Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases.

Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.

The selection landscape and genetic legacy of ancient Eurasians.

The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer's disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.

Signatures of Neuropsychological Test Results in the Long Life Family Study: A Cluster Analysis.

BACKGROUND: Discovering patterns of cognitive domains and characterizing how these patterns associate with other risk factors and biomarkers can improve our understanding of the determinants of cognitive aging. OBJECTIVE: To discover patterns of cognitive domains using neuropsychological test results in Long Life Family Study (LLFS) and characterize how these patterns associate with aging markers. METHODS: 5,086 LLFS participants were administered neuropsychological tests at enrollment. We performed a cluster analysis of six baseline neuropsychological test scores and tested the association between the identified clusters and various clinical variables, biomarkers, and polygenic risk scores using generalized estimating equations and the Chi-square test. We used Cox regression to correlate the clusters with the hazard of various medical events. We investigated whether the cluster information could enhance the prediction of cognitive decline using Bayesian beta regression. RESULTS: We identified 12 clusters with different cognitive signatures that represent profiles of performance across multiple neuropsychological tests. These signatures significantly correlated with 26 variables including polygenic risk scores, physical and pulmonary functions, and blood biomarkers and were associated with the hazard of mortality (p < 0.01), cardiovascular disease (p = 0.03), dementia (p = 0.01), and skin cancer (p = 0.03). CONCLUSION: The identified cognitive signatures capture multiple domains simultaneously and provide a holistic vision of cognitive function, showing that different patterns of cognitive function can coexist in aging individuals. Such patterns can be used for clinical intervention and primary care.

Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients. METHODS: This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively. RESULTS: The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity. LIMITATIONS: Environmental factors and differences in locations of functional areas across individuals were not taken into account. CONCLUSIONS: This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.

A qualitative study exploring the consumer experience of receiving self-initiated polygenic risk scores from a third-party website.

The number of people accessing their own polygenic risk scores (PRSs) online is rapidly increasing, yet little is known about why people are doing this, how they react to the information, and what they do with it. We conducted a qualitative interview-based study with people who pursued PRSs through Impute.me, to explore their motivations for seeking PRS information, their emotional reactions, and actions taken in response to their results. Using interpretive description, we developed a theoretical model describing the experience of receiving PRSs in a direct-to-consumer (DTC) context. Dissatisfaction with healthcare was an important motivator for seeking PRS information. Participants described having medical concerns dismissed and experiencing medical distrust, which drove them to self-advocate for their health, which ultimately led them to seek PRSs. Polygenic risk scores were often empowering for participants but could be distressing when PRS information did not align with participants' perceptions of their personal or family histories. Behavioural changes made in response to PRS results included dietary modifications, changes in vitamin supplementation and talk-based therapy. Our data provides the first qualitative insight into how people's lived experience influence their interactions with DTC PRSs.

Polygenic adaptation contributes to the invasive success of the Colorado potato beetle.

How invasive species cope with novel selective pressures with limited genetic variation is a fundamental question in molecular ecology. Several mechanisms have been proposed, but they can lack generality. Here, we addressed an alternative solution, polygenic adaptation, wherein traits that arise from multiple combinations of loci may be less sensitive to loss of variation during invasion. We tested the polygenic signal of environmental adaptation of Colorado potato beetle (CPB) introduced in Eurasia. Population genomic analyses showed declining genetic diversity in the eastward expansion of Eurasian populations, and weak population genetic structure (except for the invasion fronts in Asia). Demographic history showed that all populations shared a strong bottleneck about 100 years ago when CPB was introduced to Europe. Genome scans revealed a suite of genes involved in activity regulation functions that are plausibly related to cold stress, including some well-founded functions (e.g., the activity of phosphodiesterase, the G-protein regulator) and discrete functions. Such polygenic architecture supports the hypothesis that polygenic adaptation and potentially genetic redundancy can fuel the adaptation of CPB despite strong genetic depletion, thus representing a promising general mechanism for resolving the genetic paradox of invasion. More broadly, most complex traits based on polygenes may be less sensitive to invasive bottlenecks, thus ensuring the evolutionary success of invasive species in novel environments.

