The Human Phenotype Ontology in 2024: phenotypes around the world.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

Association of polygenic score and the involvement of cholinergic and glutamatergic pathways with lithium treatment response in patients with bipolar disorder.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N = 2367) and replicated in the combined PsyCourse (N = 89) and BipoLife (N = 102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P < 0.05. Li+PGS was positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P = 9.8 × 10-12, R2 = 1.9%) and continuous (P = 6.4 × 10-9, R2 = 2.6%) outcomes. Compared to bipolar patients in the 1st decile of the risk distribution, individuals in the 10th decile had 3.47-fold (95%CI: 2.22-5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P = 3.9 × 10-4, R2 = 0.9%), but not for the continuous outcome (P = 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGS may be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.

Using polygenic scores and clinical data for bipolar disorder patient stratification and lithium response prediction: machine learning approach.

BACKGROUND: Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment. AIMS: To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder. METHOD: This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework. RESULTS: The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data. CONCLUSIONS: Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Association of polygenic score for major depression with response to lithium in patients with bipolar disorder.

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS p value thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Alda scale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various p value thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18-2.01) and European sample: OR = 1.75 (95% CI: 1.30-2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61-4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.

Harmonized Outcome Measures for Use in Depression Patient Registries and Clinical Practice.

Major depressive disorder is a common mental health condition that affects an estimated 16.2 million adults and 3.1 million adolescents in the United States. Yet, a lack of uniformity remains in measurements and monitoring for depression both in clinical practice and in research settings. This project aimed to develop a minimum set of standardized outcome measures relevant to both patients and clinicians that can be collected in depression registries and clinical practice. Twenty-nine depression registries and related data collection efforts were identified and invited to submit outcome measures. Additional measures were identified through literature searches and reviews of quality measures. A multistakeholder panel representing clinicians; payers; government agencies; industry; and medical specialty, health care quality, and patient advocacy organizations categorized the 27 identified measures using the Agency for Healthcare Research and Quality's supported Outcome Measures Framework. The panel identified 10 broadly relevant measures and harmonized definitions for these measures through in-person and virtual meetings. The harmonized measures represent a minimum set of outcomes that are relevant to clinicians and patients and appropriate for use in depression research and clinical practice. Routine and consistent collection of these measures in registries and other systems would support creation of a national research infrastructure to efficiently address new questions, improve patient management and outcomes, and facilitate care coordination.

Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder.

Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behaviour. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ∼2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the X-chromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, P = 5.87 × 10 - 9; odds ratio (OR) = 1.12) and markers within ERBB2 (rs2517959, P = 4.53 × 10 - 9; OR = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.

One Hundred Years of Psychiatry at Johns Hopkins: A Story of Meyer to McHugh.

This article describes a history of clinical methods and constructs that guide Psychiatry at Johns Hopkins Phipps Clinic today. The contributions of Adolf Meyer and Paul McHugh are central and closely connected. Both emphasize the clinical examination as the central practice of psychiatry as a specialty within medicine. Meyer's comprehensive examination of the patient became the centerpiece of his approach and was the standard for psychiatrists in the English-speaking world. McHugh, with Phillip Slavney, developed a pluralistic and practical framework for interpreting that history and examination. Both argued against the uncritical use of the modern disease construct. McHugh argues that the disease construct, although fundamental, is but one of four useful "perspectives of psychiatry" and is, thus, an insufficient basis for psychiatric practice. The perspectives could be used as an organizing framework by all physicians who seek a practical and truly personalized approach to the care of patients.

Lithium response in bipolar disorder is associated with focal adhesion and PI3K-Akt networks: a multi-omics replication study.

Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

Exploring the genetics of lithium response in bipolar disorders.

BACKGROUND: Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N = 2064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. RESULTS: We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. CONCLUSIONS: Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.

Association of Polygenic Score and the involvement of Cholinergic and Glutamatergic Pathways with Lithium Treatment Response in Patients with Bipolar Disorder.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium's possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGS was developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N=2,367) and replicated in the combined PsyCourse (N=89) and BipoLife (N=102) studies. The associations of Li+PGS and lithium treatment response - defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P<����������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������.

Immunogenetics of lithium response and psychiatric phenotypes in patients with bipolar disorder.

The link between bipolar disorder (BP) and immune dysfunction remains controversial. While epidemiological studies have long suggested an association, recent research has found only limited evidence of such a relationship. To clarify this, we investigated the contributions of immune-relevant genetic factors to the response to lithium (Li) treatment and the clinical presentation of BP. First, we assessed the association of a large collection of immune-related genes (4,925) with Li response, defined by the Retrospective Assessment of the Lithium Response Phenotype Scale (Alda scale), and clinical characteristics in patients with BP from the International Consortium on Lithium Genetics (ConLi+Gen, N = 2,374). Second, we calculated here previously published polygenic scores (PGSs) for immune-related traits and evaluated their associations with Li response and clinical features. We found several genes associated with Li response at p < 1×10- 4 values, including HAS3, CNTNAP5 and NFIB. Network and functional enrichment analyses uncovered an overrepresentation of pathways involved in cell adhesion and intercellular communication, which appear to converge on the well-known Li-induced inhibition of GSK-3ß. We also found various genes associated with BP's age-at-onset, number of mood episodes, and presence of psychosis, substance abuse and/or suicidal ideation at the exploratory threshold. These included RTN4, XKR4, NRXN1, NRG1/3 and GRK5. Additionally, PGS analyses suggested serum FAS, ECP, TRANCE and cytokine ligands, amongst others, might represent potential circulating biomarkers of Li response and clinical presentation. Taken together, our results support the notion of a relatively weak association between immunity and clinically relevant features of BP at the genetic level.

