Comprehensive risk factor predictions for 3-year survival among HIV-associated and disseminated cryptococcosis involving lungs and central nervous system.

BACKGROUND: The global mortality rate resulting from HIV-associated cryptococcal disease is remarkably elevated, particularly in severe cases with dissemination to the lungs and central nervous system (CNS). Regrettably, there is a dearth of predictive analysis regarding long-term survival, and few studies have conducted longitudinal follow-up assessments for comparing anti-HIV and antifungal treatments. METHODS: A cohort of 83 patients with HIV-related disseminated cryptococcosis involving the lung and CNS was studied for 3 years to examine survival. Comparative analysis of clinical and immunological parameters was performed between deceased and surviving individuals. Subsequently, multivariate Cox regression models were utilized to validate mortality predictions at 12, 24, and 36 months. RESULTS: Observed plasma cytokine levels before treatment were significantly lower for IL-1RA (p < 0.001) and MCP-1 (p < 0.05) when in the survivor group. Incorporating plasma levels of IL-1RA, IL-6, and high-risk CURB-65 score demonstrated the highest area under curve (AUC) value (0.96) for predicting 1-year mortality. For 1-, 2- and 3-year predictions, the single-factor model with IL-1RA demonstrated superior performance compared to all multiple-variate models (AUC = 0.95/0.78/0.78). CONCLUSIONS: IL-1RA is a biomarker for predicting 3-year survival. Further investigations to explore the pathogenetic role of IL-1RA in HIV-associated disseminated cryptococcosis and as a potential therapeutic target are warranted.

Identifying genetic differences between bipolar disorder and major depression through multiple GWAS.

Background: Accurate diagnosis of bipolar disorder (BD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A key reason is that the first manic episode is often preceded by a depressive one, making it difficult to distinguish BD from unipolar major depressive disorder (MDD). Aims: Here, we use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores that may aid early differential diagnosis. Methods: Based on individual genotypes from case-control cohorts of BD and MDD shared through the Psychiatric Genomics Consortium, we compile case-case-control cohorts, applying a careful merging and quality control procedure. In a resulting cohort of 51,149 individuals (15,532 BD cases, 12,920 MDD cases and 22,697 controls), we perform a variety of GWAS and polygenic risk scores (PRS) analyses. Results: While our GWAS is not well-powered to identify genome-wide significant loci, we find significant SNP-heritability and demonstrate the ability of the resulting PRS to distinguish BD from MDD, including BD cases with depressive onset. We replicate our PRS findings, but not signals of individual loci in an independent Danish cohort (iPSYCH 2015 case-cohort study, N=25,966). We observe strong genetic correlation between our case-case GWAS and that of case-control BD. Conclusions: We find that MDD and BD, including BD with a depressive onset, are genetically distinct. Further, our findings support the hypothesis that Controls - MDD - BD primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BD and, importantly, BD with depressive onset from MDD.

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.

Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups. Discussion: netMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

Burnout and secondary traumatic stress in staff working with people with intellectual disabilities: The role of adverse childhood experiences, resilience and trauma-informed organisational climate.

BACKGROUND: There has been extensive research into adverse childhood experiences (ACEs), however, less consideration has been given to the prevalence and impact of ACEs for staff working with people with intellectual disabilities. METHOD: Participants were staff employed by agencies that care for people with intellectual disabilities. An online survey collected demographic information and measures of ACEs, resilience, trauma-informed organisational climate, burnout and secondary traumatic stress. Correlation, regression, mediation and moderation analyses were used. RESULTS: 81.7% of 109 participants had experienced at least one ACE. Burnout, secondary traumatic stress and resilience were greater in the present study than in comparable samples. Trauma-informed organisational climate significantly predicted burnout and secondary traumatic stress. Resilience significantly predicted burnout. CONCLUSIONS: Staff working with people with intellectual disabilities are likely to have experienced ACEs. Working in a trauma-informed organisational climate and resilience may be effective avenues for reducing burnout and secondary traumatic stress.

