A Correction to this paper has been published: https://doi.org/10.1038/s41380-021-01049-6

Genetic factors play a major role in Alzheimer's disease (AD) pathology, but biological mechanisms through which these factors contribute to AD remain elusive. Using a cerebrospinal fluid (CSF) proteomic approach, we examined associations between polygenic risk scores for AD (PGRS) and CSF proteomic profiles in 250 individuals with normal cognition, mild cognitive impairment, and AD-type dementia from the Alzheimer's Disease Neuroimaging Initiative. Out of 412 proteins, 201 were associated with PGRS. Hierarchical clustering analysis on proteins associated with PGRS at different single-nucleotide polymorphism p-value inclusion thresholds identified 3 clusters: (1) a protein cluster correlated with highly significant single-nucleotide polymorphisms, associated with amyloid-beta pathology and complement cascades; (2) a protein cluster associated with PGRS additionally including variants contributing to modest risk, involved in neural injury; (3) a protein cluster that also included less strongly associated variants, enriched with cytokine-cytokine interactions and cell adhesion molecules. These findings suggest that CSF protein levels reflect varying degrees of genetic liability for AD and may serve as a tool to investigate biological mechanisms in AD.

After a decade of genome-wide association studies (GWASs), fundamental questions in human genetics, such as the extent of pleiotropy across the genome and variation in genetic architecture across traits, are still unanswered. The current availability of hundreds of GWASs provides a unique opportunity to address these questions. We systematically analyzed 4,155 publicly available GWASs. For a subset of well-powered GWASs on 558 traits, we provide an extensive overview of pleiotropy and genetic architecture. We show that trait-associated loci cover more than half of the genome, and 90% of these overlap with loci from multiple traits. We find that potential causal variants are enriched in coding and flanking regions, as well as in regulatory elements, and show variation in polygenicity and discoverability of traits. Our results provide insights into how genetic variation contributes to trait variation. All GWAS results can be queried and visualized at the GWAS ATLAS resource ( https://atlas.ctglab.nl ).

Late onset Alzheimer’s disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis and showed strong genetic correlation with AD (rg=0.81). Genetic meta-analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipidrelated processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development. Main text Alzheimer’s disease (AD) is the most frequent neurodegenerative disease with roughly 35 million people affected.1 Results from twin studies indicate that AD is highly heritable, with estimates ranging between 60 and 80%.2 Genetically, AD can be roughly divided into 2 subgroups: 1) familial

. Coffee consumption has been suggested to decrease the risk of multiple sclerosis (MS). In this study, we aim to investigate the causal effect of coffee consumption on risk of MS by Mendelian randomization (MR) approaches.. Through a genome-wide association study including 375,833 participants from UK Biobank, we obtained single-nucleotide polymorphisms (SNPs) associated with habitual coffee consumption (P < 5 × 10−8). Summary-level data for MS were obtained from a meta-analysis, incorporating 14,802 subjects with MS and 26,703 healthy controls of European ancestry, which was conducted by the International Multiple Sclerosis Genetics Consortium. MR analyses were performed using inverse-variance-weighted method, weighted median estimator, and MR-Egger regression. Additional analyses were further performed using MR-Egger intercept and Cochran's Q statistic to verify the robustness of our findings.. Nine coffee-associated SNPs were selected as instrumental variables. We failed to detect a causal effect of coffee consumption on MS risk (odds ratio, 1,00; 95% confidence interval, 0.98-1.01; P = 0.48). In the main MR analysis. Consistent results were yielded in sensitivity analyses using the weighted median and MR-Egger methods, and no horizontal pleiotropy (P = 0.49) was identified.. Our MR results indicated that coffee consumption might not be causally associated with risk of MS occurrence. Further well-designed genetic-epidemiological studies investigating the effect of coffee intake on the disease course, such as relapse and progression, are warranted.

