Complex Trait Methylation Risk Scores in the Prediction of Major Depressive Disorder

2022 ◽  
Miruna C. Barbu ◽  
Carmen Amador ◽  
Alex Kwong ◽  
Xueyi Shen ◽  
Mark Adams ◽  
2021 ◽  
pp. 1-8
Emily DiBlasi ◽  
Jooeun Kang ◽  
Anna R. Docherty

Abstract Suicidal ideation, suicide attempt (SA) and suicide are significantly heritable phenotypes. However, the extent to which these phenotypes share genetic architecture is unclear. This question is of great relevance to determining key risk factors for suicide, and to alleviate the societal burden of suicidal thoughts and behaviors (STBs). To help address the question of heterogeneity, consortia efforts have recently shifted from a focus on suicide within the context of major psychopathology (e.g. major depressive disorder, schizophrenia) to suicide as an independent entity. Recent molecular studies of suicide risk by members of the Psychiatric Genomics Consortium and the International Suicide Genetics Consortium have identified genome-wide significant loci associated with SA and with suicide death, and have examined these phenotypes within and outside of the context of major psychopathology. This review summarizes important insights from epidemiological and biometrical research on suicide, and discusses key empirical findings from molecular genetic examinations of STBs. Polygenic risk scores for these phenotypes have been observed to be associated with case−control status and other risk phenotypes. In addition, estimated shared genetic covariance with other phenotypes suggests specific medical and psychiatric risks beyond major depressive disorder. Broadly, molecular studies suggest a complexity of suicide etiology that cannot simply be accounted for by depression. Discussion of the state of suicide genetics, a growing field, also includes important ethical and clinical implications of studying the genetic risk of suicide.

Brain ◽  
2018 ◽  
Vol 141 (12) ◽  
pp. 3457-3471 ◽  
Jiayuan Xu ◽  
Qiaojun Li ◽  
Wen Qin ◽  
Mulin Jun Li ◽  
Chuanjun Zhuo ◽  

Abstract Depression increases the conversion risk from amnestic mild cognitive impairment to Alzheimer’s disease with unknown mechanisms. We hypothesize that the cumulative genomic risk for major depressive disorder may be a candidate cause for the increased conversion risk. Here, we aimed to investigate the predictive effect of the polygenic risk scores of major depressive disorder-specific genetic variants (PRSsMDD) on the conversion from non-depressed amnestic mild cognitive impairment to Alzheimer’s disease, and its underlying neurobiological mechanisms. The PRSsMDD could predict the conversion from amnestic mild cognitive impairment to Alzheimer’s disease, and amnestic mild cognitive impairment patients with high risk scores showed 16.25% higher conversion rate than those with low risk. The PRSsMDD was correlated with the left hippocampal volume, which was found to mediate the predictive effect of the PRSsMDD on the conversion of amnestic mild cognitive impairment. The major depressive disorder-specific genetic variants were mapped into genes using different strategies, and then enrichment analyses and protein–protein interaction network analysis revealed that these genes were involved in developmental process and amyloid-beta binding. They showed temporal-specific expression in the hippocampus in middle and late foetal developmental periods. Cell type-specific expression analysis of these genes demonstrated significant over-representation in the pyramidal neurons and interneurons in the hippocampus. These cross-scale neurobiological analyses and functional annotations indicate that major depressive disorder-specific genetic variants may increase the conversion from amnestic mild cognitive impairment to Alzheimer’s disease by modulating the early hippocampal development and amyloid-beta binding. The PRSsMDD could be used as a complementary measure to select patients with amnestic mild cognitive impairment with high conversion risk to Alzheimer’s disease.

2015 ◽  
Vol 46 (4) ◽  
pp. 759-770 ◽  
N. Mullins ◽  
R. A. Power ◽  
H. L. Fisher ◽  
K. B. Hanscombe ◽  
J. Euesden ◽  

BackgroundMajor depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD.MethodThe RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them.ResultsPRS significantly predicted depression, explaining 1.1% of variance in phenotype (p= 1.9 × 10−6). SLEs and CT were also associated with MDD status (p= 2.19 × 10−4andp= 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p= 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples.ConclusionsCT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.

