Genome-wide association studies (GWAS) of psychological traits are generally conducted on (dichotomized) sums of items or symptoms (e.g., case-control status), and not on the individual items or ...symptoms themselves. We conduct large-scale GWAS on 12 neuroticism items and observe notable and replicable variation in genetic signal between items. Within samples, genetic correlations among the items range between 0.38 and 0.91 (mean r
= .63), indicating genetic heterogeneity in the full item set. Meta-analyzing the two samples, we identify 255 genome-wide significant independent genomic regions, of which 138 are item-specific. Genetic analyses and genetic correlations with 33 external traits support genetic differences between the items. Hierarchical clustering analysis identifies two genetically homogeneous item clusters denoted depressed affect and worry. We conclude that the items used to measure neuroticism are genetically heterogeneous, and that biological understanding can be gained by studying them in genetically more homogeneous clusters.
Objectives
Genome‐wide association studies (GWAS) have become increasingly popular to identify associations between single nucleotide polymorphisms (SNPs) and phenotypic traits. The GWAS method is ...commonly applied within the social sciences. However, statistical analyses will need to be carefully conducted and the use of dedicated genetics software will be required. This tutorial aims to provide a guideline for conducting genetic analyses.
Methods
We discuss and explain key concepts and illustrate how to conduct GWAS using example scripts provided through GitHub (https://github.com/MareesAT/GWA_tutorial/).
In addition to the illustration of standard GWAS, we will also show how to apply polygenic risk score (PRS) analysis. PRS does not aim to identify individual SNPs but aggregates information from SNPs across the genome in order to provide individual‐level scores of genetic risk.
Results
The simulated data and scripts that will be illustrated in the current tutorial provide hands‐on practice with genetic analyses. The scripts are based on PLINK, PRSice, and R, which are commonly used, freely available software tools that are accessible for novice users.
Conclusions
By providing theoretical background and hands‐on experience, we aim to make GWAS more accessible to researchers without formal training in the field.
Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating ...that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (r
= 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.
Insomnia is the second most prevalent mental disorder, with no sufficient treatment available. Despite substantial heritability, insight into the associated genes and neurobiological pathways remains ...limited. Here, we use a large genetic association sample (n = 1,331,010) to detect novel loci and gain insight into the pathways, tissue and cell types involved in insomnia complaints. We identify 202 loci implicating 956 genes through positional, expression quantitative trait loci, and chromatin mapping. The meta-analysis explained 2.6% of the variance. We show gene set enrichments for the axonal part of neurons, cortical and subcortical tissues, and specific cell types, including striatal, hypothalamic, and claustrum neurons. We found considerable genetic correlations with psychiatric traits and sleep duration, and modest correlations with other sleep-related traits. Mendelian randomization identified the causal effects of insomnia on depression, diabetes, and cardiovascular disease, and the protective effects of educational attainment and intracranial volume. Our findings highlight key brain areas and cell types implicated in insomnia, and provide new treatment targets.
Neuroticism is an important risk factor for psychiatric traits, including depression
, anxiety
, and schizophrenia
. At the time of analysis, previous genome-wide association studies
(GWAS) reported ...16 genomic loci associated to neuroticism
. Here we conducted a large GWAS meta-analysis (n = 449,484) of neuroticism and identified 136 independent genome-wide significant loci (124 new at the time of analysis), which implicate 599 genes. Functional follow-up analyses showed enrichment in several brain regions and involvement of specific cell types, including dopaminergic neuroblasts (P = 3.49 × 10
), medium spiny neurons (P = 4.23 × 10
), and serotonergic neurons (P = 1.37 × 10
). Gene set analyses implicated three specific pathways: neurogenesis (P = 4.43 × 10
), behavioral response to cocaine processes (P = 1.84 × 10
), and axon part (P = 5.26 × 10
). We show that neuroticism's genetic signal partly originates in two genetically distinguishable subclusters
('depressed affect' and 'worry'), suggesting distinct causal mechanisms for subtypes of individuals. Mendelian randomization analysis showed unidirectional and bidirectional effects between neuroticism and multiple psychiatric traits. These results enhance neurobiological understanding of neuroticism and provide specific leads for functional follow-up experiments.
Intelligence is associated with important economic and health-related life outcomes. Despite intelligence having substantial heritability (0.54) and a confirmed polygenic nature, initial genetic ...studies were mostly underpowered. Here we report a meta-analysis for intelligence of 78,308 individuals. We identify 336 associated SNPs (METAL P < 5 × 10
) in 18 genomic loci, of which 15 are new. Around half of the SNPs are located inside a gene, implicating 22 genes, of which 11 are new findings. Gene-based analyses identified an additional 30 genes (MAGMA P < 2.73 × 10
), of which all but one had not been implicated previously. We show that the identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P = 3.5 × 10
). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (r
= 0.89, LD score regression P = 5.4 × 10
). These findings provide new insight into the genetic architecture of intelligence.