Studying biological mechanisms underlying neuropsychiatric disorders is highly challenging as many risk genes are associated with these disorders. This complexity requires research approaches to reliably dissect the cell biology of the risk genes involved. Here, we describe a combined cellomics-proteomics approach that allows (a) medium-throughput functional screening and unbiased selection of important risk genes, and (b) discovery of common functional pathways and interactome connections of selected risk genes. The overlay of pathway and proteome data from selected genes in a biological context can be used to formulate new testable hypothesis of both the genetics and the biology of the disorders.

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.

Although the descending aortic diameter is larger in smokers, data about thoracic aortic growth is missing. Our aim is to present the distribution of thoracic aortic growth in smokers and to compare it with literature of the general population.Current and ex-smokers aged 50–70 years from the longitudinal Danish Lung Cancer Screening Trial, were included. Mean and 95th percentile of annual aortic growth of the ascending aortic (AA) and descending aortic (DA) diameters were calculated with the first and last non-contrast computed tomography scans during follow-up. Determinants of change in aortic diameter over time were investigated with linear mixed models.A total of 1987 participants (56% male, mean age 57.4 ± 4.8 years) were included. During a median follow-up of 48 months, mean AA and DA growth rates were comparable between males (AA 0.12 ± 0.31 mm/year and DA 0.10 ± 0.30 mm/year) and females (AA 0.11 ± 0.29 mm/year and DA 0.13 ± 0.27 mm/year). The 95th percentile ranged from 0.42 to 0.47 mm/year, depending on sex and location. Aortic growth was comparable between current and ex-smokers and aortic growth was not associated with pack-years. Our findings are consistent with aortic growth rates of 0.08 to 0.17 mm/years in the general population. Larger aortic growth was associated with lower age, increased height, absence of medication for hypertension or hypercholesterolemia and lower Agatston scores.This longitudinal study of smokers in the age range of 50–70 years shows that ascending and descending aortic growth is approximately 0.1 mm/year and is consistent with growth in the general population.

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 g = 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.

Intelligence is associated with important economic and health-related life outcomes. Despite substantial heritability (0.54) and confirmed polygenic nature, initial genetic studies were mostly underpowered. We recently conducted a meta-analysis for intelligence of 78,308 individuals, and report 18 genomic loci, of which 15 are novel and 52 genes, of which 40 are novel. We expect to have increased this sample size further by October 2017. The combined data currently available data yielded GWAS information for intelligence for 78,308 unrelated individuals from 13 cohorts. All association studies were performed on individuals of European descent; standard quality-control procedures included correcting for population stratification and filtering on minor allele frequency and imputation quality. As eight out of the 13 cohorts consisted of children (aged < 18; total N=19,509) and five of adults (N=58,799, aged 18–78), we first meta-analyzed the children- and adult-based cohorts separately using METAL software, and subsequently calculated the rg using LD Score regression. We used the results of an earlier GWAS for education attainment for proxy replication. We identify 336 single nucleotide polymorphisms (SNPs) (METAL P<5×10-8) in 18 genomic loci, of which 15 are novel. Roughly half are located inside a gene, implicating 22 genes, of which 11 are novel findings. Gene-based analyses identified an additional 30 genes (MAGMA P<2.73×10-6), of which all but one have not been implicated previously. We show that identified genes are predominantly expressed in brain tissue, and pathway analysis indicates the involvement of genes regulating cell development (MAGMA competitive P=3.5×10-6). Despite the well-known difference in twin-based heritability for intelligence in childhood (0.45) and adulthood (0.80), we show substantial genetic correlation (rg=0.89, LD Score regression P=5.4×10–29). SNP-based heritability was estimated at 0.20 (SE=0.01) in the total sample, and this was comparable in adults (0.21, SE=0.01) and children (0.20; SE=0.03). Bivariate LD score regression analysis shows positive genetic correlations with a.o.smoking cessation, and autism spectrum disorder, and negative genetic correlations with Alzheimer's disease, depressive symptoms, schizophrenia, and neuroticism. Of all 52 genes that were implicated, 35 were reported in the GWAS catalog for a previous association with at least one of 67 distinct traits. Nine genes (ATP2A1, NEGR1, SKAP1, FOXO3, COL16A1, YIPF7, DCC, SH2B1 and TUFM) were previously implicated with body mass index, seven (CYP2D6, NAGA, NDUFA6, TCF20 and SEPT3, FAM109B and MEF2C) with schizophrenia0 and four (NEGR1, SH2B1, DCC and WNT4) with obesity. EXOC4 and MEF2C have been associated previously with Alzheimer's disease. This is the largest GWAS for intelligence so far and for the first time shows multiple robust associations for intelligence, suggesting several functional mechanisms, such as neuronal development and regulation of cell death. These findings provide novel insight into the genetic architecture of intelligence, which may also be important to various psychiatric traits such as schizophrenia and autism spectrum disorder.

