Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages.

Importance: Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures. Objective: To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages. Design, Setting, and Participants: A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022. Main Outcomes and Measures: A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample. Results: There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample. Conclusions and Relevance: The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression.

Importance: Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives: To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants: This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures: Accuracy and generalizability of prognostic systems. Results: A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and Relevance: These findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.

New technologies for intensive prevention programs after myocardial infarction: rationale and design of the NET-IPP trial.

INTRODUCTION: Current health care data reveal suboptimal prevention in patients with coronary artery disease and an unmet need to develop effective preventive strategies. The New Technologies for Intensive Prevention Programs (NET-IPP) Trial will investigate if a long-term web-based prevention program after myocardial infarction (MI) will reduce clinical events and risk factors. In a genetic sub study the impact of disclosure of genetic risk using polygenic risk scores (PRS) will be assessed. STUDY DESIGN: Patients hospitalized for MI will be prospectively enrolled and assigned to either a 12-months web-based intensive prevention program or standard care. The web-based program will include telemetric transmission of risk factor data, e-learning and electronic contacts between a prevention assistant and the patients. The combined primary study endpoint will comprise severe adverse cardiovascular events after 2 years. Secondary endpoints will be risk factor control, adherence to medication and quality of life. In a genetic sub study genetic risk will be assessed in all patients of the web-based intensive prevention program group by PRS and patients will be randomly assigned to genetic risk disclosure vs. no disclosure. The study question will be if disclosure of genetic risk has an impact on patient motivation and cardiovascular risk factor control. CONCLUSIONS: The randomized multicenter NET-IPP study will evaluate for the first time the effects of a long-term web-based prevention program after MI on clinical events and risk factor control. In a genetic sub study the impact of disclosure of genetic risk using PRS will be investigated.

Genome-wide association analysis of chronic lymphocytic leukaemia, Hodgkin lymphoma and multiple myeloma identifies pleiotropic risk loci.

B-cell malignancies (BCM) originate from the same cell of origin, but at different maturation stages and have distinct clinical phenotypes. Although genetic risk variants for individual BCMs have been identified, an agnostic, genome-wide search for shared genetic susceptibility has not been performed. We explored genome-wide association studies of chronic lymphocytic leukaemia (CLL, N = 1,842), Hodgkin lymphoma (HL, N = 1,465) and multiple myeloma (MM, N = 3,790). We identified a novel pleiotropic risk locus at 3q22.2 (NCK1, rs11715604, P = 1.60 × 10-9) with opposing effects between CLL (P = 1.97 × 10-8) and HL (P = 3.31 × 10-3). Eight established non-HLA risk loci showed pleiotropic associations. Within the HLA region, Ser37 + Phe37 in HLA-DRB1 (P = 1.84 × 10-12) was associated with increased CLL and HL risk (P = 4.68 × 10-12), and reduced MM risk (P = 1.12 × 10-2), and Gly70 in HLA-DQB1 (P = 3.15 × 10-10) showed opposing effects between CLL (P = 3.52 × 10-3) and HL (P = 3.41 × 10-9). By integrating eQTL, Hi-C and ChIP-seq data, we show that the pleiotropic risk loci are enriched for B-cell regulatory elements, as well as an over-representation of binding of key B-cell transcription factors. These data identify shared biological pathways influencing the development of CLL, HL and MM. The identification of these risk loci furthers our understanding of the aetiological basis of BCMs.

Altered Functional Subnetwork During Emotional Face Processing: A Potential Intermediate Phenotype for Schizophrenia.

