An analysis of genetically regulated gene expression and the role of co-expression networks across 16 psychiatric and substance use phenotypes

The genetic landscape of substance use disorders

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

Association between parental psychiatric disorders and risk of offspring autism spectrum disorder: a Swedish and Finnish population-based cohort study

Background

Roughly more than one in six adults worldwide suffer from psychiatric conditions. Sporadic studies have associated parental psychiatric disorders with autism spectrum disorder in offspring. Comprehensively examining the association between parental psychiatric disorders and offspring autism spectrum disorder is needed to guide health policies, and to inform etiologic studies.

Methods

We included all children born in Sweden and Finland 1997-2016. Diagnoses were clinically ascertained from National Registers through 2017. We calculated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) for autism spectrum disorder in offspring of fathers and mothers with psychiatric disorders, in both parents jointly and across co-occurring conditions.

Findings

Among 2,505,842 children, 33,612 were diagnosed with autism spectrum disorder, of which 20% had a parent with psychiatric disorders. The risk of autism spectrum disorder was increased across all psychiatric disorders in fathers (Sweden: aHR = 2.02, 95% CI = 1.92-2.12; Finland: aHR = 1.63, 95% CI = 1.50-1.77), mothers (Sweden: aHR = 2.34, 95% CI = 2.24-2.43; Finland aHR = 2.12, 95% CI = 1.92-2.28), or both parents (Sweden: aHR = 3.76, 95% CI = 3.48-4.07; Finland aHR = 3.61, 95% CI = 3.20-4.07), compared to neither parents. Co-occurrence of parental psychiatric disorders further increased risk (e.g., Sweden: for one, two or ≥three different diagnostic categories compared to no diagnosis, in fathers aHR = 1.81, 2.07, 2.52; in mothers aHR = 2.05, 2.63, 3.57).

Interpretation

Psychiatric disorders in both parents conveyed the highest risk of offspring autism spectrum disorder, followed by mothers and then fathers. The risk increased with number of co-occurring disorders. All parental psychiatric disorders were associated with increased the risk of autism spectrum disorder. To reliably assess the risk of autism spectrum disorder in children, a comprehensive history incorporating the full range of parental psychiatric disorders is needed beyond solely focusing on familial autism spectrum disorder.

Funding

Swedish-Research-Council-2021-0214.

xWAS analysis in neuropsychiatric disorders by integrating multi-molecular phenotype quantitative trait loci and GWAS summary data

BACKGROUND

Integrating quantitative trait loci (QTL) data related to molecular phenotypes with genome-wide association study (GWAS) data is an important post-GWAS strategic approach employed to identify disease-associated molecular features. Various types of molecular phenotypes have been investigated in neuropsychiatric disorders. However, these findings pertaining to distinct molecular features are often independent of each other, posing challenges for having an overview of the mapped genes.

METHODS

In this study, we comprehensively summarized published analyses focusing on four types of risk-related molecular features (gene expression, splicing transcriptome, protein abundance, and DNA methylation) across five common neuropsychiatric disorders. Subsequently, we conducted supplementary analyses with the latest GWAS dataset and corresponding deficient molecular phenotypes using Functional Summary-based Imputation (FUSION) and summary data-based Mendelian randomization (SMR). Based on the curated and supplemented results, novel reliable genes and their functions were explored.

RESULTS

Our findings revealed that eQTL exhibited superior ability in prioritizing risk genes compared to the other QTL, followed by sQTL. Approximately half of the genes associated with splicing transcriptome, protein abundance, and DNA methylation were successfully replicated by eQTL-associated genes across all five disorders. Furthermore, we identified 436 novel reliable genes, which enriched in pathways related with neurotransmitter transportation such as synaptic, dendrite, vesicles, axon along with correlations with other neuropsychiatric disorders. Finally, we identified ten multiple molecular involved regulation patterns (MMRP), which may provide valuable insights into understanding the contribution of molecular regulation network targeting these disease-associated genes.

CONCLUSIONS

The analyses prioritized novel and reliable gene sets related with five molecular features based on published and supplementary results for five common neuropsychiatric disorders, which were missed in the original GWAS analysis. Besides, the involved MMRP behind these genes could be given priority for further investigation to elucidate the pathogenic molecular mechanisms underlying neuropsychiatric disorders in future studies.

Genetic and phenotypic similarity across major psychiatric disorders: a systematic review and quantitative assessment

There is widespread overlap across major psychiatric disorders, and this is the case at different levels of observations, from genetic variants to brain structures and function and to symptoms. However, it remains unknown to what extent these commonalities at different levels of observation map onto each other. Here, we systematically review and compare the degree of similarity between psychiatric disorders at all available levels of observation. We searched PubMed and EMBASE between January 1, 2009 and September 8, 2022. We included original studies comparing at least four of the following five diagnostic groups: Schizophrenia, Bipolar Disorder, Major Depressive Disorder, Autism Spectrum Disorder, and Attention Deficit Hyperactivity Disorder, with measures of similarities between all disorder pairs. Data extraction and synthesis were performed by two independent researchers, following the PRISMA guidelines. As main outcome measure, we assessed the Pearson correlation measuring the degree of similarity across disorders pairs between studies and biological levels of observation. We identified 2975 studies, of which 28 were eligible for analysis, featuring similarity measures based on single-nucleotide polymorphisms, gene-based analyses, gene expression, structural and functional connectivity neuroimaging measures. The majority of correlations (88.6%) across disorders between studies, within and between levels of observation, were positive. To identify a consensus ranking of similarities between disorders, we performed a principal component analysis. Its first dimension explained 51.4% (95% CI: 43.2, 65.4) of the variance in disorder similarities across studies and levels of observation. Based on levels of genetic correlation, we estimated the probability of another psychiatric diagnosis in first-degree relatives and showed that they were systematically lower than those observed in population studies. Our findings highlight that genetic and brain factors may underlie a large proportion, but not all of the diagnostic overlaps observed in the clinic.

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.