Exploring the genetics of lithium response in bipolar disorders.

Background: Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N=2,064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. Results: We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. Conclusions: Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.

Lithium Response in Bipolar Disorder is Associated with Focal Adhesion and PI3K-Akt Networks: A Multi-omics Replication Study.

Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2,039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

Total white matter hyperintensity volume in bipolar disorder patients and their healthy relatives.

OBJECTIVES: White matter hyperintensities (WMH) are more common in subjects with bipolar disorder (BP) than in healthy subjects (HS). Few studies have examined the effect of the diagnostic type of bipolar illness on WMH burden, and none have approached this question through a direct measurement of the volume of affected white matter in relationship to familiality. In this pilot study, we utilized a volumetric measurement of WMH to investigate the relationship between the total volume of WMH and the familiality and type of BP. METHODS: Forty-five individuals with bipolar I disorder (BP-I) with psychotic features, BP-I without psychotic features, or bipolar II disorder (BP-II), seven of their unaffected relatives, and 32 HS were recruited for participation. T-2 weighted magnetic resonance imaging scans were obtained on all subjects, and the total volume of all WMH for each subject was measured in cubic centimeters. The significance of difference between groups was tested using ANOVA with post-hoc adjustment for multiple comparisons. Further, we used logistic regression to test for trends between symptom load and total WMH volume. RESULTS: The mean total volume of WMH in BP-I patients with psychotic features was significantly higher (p < 0.05) than that of HS. Further, we observed a positive linear trend by familiality and type of affectedness when comparing mean total WMH volume of HS, unaffected family members, subjects with BP-II, and BP-I with and without a history of psychosis (p < 0.05). CONCLUSIONS: Based on a quantitative technique, WMH burden appears to be associated with familiality and type of BP. The significance of these findings remains to be fully elucidated.

Association study of serotonin pathway genes in attempted suicide.

Epidemiological studies, such as family, twin, and adoption studies, demonstrate the presence of a heritable component to both attempted and completed suicide. Some of this heritability is accounted for by the presence of comorbid psychiatric disorders, but the evidence also indicates that a portion of this heritability is specific to suicidality. The serotonergic system has been studied extensively in this phenotype, but findings have been inconsistent, possibly due to the presence of multiple susceptibility variants and/or gene-gene interactions. In this study, we genotyped 174 tag and coding single nucleotide polymorphisms (SNPs) from 17 genes within the serotonin pathway on 516 subjects with a major mood disorder and a history of a suicide attempt (cases) and 515 healthy controls, with the goal of capturing the common genetic variation across each of these candidate genes. We tested the 174 markers in single-SNP, haplotype, gene-based, and epistasis analyses. While these association analyses identified multiple marginally significant SNPs, haplotypes, genes, and interactions, none of them survived correction for multiple testing. Additional studies, including assessment in larger sample sets and deep resequencing to identify rare causal variants, may be required to fully understand the role that the serotonin pathway plays in suicidal behavior.

"Supporting Wellness": A depression and bipolar support alliance mixed-methods investigation of lived experience perspectives and priorities for mood disorder treatment.

BACKGROUND: The lived experience of people with mood disorders may be leveraged to inform priorities for research, define key treatment outcomes, and support decision-making in clinical care. The aim of this mixed-methods project was to provide insight into how people with depression and bipolar disorder experience the impact of symptoms, their treatment preferences, and their definitions of wellness. METHODS: The project was implemented in two phases. In Phase 1, community-based participatory research was used to develop a web-based survey enquiring about living with a mood disorder, treatment experiences, and wellness priorities. In Phase 2, a series of focus groups were conducted to explore aspects of wellness in greater detail. RESULTS: Respondents (n= 6153) described the symptoms of mood disorders as having a significant, chronic impact on their lives. A holistic approach to treatment was desired by participants, but not necessarily experienced. Qualitative findings were used to further describe four highly ranked wellness priorities identified in the survey: ability to act independently or according to my own will; purpose in life; getting through the day; and contentment. LIMITATIONS: Experience of a mood disorder was self-reported, and no formal confirmation of diagnosis occurred. Although the survey could not incorporate all possible wellness definitions, this was supplemented by qualitative focus groups. CONCLUSION: The present findings provide important insights from the perspectives of individuals with lived experience of mood disorders. Implications of this for research and clinical practice are discussed, particularly with regards to measurement-based care and use of wellness-oriented clinical outcome assessments.

Genome-wide association analysis of age at onset and psychotic symptoms in bipolar disorder.

Genome-wide association studies (GWAS) have identified several susceptibility loci for bipolar disorder (BP), most notably ANK3. However, most of the inherited risk for BP remains unexplained. One reason for the limited success may be the genetic heterogeneity of BP. Clinical sub-phenotypes of BP may identify more etiologically homogeneous subsets of patients, which can be studied with increased power to detect genetic variation. Here, we report on a mega-analysis of two widely studied sub-phenotypes of BP, age at onset and psychotic symptoms, which are familial and clinically significant. We combined data from three GWAS: NIMH Bipolar Disorder Genetic Association Information Network (GAIN-BP), NIMH Bipolar Disorder Genome Study (BiGS), and a German sample. The combined sample consisted of 2,836 BP cases with information on sub-phenotypes and 2,744 controls. Imputation was performed, resulting in 2.3 million SNPs available for analysis. No SNP reached genome-wide significance for either sub-phenotype. In addition, no SNP reached genome-wide significance in a meta-analysis with an independent replication sample. We had 80% power to detect associations with a common SNP at an OR of 1.6 for psychotic symptoms and a mean difference of 1.8 years in age at onset. Age at onset and psychotic symptoms in BP may be influenced by many genes of smaller effect sizes or other variants not measured well by SNP arrays, such as rare alleles.