IMPORTANCE: Although deficits in emotional processing are prominent in schizophrenia, it has been difficult to identify neural mechanisms related to the genetic risk for this highly heritable illness. Prior studies have not found consistent regional activation or connectivity alterations in first-degree relatives compared with healthy controls, suggesting that a more comprehensive search for connectomic biomarkers is warranted. OBJECTIVES: To identify a potential systems-level intermediate phenotype linked to emotion processing in schizophrenia and to examine the psychological association, task specificity, test-retest reliability, and clinical validity of the identified phenotype. DESIGN, SETTING, AND PARTICIPATIONS: The study was performed in university research hospitals from June 1, 2008, through December 31, 2013. We examined 58 unaffected first-degree relatives of patients with schizophrenia and 94 healthy controls with an emotional face-matching functional magnetic resonance imaging paradigm. Test-retest reliability was analyzed with an independent sample of 26 healthy participants. A clinical association study was performed in 31 patients with schizophrenia and 45 healthy controls. Data analysis was performed from January 1 to September 30, 2014. MAIN OUTCOMES AND MEASURES: Conventional amygdala activity and seeded connectivity measures, graph-based global and local network connectivity measures, Spearman rank correlation, intraclass correlation, and gray matter volumes. RESULTS: Among the 152 volunteers included in the relative-control sample, 58 were unaffected first-degree relatives of patients with schizophrenia (mean [SD] age, 33.29 [12.56]; 38 were women), and 94 were healthy controls without a first-degree relative with mental illness (mean [SD] age, 32.69 [10.09] years; 55 were women). A graph-theoretical connectivity approach identified significantly decreased connectivity in a subnetwork that primarily included the limbic cortex, visual cortex, and subcortex during emotional face processing (cluster-level P corrected for familywise error = .006) in relatives compared with controls. The connectivity of the same subnetwork was significantly decreased in patients with schizophrenia (F = 6.29, P = .01). Furthermore, we found that this subnetwork connectivity measure was negatively correlated with trait anxiety scores (P = .04), test-retest reliable (intraclass correlation coefficient = 0.57), specific to emotional face processing (F = 17.97, P < .001), and independent of gray matter volumes of the identified brain areas (F = 1.84, P = .18). Replicating previous results, no significant group differences were found in face-related amygdala activation and amygdala-anterior cingulate cortex connectivity (P corrected for familywise error =.37 and .11, respectively). CONCLUSIONS AND RELEVANCE: Our results indicate that altered connectivity in a visual-limbic subnetwork during emotional face processing may be a functional connectomic intermediate phenotype for schizophrenia. The phenotype is reliable, task specific, related to trait anxiety, and associated with manifest illness. These data encourage the further investigation of this phenotype in clinical and pharmacologic studies.

Genome-wide association study identifies variation at 6q25.1 associated with survival in multiple myeloma.

Survival following a diagnosis of multiple myeloma (MM) varies between patients and some of these differences may be a consequence of inherited genetic variation. In this study, to identify genetic markers associated with MM overall survival (MM-OS), we conduct a meta-analysis of four patient series of European ancestry, totalling 3,256 patients with 1,200 MM-associated deaths. Each series is genotyped for ∼600,000 single nucleotide polymorphisms across the genome; genotypes for six million common variants are imputed using 1000 Genomes Project and UK10K as the reference. The association between genotype and OS is assessed by Cox proportional hazards model adjusting for age, sex, International staging system and treatment. We identify a locus at 6q25.1 marked by rs12374648 associated with MM-OS (hazard ratio=1.34, 95% confidence interval=1.22-1.48, P=4.69 × 10(-9)). Our findings have potential clinical implications since they demonstrate that inherited genotypes can provide prognostic information in addition to conventional tumor acquired prognostic factors.

Novel genetic matching methods for handling population stratification in genome-wide association studies.

BACKGROUND: A usually confronted problem in association studies is the occurrence of population stratification. In this work, we propose a novel framework to consider population matchings in the contexts of genome-wide and sequencing association studies. We employ pairwise and groupwise optimal case-control matchings and present an agglomerative hierarchical clustering, both based on a genetic similarity score matrix. In order to ensure that the resulting matches obtained from the matching algorithm capture correctly the population structure, we propose and discuss two stratum validation methods. We also invent a decisive extension to the Cochran-Armitage Trend test to explicitly take into account the particular population structure. RESULTS: We assess our framework by simulations of genotype data under the null hypothesis, to affirm that it correctly controls for the type-1 error rate. By a power study we evaluate that structured association testing using our framework displays reasonable power. We compare our result with those obtained from a logistic regression model with principal component covariates. Using the principal components approaches we also find a possible false-positive association to Alzheimer's disease, which is neither supported by our new methods, nor by the results of a most recent large meta analysis or by a mixed model approach. CONCLUSIONS: Matching methods provide an alternative handling of confounding due to population stratification for statistical tests for which covariates are hard to model. As a benchmark, we show that our matching framework performs equally well to state of the art models on common variants.