High genetic risk for depression as an independent risk factor for mortality in patients referred for coronary angiography.

Background: Different observations have suggested that patients with depression have a higher risk for a number of comorbidities and mortality. The underlying causes have not been fully understood yet. Aims: The aim of our study was to investigate the association of a genetic depression risk score (GDRS) with mortality [all-cause and cardiovascular (CV)] and markers of depression (including intake of antidepressants and a history of depression) in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study involving 3,316 patients who had been referred for coronary angiography. Methods and results: The GDRS was calculated in 3,061 LURIC participants according to a previously published method and was found to be associated with all-cause (p = 0.016) and CV mortality (p = 0.0023). In Cox regression models adjusted for age, sex, body mass index, LDL-cholesterol, HDL-cholesterol, triglycerides, hypertension, smoking, and diabetes mellitus, the GDRS remained significantly associated with all-cause [1.18 (1.04-1.34, p = 0.013)] and CV [1.31 (1.11-1.55, p = 0.001)] mortality. The GDRS was not associated with the intake of antidepressants or a history of depression. However, this cohort of CV patients had not specifically been assessed for depression, leading to marked underreporting. We were unable to identify any specific biomarkers correlated with the GDRS in LURIC participants. Conclusion: A genetic predisposition for depression estimated by a GDRS was independently associated with all-cause and CV mortality in our cohort of patients who had been referred for coronary angiography. No biomarker correlating with the GDRS could be identified.

Genetic structure of major depression symptoms across clinical and community cohorts.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

Automatically annotated motion tracking identifies a distinct social behavioral profile following chronic social defeat stress.

Severe stress exposure increases the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and behavioral changes. Current markerless pose estimation tools allow for more complex and naturalistic behavioral tests. Here, we introduce the open-source tool DeepOF to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut-annotated pose estimation data. Applying this tool to chronic social defeat in male mice, the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter and fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. Moreover, DeepOF aims to facilitate reproducibility and unification of behavioral classification by providing an open-source tool, which can advance the study of rodent individual and social behavior, thereby enabling biological insights and, for example, subsequent drug development for psychiatric disorders.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases.

Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity.

Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

Advancing social behavioral neuroscience by integrating ethology and comparative psychology methods through machine learning.

Social behavior is naturally occurring in vertebrate species, which holds a strong evolutionary component and is crucial for the normal development and survival of individuals throughout life. Behavioral neuroscience has seen different influential methods for social behavioral phenotyping. The ethological research approach has extensively investigated social behavior in natural habitats, while the comparative psychology approach was developed utilizing standardized and univariate social behavioral tests. The development of advanced and precise tracking tools, together with post-tracking analysis packages, has recently enabled a novel behavioral phenotyping method, that includes the strengths of both approaches. The implementation of such methods will be beneficial for fundamental social behavioral research but will also enable an increased understanding of the influences of many different factors that can influence social behavior, such as stress exposure. Furthermore, future research will increase the number of data modalities, such as sensory, physiological, and neuronal activity data, and will thereby significantly enhance our understanding of the biological basis of social behavior and guide intervention strategies for behavioral abnormalities in psychiatric disorders.

Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder.

Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health.

Creating sparser prediction models of treatment outcome in depression: a proof-of-concept study using simultaneous feature selection and hyperparameter tuning.