Genetic variation in satiety signaling and hypothalamic inflammation: merging fields for the study of obesity.

Although obesity has been a longstanding health crisis, the genetic architecture of the disease remains poorly understood. Genome-wide association studies have identified many genomic loci associated with obesity, with genes being enriched in the brain, particularly in the hypothalamus. This points to the role of the central nervous system (CNS) in predisposition to obesity, and we emphasize here several key genes along the satiety signaling pathway involved in genetic susceptibility. Interest has also risen regarding the chronic, low-grade obesity-associated inflammation, with a growing concern toward inflammation in the hypothalamus as a precursor to obesity. Recent studies have found that genetic variation in inflammatory genes play a role in obesity susceptibility, and we highlight here several key genes. Despite the interest in the genetic variants of these pathways individually, there is a lack of research that investigates the relationship between the two. Understanding the interplay between genetic variation in obesity genes enriched in the CNS and inflammation genes will advance our understanding of obesity etiology and heterogeneity, improve genetic risk prediction analyses, and highlight new drug targets for the treatment of obesity. Additionally, this increased knowledge will assist in physician's ability to develop personalized nutrition and medication strategies for combating the obesity epidemic. Though it often seems to present universally, obesity is a highly individual disease, and there remains a need in the field to develop methods to treat at the individual level.

Cis-regulatory architecture of human ESC-derived hypothalamic neuron differentiation aids in variant-to-gene mapping of relevant complex traits.

The hypothalamus regulates metabolic homeostasis by influencing behavior and endocrine systems. Given its role governing key traits, such as body weight and reproductive timing, understanding the genetic regulation of hypothalamic development and function could yield insights into disease pathogenesis. However, given its inaccessibility, studying human hypothalamic gene regulation has proven challenging. To address this gap, we generate a high-resolution chromatin architecture atlas of an established embryonic stem cell derived hypothalamic-like neuron model across three stages of in vitro differentiation. We profile accessible chromatin and identify physical contacts between gene promoters and putative cis-regulatory elements to characterize global regulatory landscape changes during hypothalamic differentiation. Next, we integrate these data with GWAS loci for various complex traits, identifying multiple candidate effector genes. Our results reveal common target genes for these traits, potentially affecting core developmental pathways. Our atlas will enable future efforts to determine hypothalamic mechanisms influencing disease susceptibility.

QTL mapping in outbred tetraploid (and diploid) diallel populations.

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multiallelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and nonadditive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with three tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher-order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

Genetic scores for adult subcortical volumes associate with subcortical volumes during infancy and childhood.

Individual differences in subcortical brain volumes are highly heritable. Previous studies have identified genetic variants that underlie variation in subcortical volumes in adults. We tested whether those previously identified variants also affect subcortical regions during infancy and early childhood. The study was performed within the Generation R study, a prospective birth cohort. We calculated polygenic scores based on reported GWAS for volumes of the accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen, and thalamus. Participants underwent cranial ultrasound around 7 weeks of age (range: 3-20), and we obtained metrics for the gangliothalamic ovoid, a predecessor of the basal ganglia. Furthermore, the children participated in a magnetic resonance imaging (MRI) study around the age of 10 years (range: 9-12). A total of 340 children had complete data at both examinations. Polygenic scores primarily associated with their corresponding volumes at 10 years of age. The scores also moderately related to the diameter of the gangliothalamic ovoid on cranial ultrasound. Mediation analysis showed that the genetic influence on subcortical volumes at 10 years was only mediated for 16.5-17.6% of the total effect through the gangliothalamic ovoid diameter at 7 weeks of age. Combined, these findings suggest that previously identified genetic variants in adults are relevant for subcortical volumes during early life, and that they affect both prenatal and postnatal development of the subcortical regions.