Rare variant testing of imputed data: an analysis pipeline typified.

Important methodological advancements in rare variant association testing have been made recently, among them collapsing tests, kernel methods and the variable threshold (VT) technique. Typically, rare variants from a region of interest are tested for association as a group ('bin'). Rare variant studies are already routinely performed as whole-exome sequencing studies. As an alternative approach, we propose a pipeline for rare variant analysis of imputed data and develop respective quality control criteria. We provide suggestions for the choice and construction of analysis bins in whole-genome application and support the analysis with implementations of standard burden tests (COLL, CMAT) in our INTERSNP-RARE software. In addition, three rare variant regression tests (REG, FRACREG and COLLREG) are implemented. All tests are accompanied with the VT approach which optimizes the definition of 'rareness'. We integrate kernel tests as implemented in SKAT/SKAT-O into the suggested strategies. Then, we apply our analysis scheme to a genome-wide association study of Alzheimer's disease. Further, we show that our pipeline leads to valid significance testing procedures with controlled type I error rates. Strong association signals surrounding the known APOE locus demonstrate statistical power. In addition, we highlight several suggestive rare variant association findings for follow-up studies, including genomic regions overlapping MCPH1, MED18 and NOTCH3. In summary, we describe and support a straightforward and cost-efficient rare variant analysis pipeline for imputed data and demonstrate its feasibility and validity. The strategy can complement rare variant studies with next generation sequencing data.

Genetic association signal near NTN4 in Tourette syndrome.

Tourette syndrome (TS) is a neurodevelopmental disorder with a complex genetic etiology. Through an international collaboration, we genotyped 42 single nucleotide polymorphisms (p < 10(-3) ) from the recent TS genomewide association study (GWAS) in 609 independent cases and 610 ancestry-matched controls. Only rs2060546 on chromosome 12q22 (p = 3.3 × 10(-4) ) remained significant after Bonferroni correction. Meta-analysis with the original GWAS yielded the strongest association to date (p = 5.8 × 10(-7) ). Although its functional significance is unclear, rs2060546 lies closest to NTN4, an axon guidance molecule expressed in developing striatum. Risk score analysis significantly predicted case-control status (p = 0.042), suggesting that many of these variants are true TS risk alleles.

Whole exome sequencing of distant relatives in multiplex families implicates rare variants in candidate genes for oral clefts.

A dozen genes/regions have been confirmed as genetic risk factors for oral clefts in human association and linkage studies, and animal models argue even more genes may be involved. Genomic sequencing studies should identify specific causal variants and may reveal additional genes as influencing risk to oral clefts, which have a complex and heterogeneous etiology. We conducted a whole exome sequencing (WES) study to search for potentially causal variants using affected relatives drawn from multiplex cleft families. Two or three affected second, third, and higher degree relatives from 55 multiplex families were sequenced. We examined rare single nucleotide variants (SNVs) shared by affected relatives in 348 recognized candidate genes. Exact probabilities that affected relatives would share these rare variants were calculated, given pedigree structures, and corrected for the number of variants tested. Five novel and potentially damaging SNVs shared by affected distant relatives were found and confirmed by Sanger sequencing. One damaging SNV in CDH1, shared by three affected second cousins from a single family, attained statistical significance (P = 0.02 after correcting for multiple tests). Family-based designs such as the one used in this WES study offer important advantages for identifying genes likely to be causing complex and heterogeneous disorders.

Converging Evidence for Epistasis between ANK3 and Potassium Channel Gene KCNQ2 in Bipolar Disorder.