BACKGROUND: Predicting treatment outcome in major depressive disorder (MDD) remains an essential challenge for precision psychiatry. Clinical prediction models (CPMs) based on supervised machine learning have been a promising approach for this endeavor. However, only few CPMs have focused on model sparsity even though sparser models might facilitate the translation into clinical practice and lower the expenses of their application. METHODS: In this study, we developed a predictive modeling pipeline that combines hyperparameter tuning and recursive feature elimination in a nested cross-validation framework. We applied this pipeline to a real-world clinical data set on MDD treatment response and to a second simulated data set using three different classification algorithms. Performance was evaluated by permutation testing and comparison to a reference pipeline without nested feature selection. RESULTS: Across all models, the proposed pipeline led to sparser CPMs compared to the reference pipeline. Except for one comparison, the proposed pipeline resulted in equally or more accurate predictions. For MDD treatment response, balanced accuracy scores ranged between 61 and 71% when models were applied to hold-out validation data. CONCLUSIONS: The resulting models might be particularly interesting for clinical applications as they could reduce expenses for clinical institutions and stress for patients.

ExomeChip-based rare variant association study in restless legs syndrome.

Restless legs syndrome (RLS) is a common sleep-related movement disorder in populations of European descent and disease risk is strongly influenced by genetic factors. Common variants have been assessed extensively in several genome-wide association studies, but the contribution of rarer genetic variation has not been investigated at this scale. We therefore genotyped a case-control set of 9246 individuals for mainly rare and low frequency exonic variants using the Illumina ExomeChip. However, standard single variant and gene-level association tests were negative. This does not preclude a role of rare variants in RLS, but is likely due to the small sample size and the limited selection of rare genetic variation captured on the array. Therefore, exome or whole genome sequencing should be performed rather than increasing the sample size of ExomeChip studies in order to identify rare risk variants for RLS.

DNA-methylation dynamics across short-term, exposure-containing CBT in patients with panic disorder.

Interaction of genetic predispositions and environmental factors via epigenetic mechanisms have been hypothesized to play a central role in Panic Disorder (PD) aetiology and therapy. Cognitive Behavioral Therapy (CBT), including exposure interventions, belong to the most efficient treatments of PD although its biological mechanism of action remains unknown. For the first time, we explored the dynamics and magnitude of DNA-methylation and immune cell-type composition during CBT (n = 38) and the therapeutic exposure intervention (n = 21) to unravel their biological correlates and identify possible biomarkers of therapy success. We report transient regulation of the CD4 + T-Cells, Natural Killers cells, Granulocytes during exposure and a significant change in the proportions of CD4 + T cells, CD8 + T cells and B-Cells and Granulocytes during therapy. In an epigenome-wide association study we identified cg01586609 located in a CpG island and annotated to the serotonin receptor 3 A (HTR3A) to be differentially methylated during fear exposure and regulated at gene expression level with significant differences between remitters and non-remitters (p = 0.028). We moreover report cg01699630 annotated to ARG1 to undergo long lasting methylation changes during therapy (paired t test, genome-wide adj.p value = 0.02). This study reports the first data-driven biological candidates for epigenetically mediated effects of acute fear exposure and CBT in PD patients. Our results provide evidence of changes in the serotonin receptor 3 A methylation and expression during fear exposure associated with different long-term CBT trajectories and outcome, making it a possible candidate in the search of markers for therapy success. Finally, our results add to a growing body of evidence showing immune system changes associated with PD.

Genome and transcriptome profiling of spontaneous preterm birth phenotypes.

Preterm birth (PTB) occurs before 37 weeks of gestation. Risk factors include genetics and infection/inflammation. Different mechanisms have been reported for spontaneous preterm birth (SPTB) and preterm birth following preterm premature rupture of membranes (PPROM). This study aimed to identify early pregnancy biomarkers of SPTB and PPROM from the maternal genome and transcriptome. Pregnant women were recruited at the Liverpool Women's Hospital. Pregnancy outcomes were categorised as SPTB, PPROM (≤ 34 weeks gestation, n = 53), high-risk term (HTERM, ≥ 37 weeks, n = 126) or low-risk (no history of SPTB/PPROM) term (LTERM, ≥ 39 weeks, n = 188). Blood samples were collected at 16 and 20 weeks gestation from which, genome (UK Biobank Axiom array) and transcriptome (Clariom D Human assay) data were acquired. PLINK and R were used to perform genetic association and differential expression analyses and expression quantitative trait loci (eQTL) mapping. Several significant molecular signatures were identified across the analyses in preterm cases. Genome-wide significant SNP rs14675645 (ASTN1) was associated with SPTB whereas microRNA-142 transcript and PPARG1-FOXP3 gene set were associated with PPROM at week 20 of gestation and is related to inflammation and immune response. This study has determined genomic and transcriptomic candidate biomarkers of SPTB and PPROM that require validation in diverse populations.

