Meta-analysis of polymorphisms in TPH2 gene and suicidal behavior

Genetic signatures of suicide attempt behavior: insights and applications

INTRODUCTION

Every year about 800,000 complete suicide events occur. The identification of biologic markers to identify subjects at risk would be helpful in targeting specific support treatments.

AREA COVERED

A narrative review defines the meta-analytic level of current evidence about the biologic markers of suicide behavior (SB). The meta-analytic evidence gathered so far indicates that the hypothesis-driven research largely failed to identify the biologic markers of suicide. The most consistent and replicated result was reported for: 1) 5-HTR2A T102C, associated with SB in patients with schizophrenia (OR = 1.73 (1.11-2.69)) and 2) BDNF Val66Met (rs6265), with the Met-Val + Val-Val carriers found to be at risk for suicide in the Caucasian population (OR: 1.96 (1.58-2.43)), while Val-Val vs. Val-Met + Met carriers found to be at risk for suicide in the Asian populations (OR: 1.36 (1.04-1.78)). GWAS-based meta-analyses indicate some positive replicated findings regarding the DRD2, Neuroligin gene, estrogen-related genes, and genes involved in gene expression.

EXPERT OPINION

Most consistent results were obtained when analyzing sub-samples of patients. Some promising results come from the implementation of the polygenic risk score. There is no current consensus about an implementable biomarker for SB.

Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches

OBJECTIVE

To identify DNA methylation and clinical features, and to construct machine learning classifiers to assign the patients with major depressive disorder (MDD) into responders and non-responders after a 2-week treatment into responders and non-responders.

METHOD

Han Chinese patients (291 in total) with MDD comprised the study population. Datasets contained demographic information, environment stress factors, and the methylation levels of 38 methylated sites of tryptophan hydroxylase 2 (TPH2) genes in peripheral blood samples. Recursive Feature Elimination (RFE) was employed to select features. Five classification algorithms (logistic regression, classification and regression trees, support vector machine, logitboost and random forests) were used to establish the models. Performance metrics (AUC, F-Measure, G-Mean, accuracy, sensitivity, specificity, positive predictive value and negative predictive value) were computed with 5-fold-cross-validation. Variable importance was evaluated by random forest algorithm.

RESULT

RF with RFE outperformed the other models in our samples based on the demographic information and clinical features (AUC = 61.2%, 95%CI: 60.1-62.4%) / TPH2 CpGs features (AUC = 66.6%, 95%CI: 65.4-67.8%) / both clinical and TPH2 CpGs features (AUC = 72.9%, 95%CI: 71.8-74.0%).

CONCLUSION

The effects of TPH2 on the early-stage antidepressant response were explored by machine learning algorithms. On the basis of the baseline depression severity and TPH2 CpG sites, machine learning approaches can enhance our ability to predict the early-stage antidepressant response. Some potentially important predictors (e.g., TPH2-10-60 (rs2129575), TPH2-2-163 (rs11178998), age of first onset, age) in early-stage treatment response could be utilized in future fundamental research, drug development and clinical practice.

TPH2 polymorphisms across the spectrum of psychiatric morbidity: A systematic review and meta-analysis

Tryptophan hydroxylase 2 (TPH2) is the rate-limiting enzyme in brain serotonin synthesis. The TPH2 gene has frequently been investigated in relation to psychiatric morbidity. The aim of the present review is to integrate results from association studies between TPH2 single nucleotide polymorphisms (SNPs) and various psychiatric disorders, which we furthermore quantified with meta-analysis. We reviewed 166 studies investigating 69 TPH2 SNPs in a broad range of psychiatric disorders, including over 30,000 patients. According to our meta-analysis, TPH2 polymorphisms show strongest associations with mood disorders, suicide (attempt) and schizophrenia. Despite small effect sizes, we conclude that TPH2 SNPs in the coding and non-coding areas (rs4570625, rs11178997, rs11178998, rs10748185, rs1843809, rs4290270, rs17110747) are each associated with one or more psychopathological conditions. Our findings highlight the possible common serotonergic mechanisms of the investigated psychiatric disorders. Yet, the functional relevance of most TPH2 polymorphisms is unclear. Characterizing how exactly the different TPH2 variants influence the serotonergic neurotransmission is a next necessary step in understanding the psychiatric disorders where serotonin is implicated.

Genetic Vulnerability as a Distal Risk Factor for Suicidal Behaviour: Historical Perspective and Current Knowledge

INTRODUCTION

Suicide is a multidimensional problem. Observations of family history of suicide suggest the existence of a genetic vulnerability to suicidal behaviour.

AIM

Starting with a historical perspective, the article reviews current knowledge of a genetic vulnerability to suicidal behaviour, distinct from the genetic vulnerability to psychiatric disorders, focused on clinical and population-based studies, and findings from recent molecular genetics association studies.

METHOD

The review includes peer-reviewed research articles and review papers from the professional literature in English language, retrieved from PubMed/Medline and PsycINFO.

RESULTS

The research literature confirms a existence of a genetic vulnerability to suicidal behaviour. Even though the results of individual studies are difficult to compare, genetic influences could explain up to half of the variance of the occurrence of suicide.

CONCLUSION

Genetic vulnerability could be a distal risk factor for suicide, which helps us to understand the occurrence of suicide among vulnerable people. Ethical implications of such vulnerability are highlighted.