Genetic underpinnings of risky behaviour relate to altered neuroanatomy.

Previous research points to the heritability of risk-taking behaviour. However, evidence on how genetic dispositions are translated into risky behaviour is scarce. Here, we report a genetically informed neuroimaging study of real-world risky behaviour across the domains of drinking, smoking, driving and sexual behaviour in a European sample from the UK Biobank (N = 12,675). We find negative associations between risky behaviour and grey-matter volume in distinct brain regions, including amygdala, ventral striatum, hypothalamus and dorsolateral prefrontal cortex (dlPFC). These effects are replicated in an independent sample recruited from the same population (N = 13,004). Polygenic risk scores for risky behaviour, derived from a genome-wide association study in an independent sample (N = 297,025), are inversely associated with grey-matter volume in dlPFC, putamen and hypothalamus. This relation mediates roughly 2.2% of the association between genes and behaviour. Our results highlight distinct heritable neuroanatomical features as manifestations of the genetic propensity for risk taking.

Extended-amygdala intrinsic functional connectivity networks: A population study.

Pre-clinical and human neuroimaging research implicates the extended-amygdala (ExtA) (including the bed nucleus of the stria terminalis [BST] and central nucleus of the amygdala [CeA]) in networks mediating negative emotional states associated with stress and substance-use behaviours. The extent to which individual ExtA structures form a functionally integrated unit is controversial. We utilised a large sample (n > 1,000 healthy young adult humans) to compare the intrinsic functional connectivity networks (ICNs) of the BST and CeA using task-free functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. We assessed whether inter-individual differences within these ICNs were related to two principal components representing negative disposition and alcohol use. Building on recent primate evidence, we tested whether within BST-CeA intrinsic functional connectivity (iFC) was heritable and further examined co-heritability with our principal components. We demonstrate the BST and CeA to have discrete, but largely overlapping ICNs similar to previous findings. We found no evidence that within BST-CeA iFC was heritable; however, post hoc analyses found significant BST iFC heritability with the broader superficial and centromedial amygdala regions. There were no significant correlations or co-heritability associations with our principal components either across the ICNs or for specific BST-Amygdala iFC. Possible differences in phenotype associations across task-free, task-based, and clinical fMRI are discussed, along with suggestions for more causal investigative paradigms that make use of the now well-established ExtA ICNs.

Multi-level convergence of complex traits and the evolution of bioluminescence.

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or 'multi-level convergent evolution'. To investigate multi-level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long-standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi-level convergent evolution.

The Polygenic and Monogenic Basis of Blood Traits and Diseases.

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

The emerging field of polygenic risk scores and perspective for use in clinical care.

Genetic testing is used widely for diagnostic, carrier and predictive testing in monogenic diseases. Until recently, there were no genetic testing options available for multifactorial complex diseases like heart disease, diabetes and cancer. Genome-wide association studies (GWAS) have been invaluable in identifying single-nucleotide polymorphisms (SNPs) associated with increased or decreased risk for hundreds of complex disorders. For a given disease, SNPs can be combined to generate a cumulative estimation of risk known as a polygenic risk score (PRS). After years of research, PRSs are increasingly used in clinical settings. In this article, we will review the literature on how both genome-wide and restricted PRSs are developed and the relative merit of each. The validation and evaluation of PRSs will also be discussed, including the recognition that PRS validity is intrinsically linked to the methodological and analytical approach of the foundation GWAS together with the ethnic characteristics of that cohort. Specifically, population differences may affect imputation accuracy, risk magnitude and direction. Even as PRSs are being introduced into clinical practice, there is a push to combine them with clinical and demographic risk factors to develop a holistic disease risk. The existing evidence regarding the clinical utility of PRSs is considered across four different domains: informing population screening programs, guiding therapeutic interventions, refining risk for families at high risk, and facilitating diagnosis and predicting prognostic outcomes. The evidence for clinical utility in relation to five well-studied disorders is summarized. The potential ethical, legal and social implications are also highlighted.