Genome-wide association studies (GWAS) have implicated ANK3 as a susceptibility gene for bipolar disorder (BP). We examined whether epistasis with ANK3 may contribute to the "missing heritability" in BP. We first identified via the STRING database 14 genes encoding proteins with prior biological evidence that they interact molecularly with ANK3. We then tested for statistical evidence of interactions between SNPs in these genes in association with BP in a discovery GWAS dataset and two replication GWAS datasets. The most significant interaction in the discovery GWAS was between SNPs in ANK3 and KCNQ2 (p = 3.18 × 10(-8)). A total of 31 pair-wise interactions involving combinations between two SNPs from KCNQ2 and 16 different SNPs in ANK3 were significant after permutation. Of these, 28 pair-wise interactions were significant in the first replication GWAS. None were significant in the second replication GWAS, but the two SNPs from KCNQ2 were found to significantly interact with five other SNPs in ANK3, suggesting possible allelic heterogeneity. KCNQ2 forms homo- and hetero-meric complexes with KCNQ3 that constitute voltage-gated potassium channels in neurons. ANK3 is an adaptor protein that, through its interaction with KCNQ2 and KCNQ3, directs the localization of this channel in the axon initial segment (AIS). At the AIS, the KCNQ2/3 complex gives rise to the M-current, which stabilizes the neuronal resting potential and inhibits repetitive firing of action potentials. Thus, these channels act as "dampening" components and prevent neuronal hyperactivity. The interactions between ANK3 and KCNQ2 merit further investigation, and if confirmed, may motivate a new line of research into a novel therapeutic target for BP.

A mega-analysis of genome-wide association studies for major depressive disorder.

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P<0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P<5 × 10(-8)), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083-53 822 102, minimum P=5.9 × 10(-9) at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status.

FAF1, a gene that is disrupted in cleft palate and has conserved function in zebrafish.

Cranial neural crest (CNC) is a multipotent migratory cell population that gives rise to most of the craniofacial bones. An intricate network mediates CNC formation, epithelial-mesenchymal transition, migration along distinct paths, and differentiation. Errors in these processes lead to craniofacial abnormalities, including cleft lip and palate. Clefts are the most common congenital craniofacial defects. Patients have complications with feeding, speech, hearing, and dental and psychological development. Affected by both genetic predisposition and environmental factors, the complex etiology of clefts remains largely unknown. Here we show that Fas-associated factor-1 (FAF1) is disrupted and that its expression is decreased in a Pierre Robin family with an inherited translocation. Furthermore, the locus is strongly associated with cleft palate and shows an increased relative risk. Expression studies show that faf1 is highly expressed in zebrafish cartilages during embryogenesis. Knockdown of zebrafish faf1 leads to pharyngeal cartilage defects and jaw abnormality as a result of a failure of CNC to differentiate into and express cartilage-specific markers, such as sox9a and col2a1. Administration of faf1 mRNA rescues this phenotype. Our findings therefore identify FAF1 as a regulator of CNC differentiation and show that it predisposes humans to cleft palate and is necessary for lower jaw development in zebrafish.

Increased levels of glucocorticoid receptors and enhanced glucocorticoid receptor auto-regulation after hydrocortisone challenge in B-lymphoblastoids from patients with affective disorders.

The stress response is mediated by a negative feedback effect of glucocorticoids on corticosteroid receptors. Here, we examine the potential contribution of these receptors and their response to a glucocorticoid challenge to dysfunctions of the hypothalamic-pituitary-adrenal axis reported for patients with affective disorders. In a pilot-study, we established B-lymphoblastoid cell lines from patients suffering from affective disorders and healthy subjects and measured the quantity of glucocorticoid receptors at steady state conditions after 12-weeks cell culture. After short-term incubation with 0.1 microM hydrocortisone for 48 h, the decrease of glucocorticoid receptors was also investigated. After 12-weeks cell culture, we found a significantly higher number of cytosolic glucocorticoid receptors in B-lymphoblastoids from patients (B(max)=804.9+/-342.5 fmol/mg protein) compared to those from healthy subjects (B(max)=576.9+/-190.3 fmol/mg protein: p=0.045; t-test). The increase of the glucocorticoid receptor level in the group of patients could be attributed largely to the higher number of these receptors measured in B-lymphoblastoids of patients suffering from major depressive disorder. The in vitro regulation of glucocorticoid receptors in response to 0.1 microM hydrocortisone for 48 h resulted in a significantly larger decrease in cultures of B-lymphoblastoids derived from patients (to 32.9+/-7.5%) than in those from healthy subjects (to 45.8+/-8.2%). The stronger decrease of glucocorticoid receptors in the group of patients (p=0.0001; t-test) was independent of the duration of illness and medication, suggesting a trait-like characteristic of the response.