Attention-deficit/hyperactivity disorder (ADHD) is a severely impairing neurodevelopmental disorder with a prevalence of 5% in children and adolescents and of 2.5% in adults. Comorbid conditions in ...ADHD play a key role in symptom progression, disorder course and outcome. ADHD is associated with a significantly increased risk for substance use, abuse and dependence. ADHD and cannabis use are partly determined by genetic factors; the heritability of ADHD is estimated at 70-80% and of cannabis use initiation at 40-48%. In this study, we used summary statistics from the largest available meta-analyses of genome-wide association studies (GWAS) of ADHD (n = 53,293) and lifetime cannabis use (n = 32,330) to gain insights into the genetic overlap and causal relationship of these two traits. We estimated their genetic correlation to be r
= 0.29 (P = 1.63 × 10
) and identified four new genome-wide significant loci in a cross-trait analysis: two in a single variant association analysis (rs145108385, P = 3.30 × 10
and rs4259397, P = 4.52 × 10
) and two in a gene-based association analysis (WDPCP, P = 9.67 × 10
and ZNF251, P = 1.62 × 10
). Using a two-sample Mendelian randomization approach we found support that ADHD is causal for lifetime cannabis use, with an odds ratio of 7.9 for cannabis use in individuals with ADHD in comparison to individuals without ADHD (95% CI (3.72, 15.51), P = 5.88 × 10
). These results substantiate the temporal relationship between ADHD and future cannabis use and reinforce the need to consider substance misuse in the context of ADHD in clinical interventions.
An enigma in studies of neuropsychiatric disorders is how to translate polygenic risk into disease biology. For schizophrenia, where > 145 significant GWAS loci have been identified and only a few ...genes directly implicated, addressing this issue is a particular challenge. We used a combined cellomics and proteomics approach to show that polygenic risk can be disentangled by searching for shared neuronal morphology and cellular pathway phenotypes of candidate schizophrenia risk genes. We first performed an automated high-content cellular screen to characterize neuronal morphology phenotypes of 41 candidate schizophrenia risk genes. The transcription factors Tcf4 and Tbr1 and the RNA topoisomerase Top3b shared a neuronal phenotype marked by an early and progressive reduction in synapse numbers upon knockdown in mouse primary neuronal cultures. Proteomics analysis subsequently showed that these three genes converge onto the syntaxin-mediated neurotransmitter release pathway, which was previously implicated in schizophrenia, but for which genetic evidence was weak. We show that dysregulation of multiple proteins in this pathway may be due to the combined effects of schizophrenia risk genes Tcf4, Tbr1, and Top3b. Together, our data provide new biological functions for schizophrenia risk genes and support the idea that polygenic risk is the result of multiple small impacts on common neuronal signaling pathways.
Induced pluripotent stem cell (iPSC) technology is more and more used for the study of genetically complex human disease but is challenged by variability, sample size and polygenicity. We discuss ...studies involving iPSC-derived neurons from patients with Schizophrenia (SCZ), to exemplify that heterogeneity in sampling strategy complicate the detection of disease mechanisms. We offer a solution to controlling variability within and between iPSC studies by using specific patient selection strategies.
Abstract Background Schizophrenia patients and their parents have an increased risk of immune disorders compared to population controls and their parents. This may be explained by genetic overlap in ...the pathogenesis of both types of disorders. The purpose of this study was to investigate the genetic overlap between schizophrenia and three immune disorders and to compare with the overlap between schizophrenia and two disorders not primarily characterized by immune dysregulation: bipolar disorder and type 2 diabetes. Methods We performed a polygenic risk score analysis using results from the schizophrenia Psychiatric GWAS consortium (PGC) (8922 cases and 9528 controls) and five Wellcome Trust Case Control Consortium (WTCCC) case samples as target cases: bipolar disorder (n = 1998), type 1 diabetes (n = 2000), Crohn's diseases (n = 2005), rheumatoid arthritis (n = 1999), and type 2 diabetes (n = 1999). The WTCCC British Birth Cohort and National Blood Service samples (n = 3004) were used as target controls. Additionally, we tested whether schizophrenia polygenic risk scores significantly differed between patients with immune disorder, bipolar disorder, and type 2 diabetes respectively. Results Polygenic risk scores for schizophrenia significantly predicted disease status in all three immune disorder samples (Nagelkerke-R2 1.1%–1.3%; p < 0.05). The polygenic risk of schizophrenia in patients with immune disorders was significantly lower than in patients with bipolar disorder (Nagelkerke-R2 6.0%; p < 0.05), but higher than in type 2 diabetes patients (Nagelkerke-R2 0.5%; p < 0.05). Conclusions Our results suggest that genetic factors are shared between schizophrenia and immune disorders. This contributes to an accumulating body of evidence that immune processes may play a role in the etiology of schizophrenia.
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