resistant schizophrenia. Methods: The authors used mixed-model regression to combine data from multiple assays of clozapine metabolite plasma concentrations from a clozapine monitoring service and carried out a genome-wide analysis of clozapine, norclozapine, and their ratio on 10,353 assays from 2,989 individuals. These analyses were adjusted for demographic factors known to influence clozapine metabolism, although it was not possible to adjust for all potential mediators given the available data. GWAS results were used to pinpoint specific enzymes and metabolic pathways and compounds that might interact with clozapine pharmacokinetics. Results: The authors identified four distinct genome-wide significant loci that harbor common variants affecting the metabolism of clozapine or its metabolites. Detailed examination pointed to coding and regulatory variants at several CYP* and UGT* genes as well as corroborative evidence for interactions between the metabolism of clozapine, coffee, and tobacco. Individual effects of single single-nucleotide polymorphisms (SNPs) fine-mapped from these loci were large, such as the minor allele of rs2472297, which was associated with a reduction in clozapine concentrations roughly equivalent to a decrease of 50 mg/day in clozapine dosage. On their own, these single SNPs explained from 1.15% to 9.48% of the variance in the plasma concentration data. Conclusions: Common genetic variants with large effects on clozapine metabolism exist and can be found via genome-wide approaches. Their identification opens the way for clinical studies assessing the use of pharmacogenomics in the clinical management of patients with treatment-resistant schizophrenia

A transcriptome-wide association study implicates specific pre- and post-synaptic abnormalities in schizophrenia

chizophrenia is a complex highly heritable disorder. Genome-wide association studies (GWAS) have identified multiple loci that influence the risk of developing schizophrenia, although the causal variants driving these associations and their impacts on specific genes are largely unknown. We identify a significant correlation between schizophrenia risk and expression at 89 genes in dorsolateral prefrontal cortex (P ≤ 9.43x10−6), including 20 novel genes. Genes whose expression correlate with schizophrenia were enriched for those involved in abnormal CNS synaptic transmission (PFDR = 0.02) and antigen processing and presentation of peptide antigen via MHC class I (PFDR = 0.02). Within the CNS synaptic transmission set, we identify individual significant candidate genes to which we assign direction of expression changes in schizophrenia. The findings provide strong candidates for experimentally probing the molecular basis of synaptic pathology in schizophrenia

Novel insight into the etiology of autism spectrum disorder gained by integrating expression data with genome-wide association statistics

Background A recent genome-wide association study (GWAS) of autism spectrum disorders (ASD) (Ncases=18,381, Ncontrols=27,969) has provided novel opportunities for investigating the aetiology of ASD. Here, we integrate the ASD GWAS summary statistics with summary-level gene expression data to infer differential gene expression in ASD, an approach called transcriptome-wide association study (TWAS). Methods Using FUSION software, ASD GWAS summary statistics were integrated with predictors of gene expression from 16 human datasets, including adult and fetal brain. A novel adaptation of established statistical methods was then used to test for enrichment within candidate pathways, specific tissues, and at different stages of brain development. The proportion of ASD heritability explained by predicted expression of genes in the TWAS was estimated using stratified linkage disequilibrium-score regression. Results This study identified 14 genes as significantly differentially expressed in ASD, 13 of which were outside of known genome-wide significant loci (±500kb). XRN2, a gene proximal to an ASD GWAS locus, was inferred to be significantly upregulated in ASD, providing insight into functional consequence of this associated locus. One novel transcriptome-wide significant association from this study is the downregulation of PDIA6, which showed minimal evidence of association in the GWAS, and in gene-based analysis using MAGMA. Predicted gene expression in this study accounted for 13.0% of the total ASD SNP-heritability. Conclusion This study has implicated several genes as significantly up-/down-regulated in ASD providing novel and useful information for subsequent functional studies. This study also explores the utility of TWAS-based enrichment analysis and compares TWAS results with a functionally agnostic approach

A genome-wide association study in individuals of African ancestry reveals the importance of the Duffy-null genotype in the assessment of clozapine-related neutropenia

Individuals of African ancestry in the United States and Europe are at increased risk of developing schizophrenia and have poorer clinical outcomes. The antipsychotic clozapine, the only licensed medication for treatment-resistant schizophrenia, is under-prescribed and has high rates of discontinuation in individuals of African ancestry, due in part to increased rates of neutropenia. The genetic basis of lower neutrophil levels in those of African ancestry has not previously been investigated in the context of clozapine treatment. We sought to identify risk alleles in the first genome-wide association study of neutrophil levels during clozapine treatment, in 552 individuals with treatment-resistant schizophrenia and robustly inferred African genetic ancestry. Two genome-wide significant loci were associated with low neutrophil counts during clozapine treatment. The most significantly associated locus was driven by rs2814778 (β = −0.9, P = 4.21 × 10−21), a known regulatory variant in the atypical chemokine receptor 1 (ACKR1) gene. Individuals homozygous for the C allele at rs2814778 were significantly more likely to develop neutropenia and have to stop clozapine treatment (OR = 20.4, P = 3.44 × 10−7). This genotype, also termed “Duffy-null”, has previously been shown to be associated with lower neutrophil levels in those of African ancestry. Our results indicate the relevance of the rs2814778 genotype for those taking clozapine and its potential as a pharmacogenetic test, dependent on the outcome of additional safety studies, to assist decision making in the initiation and on-going management of clozapine treatmen