Genetic factors play a major role in Alzheimer's disease (AD) pathology, but through which biological mechanisms these factors contribute to AD remains elusive. Cerebrospinal fluid (CSF) proteomic studies have demonstrated disrupted biological processes in AD. Using a CSF proteomic approach, we examined the contribution of the genetic predisposition for AD to these biological processes. We selected 250 subjects (AD/MCI/normal cognition=61/116/73, age 75±7 years, 39%female) from the Alzheimer's disease Neuroimaging Initiative (ADNI). We calculated fourteen polygenic risk scores for AD (PGRS-AD) with increasing significance thresholds (range p=1e-30-p=0.5) using IGAP GWAS summary statistics. Associations between PGRS-AD and 412 CSF protein(s)(fragments) were examined using linear regression, adjusted for age and sex. Analyses were repeated adjusting for APOE-ε4 carrier status. We further performed hierarchical clustering analysis on proteins with at least one nominal significant PGRS-AD association (puncorrected<0.05), to determine how CSF proteins were associated with different PGRS thresholds. Enrichment analysis on PGRS-AD associated CSF proteins was performed using STRINGv10, including KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways and Gene Ontology (GO) databases. Fifty-six (13%) protein(s)(fragments) were associated with at least one PGRS-AD score (n=56 proteins; pFDR<0.05, n=199 proteins; puncorrected<0.05) (Figure 1). Using hierarchical clustering analysis, we identified three patterns of genetic correlations with CSF expression profiles. Cluster 1 proteins (n=69, 34%) were correlated with highly significant variants (pIGAP<1.00e-03) and seemed to be involved in Aβ pathology, and showed enrichment for complement and coagulation cascades. Cluster 2 proteins (n=19, 10%) were more polygenic (range pIGAP=0.01-0.05) and appeared to be involved in neuronal injury - but showed no enrichment for specific biological processes. Cluster 3 proteins (n=112, 56%) had a strong polygenic pattern of inheritance (pIGAP>0.1), and were enriched for cytokine-cytokine interactions and cell adhesion molecules.

Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence 3–7 , but much about its genetic underpinnings remains to be discovered. Here, we present the largest genetic association study of intelligence to date (N=279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure. We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

Several occurrences of the word 'schizophrenia' have been re-worded as 'liability to schizophrenia' or 'schizophrenia risk', including in the title, which should have been "GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability," as well as in Supplementary Figures 1–10 and Supplementary Tables 7–10, to more accurately reflect the findings of the work.

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts ( P =3×10-8), medium spiny neurons ( P =4×10-8) and serotonergic neurons ( P =1×10-7). Gene-set analyses implicate three specific pathways: neurogenesis ( P =4.4×10-9), behavioural response to cocaine processes ( P =1.84×10-7), and axon part (P=5.26×10-8). We show that neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 ( depressed affect and worry , the former being genetically strongly related to depression, rg =0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

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.