Long-read bitter gourd (Momordica charantia) genome and the genomic architecture of nonclassic domestication.

The genetic architecture of quantitative traits is determined by both Mendelian and polygenic factors, yet classic examples of plant domestication focused on selective sweep of newly mutated Mendelian genes. Here we report the chromosome-level genome assembly and the genomic investigation of a nonclassic domestication example, bitter gourd (Momordica charantia), an important Asian vegetable and medicinal plant of the family Cucurbitaceae. Population resequencing revealed the divergence between wild and South Asian cultivars about 6,000 y ago, followed by the separation of the Southeast Asian cultivars about 800 y ago, with the latter exhibiting more extreme trait divergence from wild progenitors and stronger signs of selection on fruit traits. Unlike some crops where the largest phenotypic changes and traces of selection happened between wild and cultivar groups, in bitter gourd large differences exist between two regional cultivar groups, likely reflecting the distinct consumer preferences in different countries. Despite breeding efforts toward increasing female flower proportion, a gynoecy locus exhibits complex patterns of balanced polymorphism among haplogroups, with potential signs of selective sweep within haplogroups likely reflecting artificial selection and introgression from cultivars back to wild accessions. Our study highlights the importance to investigate such nonclassic example of domestication showing signs of balancing selection and polygenic trait architecture in addition to classic selective sweep in Mendelian factors.

The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster.

BACKGROUND: Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain a majority of the heritability of the trait under study. Thus, we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP). RESULTS: We first studied genetic variation for H2O2 resistance in 179 DGRP lines and along with identifying the insulin signaling modulator u-shaped and several regulators of feeding behavior, we estimate that a substantial amount of phenotypic variation can be explained by a polygenic model of genetic variation. We then profiled a portion of the aqueous metabolome in subsets of eight 'high resistance' lines and eight 'low resistance' lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Importantly, the resistance phenotype of these flies was more easily distinguished by their metabolome profiles than by their genotypes. Furthermore, we found a metabolic response to H2O2 in sensitive, but not in resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food. CONCLUSIONS: This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and can efficiently elucidate mechanisms underlying trait variation.

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium.

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.

Identification of relevant hub genes for early intervention at gene coexpression modules with altered predicted expression in schizophrenia.

Genetic risk for schizophrenia is due to the joint effect of multiple genes acting mainly at two different processes, prenatal/perinatal neurodevelopment and adolescence/early adulthood synapse maturation. Identification of important genes at the second process is of relevance for early intervention. The aim of this work was to identify gene co-expression modules with altered expression in schizophrenia during adolescence/early adulthood. To this goal, we predicted frontal cortex gene expression in one discovery sample, the largest GWAS of schizophrenia from the Psychiatric Genomics Consortium, using S-prediXcan, and in one target sample, consisting of 625 schizophrenic patients and 819 controls from Spain, using prediXcan. Prediction models were trained on GTEx frontal cortex expression dataset. In parallel, we identified brain co-expression modules from BrainSpan using WGCNA. Then, we estimated polygenic risk scores based on predicted expression (PE-PRS) for each co-expression module in the target sample, based on PE-PRS model from the discovery sample. This analysis led to the identification of a module with mainly adolescence/adulthood expression whose PE-PRS was significantly associated with schizophrenia. The module was significantly enriched in synaptic processes. Several hub genes at this module are drugabble, according to the drug-gene interaction database, and/or involved in synaptic transmission, such as the voltage-gated ion channels SCN2B and KCNAB2, the calcium calmodulin kinases CAMK2A and CAMK1G, or genes involved in synaptic vesicle cycle, such as DNM1, or SYNGR1. Therefore, identification of this module may be the first step in patient stratification based on biology, as well as in drug design and drug repurposing efforts.