Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 diseaseassociated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed the largest 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 (rg=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 randomisation 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.

Intelligence is highly heritable and a major determinant of human health and well-being. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence, but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) 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 associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

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ince the first genome-wide association study on macular degeneration in 2005 (ref. 1), over 3,000 GWASs have been published, for over 1,000 traits, reporting on tens of thousands of genetic risk variants 2. These results have increased our understanding of the genetic architecture of traits. Occasionally, GWAS results have led to further insight into disease mechanisms 3,4 , such as autophagy for Crohn's disease 5 , immunodeficiency for rheumatoid arthritis 6 and transcriptome regulation through FOXA2 in the pancreatic islet and liver for type 2 diabetes 7. After a decade of GWASs, we have learned that the majority of studied traits are highly polygenic and influenced by many genetic variants, each of small effect 4,8 , with disparate genetic architectures across traits 9. Fundamental questions (such as whether all genetic variants or genes in the human genome are associated with at least one, many or even all traits, and whether the polygenic effects for specific traits are functionally clustered or randomly spread across the genome) are, however, still unanswered 4,10,11. Such knowledge would greatly enhance our understanding of how genetic variation leads to trait variation and trait correlations. Whereas GWAS primarily aims to discover genetic variants associated with specific traits, the current availability of vast amounts of GWAS results allow investigation of these general questions. To this end, we compiled a catalog of 4,155 GWAS results across 2,965 unique traits from 295 studies (https://atlas.ctglab.nl), including publicly available GWASs and new results for 600 traits from the UK Biobank 12. These GWAS results were used in the current study to (1) chart the extent of pleiotropy at trait-associated locus, gene, SNP and gene-set levels, (2) characterize the nature of trait-associated variants (that is, the distribution of effect size, minor allele frequency (MAF) and biological functionality of trait-associated or credible SNPs) and (3) investigate genetic architecture across a variety of traits and domains in terms of SNP heritability and trait polygenicity (see Supplementary Fig. 1). Results Catalog of 4,155 GWAS summary statistics. We collected publicly available, full GWAS summary statistics (last update 23 October 2018; see Methods) resulting in 3,555 sets of GWAS summary statistics from 294 studies. We additionally performed GWAS on 600 traits available from the UK Biobank release 2 cohort (UKB2; release May 2017) 12 , by selecting nonbinary traits with >50,000 European individuals with nonmissing phenotypes, and binary traits for which the number of available cases and controls were both >10,000 and total sample size was >50,000 (see Methods, Supplementary Note and Supplementary Tables 1 and 2). In total, we collected 4,155 GWASs from 295 unique studies covering 2,965 unique traits (Supplementary Table 3). Traits were classified into 27 domains 13,14. The average sample size across curated GWASs was 56,250 subjects, with a maximum of 898,130 for type 2 diabetes 15. The 4,155 GWAS results are ma...

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.

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Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

ObjectivesGenome‐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.MethodsWe 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.ResultsThe 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.ConclusionsBy providing theoretical background and hands‐on experience, we aim to make GWAS more accessible to researchers without formal training in the field.