Polygenic risk for schizophrenia and schizotypal traits in non-clinical subjects.

BACKGROUND: Schizotypy is a putative risk phenotype for psychosis liability, but the overlap of its genetic architecture with schizophrenia is poorly understood. METHODS: We tested the hypothesis that dimensions of schizotypy (assessed with the SPQ-B) are associated with a polygenic risk score (PRS) for schizophrenia in a sample of 623 psychiatrically healthy, non-clinical subjects from the FOR2107 multi-centre study and a second sample of 1133 blood donors. RESULTS: We did not find correlations of schizophrenia PRS with either overall SPQ or specific dimension scores, nor with adjusted schizotypy scores derived from the SPQ (addressing inter-scale variance). Also, PRS for affective disorders (bipolar disorder and major depression) were not significantly associated with schizotypy. CONCLUSIONS: This important negative finding demonstrates that despite the hypothesised continuum of schizotypy and schizophrenia, schizotypy might share less genetic risk with schizophrenia than previously assumed (and possibly less compared to psychotic-like experiences).

Cis-epistasis at the LPA locus and risk of cardiovascular diseases.

AIMS: Coronary artery disease (CAD) has a strong genetic predisposition. However, despite substantial discoveries made by genome-wide association studies (GWAS), a large proportion of heritability awaits identification. Non-additive genetic effects might be responsible for part of the unaccounted genetic variance. Here, we attempted a proof-of-concept study to identify non-additive genetic effects, namely epistatic interactions, associated with CAD. METHODS AND RESULTS: We tested for epistatic interactions in 10 CAD case-control studies and UK Biobank with focus on 8068 SNPs at 56 loci with known associations with CAD risk. We identified a SNP pair located in cis at the LPA locus, rs1800769 and rs9458001, to be jointly associated with risk for CAD [odds ratio (OR) = 1.37, P = 1.07 × 10-11], peripheral arterial disease (OR = 1.22, P = 2.32 × 10-4), aortic stenosis (OR = 1.47, P = 6.95 × 10-7), hepatic lipoprotein(a) (Lp(a)) transcript levels (beta = 0.39, P = 1.41 × 10-8), and Lp(a) serum levels (beta = 0.58, P = 8.7 × 10-32), while individual SNPs displayed no association. Further exploration of the LPA locus revealed a strong dependency of these associations on a rare variant, rs140570886, that was previously associated with Lp(a) levels. We confirmed increased CAD risk for heterozygous (relative OR = 1.46, P = 9.97 × 10-32) and individuals homozygous for the minor allele (relative OR = 1.77, P = 0.09) of rs140570886. Using forward model selection, we also show that epistatic interactions between rs140570886, rs9458001, and rs1800769 modulate the effects of the rs140570886 risk allele. CONCLUSIONS: These results demonstrate the feasibility of a large-scale knowledge-based epistasis scan and provide rare evidence of an epistatic interaction in a complex human disease. We were directed to a variant (rs140570886) influencing risk through additive genetic as well as epistatic effects. In summary, this study provides deeper insights into the genetic architecture of a locus important for cardiovascular diseases.

GWAS meta-analysis followed by Mendelian randomization revealed potential control mechanisms for circulating α-Klotho levels.