2016 ◽  
Vol 6 (11) ◽  
pp. e938-e938 ◽  
H C Whalley ◽  
M J Adams ◽  
L S Hall ◽  
T-K Clarke ◽  
A M Fernandez-Pujals ◽  

2017 ◽  
Wendy Marie Ingram ◽  
Anna M. Baker ◽  
Christopher R. Bauer ◽  
Jason P. Brown ◽  
Fernando S. Goes ◽  

ABSTRACTBackgroundMajor Depressive Disorder (MDD) is one of the most common mental illnesses and a leading cause of disability worldwide. Electronic Health Records (EHR) allow researchers to conduct unprecedented large-scale observational studies investigating MDD, its disease development and its interaction with other health outcomes. While there exist methods to classify patients as clear cases or controls, given specific data requirements, there are presently no simple, generalizable, and validated methods to classify an entire patient population into varying groups of depression likelihood and severity.MethodsWe have tested a simple, pragmatic electronic phenotype algorithm that classifies patients into one of five mutually exclusive, ordinal groups, varying in depression phenotype. Using data from an integrated health system on 278,026 patients from a 10-year study period we have tested the convergent validity of these constructs using measures of external validation, including patterns of psychiatric prescriptions, symptom severity, indicators of suicidality, comorbidity, mortality, health care utilization, and polygenic risk scores for MDD.ResultsWe found consistent patterns of increasing morbidity and/or adverse outcomes across the five groups, providing evidence for convergent validity.LimitationsThe study population is from a single rural integrated health system which is predominantly white, possibly limiting its generalizability.ConclusionOur study provides initial evidence that a simple algorithm, generalizable to most EHR data sets, provides categories with meaningful face and convergent validity that can be used for stratification of an entire patient population.

2020 ◽  
pp. 1-8 ◽  
David T. Liebers ◽  
Mehdi Pirooznia ◽  
Andrea Ganna ◽  
Fernando S. Goes ◽  

Abstract Background Although accurate differentiation between bipolar disorder (BD) and unipolar major depressive disorder (MDD) has important prognostic and therapeutic implications, the distinction is often challenging based on clinical grounds alone. In this study, we tested whether psychiatric polygenic risk scores (PRSs) improve clinically based classification models of BD v. MDD diagnosis. Methods Our sample included 843 BD and 930 MDD subjects similarly genotyped and phenotyped using the same standardized interview. We performed multivariate modeling and receiver operating characteristic analysis, testing the incremental effect of PRSs on a baseline model with clinical symptoms and features known to associate with BD compared with MDD status. Results We found a strong association between a BD diagnosis and PRSs drawn from BD (R2 = 3.5%, p = 4.94 × 10−12) and schizophrenia (R2 = 3.2%, p = 5.71 × 10−11) genome-wide association meta-analyses. Individuals with top decile BD PRS had a significantly increased risk for BD v. MDD compared with those in the lowest decile (odds ratio 3.39, confidence interval 2.19–5.25). PRSs discriminated BD v. MDD to a degree comparable with many individual symptoms and clinical features previously shown to associate with BD. When compared with the full composite model with all symptoms and clinical features PRSs provided modestly improved discriminatory ability (ΔC = 0.011, p = 6.48 × 10−4). Conclusions Our study demonstrates that psychiatric PRSs provide modest independent discrimination between BD and MDD cases, suggesting that PRSs could ultimately have utility in subjects at the extremes of the distribution and/or subjects for whom clinical symptoms are poorly measured or yet to manifest.

2018 ◽  
Joey Ward ◽  
Nicholas Graham ◽  
Rona Strawbridge ◽  
Amy Ferguson ◽  
Gregory Jenkins ◽  

AbstractThere are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 783). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10-5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

2019 ◽  
Vol 29 ◽  
pp. S206-S207
Giuseppe Fanelli ◽  
Siegfried Kasper ◽  
Alexander Kautzky ◽  
Joseph Zohar ◽  
Daniel Souery ◽  

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