The state of the science in psychiatric genomics

The cost-utility of an intervention for children and adolescents with a parent having a mental illness in the framework of the German health and social care system: a health economic evaluation of a randomized controlled trial

BACKGROUND

Children of families with a parent with a mental illness have an increased risk of developing social and mental health problems resulting in decreased quality of life. Therefore, children and adolescents living in families with a parent with mental illness are regarded as a target group for preventive interventions. To date, only a few economic evaluation studies for interventions directed at preventing the intergenerational transmission of mental health problems exist. In this investigation we estimated the cost utility of an intervention for the support of children and adolescents with a parent having a mental illness from the perspective of the German health and social care system.

METHODS

We randomly assigned a total of 214 families with 337 children and adolescents to the intervention (INT) group (108/170) or the control (TAU) group (106/167). Families in the intervention group received on average eight intervention sessions (50-90 min) over 6 months. We estimated total cost of illness by means of the Children and Adolescent Mental Health Service Receipt Inventory (CAMHSRI) over 24 months. For the estimation of Quality-Adjusted Live Years (QALYs) we applied the KIDSCREEN-10. For estimating the incremental cost-utility of the intervention compared to treatment as usual we used the net-benefit approach.

RESULTS

We estimated the annual cost of illness amounting to € 3784.59 (SD € 8581.11) in the TAU group and € 3264.44 (SD € 9431.89) in the INT group. The annual cost difference between INT and TAU was € - 516.14 (SE 1124.95) which was not significant (p ≤ 0.05). We estimated the average QALY to be 0.759 (SD 0.073) in the TAU group and 0.763 (SD 0.072). The QALY difference between INT and TAU was 0.0037 (SE 0.0092) which was not significant (p ≤ 0.05). The incremental cost utility ratio (ICUR) indicated that the gain of one additional year in full health by means of the intervention was associated with the saving of € 139.49. However, the stochastic insecurity of the ICUR did not allow a unique decision about the cost-utility of the intervention.

CONCLUSIONS

More information on the economic value of the intervention for families with a parent with mental illness in comparison to treatment as usual in Germany is needed.

TRIAL REGISTRATION

ClinicalTrials.gov, identifier NCT02308462; German Clinical Trials Register: DRKS00006806.

Dimensional and transdiagnostic phenotypes in psychiatric genome-wide association studies

Genome-wide association studies (GWAS) provide biological insights into disease onset and progression and have potential to produce clinically useful biomarkers. A growing body of GWAS focuses on quantitative and transdiagnostic phenotypic targets, such as symptom severity or biological markers, to enhance gene discovery and the translational utility of genetic findings. The current review discusses such phenotypic approaches in GWAS across major psychiatric disorders. We identify themes and recommendations that emerge from the literature to date, including issues of sample size, reliability, convergent validity, sources of phenotypic information, phenotypes based on biological and behavioral markers such as neuroimaging and chronotype, and longitudinal phenotypes. We also discuss insights from multi-trait methods such as genomic structural equation modelling. These provide insight into how hierarchical 'splitting' and 'lumping' approaches can be applied to both diagnostic and dimensional phenotypes to model clinical heterogeneity and comorbidity. Overall, dimensional and transdiagnostic phenotypes have enhanced gene discovery in many psychiatric conditions and promises to yield fruitful GWAS targets in the years to come.

Integrative analysis of genomic and exposomic influences on youth mental health

BACKGROUND

Understanding complex influences on mental health problems in young people is needed to inform early prevention strategies. Both genetic and environmental factors are known to influence youth mental health, but a more comprehensive picture of their interplay, including wide-ranging environmental exposures - that is, the exposome - is needed. We perform an integrative analysis of genomic and exposomic data in relation to internalizing and externalizing symptoms in a cohort of 4,314 unrelated youth from the Adolescent Brain and Cognitive Development (ABCD) Study.

METHODS

Using novel GREML-based approaches, we model the variance in internalizing and externalizing symptoms explained by additive and interactive influences from the genome (G) and modeled exposome (E) consisting of up to 133 variables at the family, peer, school, neighborhood, life event, and broader environmental levels, including genome-by-exposome (G × E) and exposome-by-exposome (E × E) effects.

RESULTS

A best-fitting integrative model with G, E, and G × E components explained 35% and 63% of variance in youth internalizing and externalizing symptoms, respectively. Youth in the top quintile of model-predicted risk accounted for the majority of individuals with clinically elevated symptoms at follow-up (60% for internalizing; 72% for externalizing). Of note, different domains of environmental exposures were most impactful for internalizing (life events) and externalizing (contextual including family, school, and peer-level factors) symptoms. In addition, variance explained by G × E contributions was substantially larger for externalizing (33%) than internalizing (13%) symptoms.

CONCLUSIONS

Advanced statistical genetic methods in a longitudinal cohort of youth can be leveraged to address fundamental questions about the role of 'nature and nurture' in developmental psychopathology.

A Local Genetic Correlation Analysis Provides Biological Insights Into the Shared Genetic Architecture of Psychiatric and Substance Use Phenotypes

BACKGROUND

Global genetic correlation analysis has provided valuable insight into the shared genetic basis between psychiatric and substance use disorders. However, little is known about which regions disproportionately contribute to the global correlation.

METHODS

We used Local Analysis of [co]Variant Annotation to calculate bivariate local genetic correlations across 2495 approximately equal-sized, semi-independent genomic regions for 20 psychiatric and substance use phenotypes. We performed a transcriptome-wide association study using expression weights from the prefrontal cortex to identify risk genes for each phenotype, followed by probabilistic fine-mapping to prioritize credible causal genes within each bivariate locus.

RESULTS

We detected 80 significant (p < 2.08 × 10-6) bivariate local genetic correlations across 61 loci. The expression effect directions for risk genes within each bivariate locus were largely consistent with the local correlation coefficients, suggesting that genetically regulated gene expression may be used in the functional interpretation of local genetic correlations. Probabilistic fine-mapping identified several genes that may drive pleiotropic mechanisms for genetically correlated phenotypes. For example, we confirmed a local genetic correlation between schizophrenia and smoking behavior at 15q25 and prioritized PSMA4 as the most credible gene candidate underlying both phenotypes.

CONCLUSIONS

Our study reveals previously unreported local bivariate genetic correlations between psychiatric and substance use phenotypes, which we fine-mapped to identify shared credible causal genes underlying genetically correlated phenotypes.

Ten challenges for clinical translation in psychiatric genetics

Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.