The protein α-Klotho acts as transmembrane co-receptor for fibroblast growth factor 23 (FGF23) and is a key regulator of phosphate homeostasis. However, α-Klotho also exists in a circulating form, with pleiotropic, but incompletely understood functions and regulation. Therefore, we undertook a genome-wide association study (GWAS) meta-analysis followed by Mendelian randomization (MR) of circulating α-Klotho levels. Plasma α-Klotho levels were measured by enzyme-linked immunosorbent assay (ELISA) in the Ludwigshafen Risk and Cardiovascular Health and Avon Longitudinal Study of Parents and Children (mothers) cohorts, followed by a GWAS meta-analysis in 4376 individuals across the two cohorts. Six signals at five loci were associated with circulating α-Klotho levels at genome-wide significance (P < 5 × 10-8), namely ABO, KL, FGFR1, and two post-translational modification genes, B4GALNT3 and CHST9. Together, these loci explained >9% of the variation in circulating α-Klotho levels. MR analyses revealed no causal relationships between α-Klotho and renal function, FGF23-dependent factors such as vitamin D and phosphate levels, or bone mineral density. The screening for genetic correlations with other phenotypes followed by targeted MR suggested causal effects of liability of Crohn's disease risk [Inverse variance weighted (IVW) beta = 0.059 (95% confidence interval 0.026, 0.093)] and low-density lipoprotein cholesterol levels [-0.198 (-0.332, -0.063)] on α-Klotho. Our GWAS findings suggest that two enzymes involved in post-translational modification, B4GALNT3 and CHST9, contribute to genetic influences on α-Klotho levels, presumably by affecting protein turnover and stability. Subsequent evidence from MR analyses on α-Klotho levels suggest regulation by mechanisms besides phosphate-homeostasis and raise the possibility of cross-talk with FGF19- and FGF21-dependent pathways, respectively. Significance statement: α-Klotho as a transmembrane protein is well investigated along the endocrine FGF23-α-Klotho pathway. However, the role of the circulating form of α-Klotho, which is generated by cleavage of transmembrane α-Klotho, remains incompletely understood. Genetic analyses might help to elucidate novel regulatory and functional mechanisms. The identification of genetic factors related to circulating α-Klotho further enables MR to examine causal relationships with other factors. The findings from the first GWAS meta-analysis of circulating α-Klotho levels identified six genome-wide significant signals across five genes. Given the function of two of the genes identified, B4GALNT3 and CHST9, it is tempting to speculate that post-translational modification significantly contributes to genetic influences on α-Klotho levels, presumably by affecting protein turnover and stability.

Sex differences in the genetic regulation of the blood transcriptome response to glucocorticoid receptor activation.

Substantial sex differences have been reported in the physiological response to stress at multiple levels, including the release of the stress hormone, cortisol. Here, we explore the genomic variants in 93 females and 196 males regulating the initial transcriptional response to cortisol via glucocorticoid receptor (GR) activation. Gene expression levels in peripheral blood were obtained before and after GR-stimulation with the selective GR agonist dexamethasone to identify differential expression following GR-activation. Sex stratified analyses revealed that while the transcripts responsive to GR-stimulation were mostly overlapping between males and females, the quantitative trait loci (eQTLs) regulation differential transcription to GR-stimulation was distinct. Sex-stratified eQTL SNPs (eSNPs) were located in different functional genomic elements and sex-stratified transcripts were enriched within postmortem brain transcriptional profiles associated with Major Depressive Disorder (MDD) specifically in males and females in the cingulate cortex. Female eSNPs were enriched among SNPs linked to MDD in genome-wide association studies. Finally, transcriptional sensitive genetic profile scores derived from sex-stratified eSNPS regulating differential transcription to GR-stimulation were predictive of depression status and depressive symptoms in a sex-concordant manner in a child and adolescent cohort (n = 584). These results suggest the potential of eQTLs regulating differential transcription to GR-stimulation as biomarkers of sex-specific biological risk for stress-related psychiatric disorders.