A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease

Late-onset Alzheimer’s disease is a prevalent age-related polygenic disease that accounts for 50–70% of dementia cases. Currently, only a fraction of the genetic variants underlying Alzheimer’s disease have been identified. Here we show that increased sample sizes allowed identification of seven previously unidentified genetic loci contributing to Alzheimer’s disease. This study highlights microglia, immune cells and protein catabolism as relevant to late-onset Alzheimer’s disease, while identifying and prioritizing previously unidentified genes of potential interest. We anticipate that these results can be included in larger meta-analyses of Alzheimer’s disease to identify further genetic variants that contribute to Alzheimer’s pathology

Genome-wide analysis of 53,400 people with irritable bowel syndrome highlights shared genetic pathways with mood and anxiety disorders

Irritable bowel syndrome (IBS) results from disordered brain–gut interactions. Identifying susceptibility genes could highlight the underlying pathophysiological mechanisms. We designed a digestive health questionnaire for UK Biobank and combined identified cases with IBS with independent cohorts. We conducted a genome-wide association study with 53,400 cases and 433,201 controls and replicated significant associations in a 23andMe panel (205,252 cases and 1,384,055 controls). Our study identified and confirmed six genetic susceptibility loci for IBS. Implicated genes included NCAM1, CADM2, PHF2/FAM120A, DOCK9, CKAP2/TPTE2P3 and BAG6. The first four are associated with mood and anxiety disorders, expressed in the nervous system, or both. Mirroring this, we also found strong genome-wide correlation between the risk of IBS and anxiety, neuroticism and depression (rg > 0.5). Additional analyses suggested this arises due to shared pathogenic pathways rather than, for example, anxiety causing abdominal symptoms. Implicated mechanisms require further exploration to help understand the altered brain–gut interactions underlying IBS

Deciphering osteoarthritis genetics across 826,690 individuals from 9 populations

Osteoarthritis affects over 300 million people worldwide. Here, we conduct a genome-wide association study meta-analysis across 826,690 individuals (177,517 with osteoarthritis) and identify 100 independently associated risk variants across 11 osteoarthritis phenotypes, 52 of which have not been associated with the disease before. We report thumb and spine osteoarthritis risk variants and identify differences in genetic effects between weight-bearing and non-weight-bearing joints. We identify sex-specific and early age-at-onset osteoarthritis risk loci. We integrate functional genomics data from primary patient tissues (including articular cartilage, subchondral bone, and osteophytic cartilage) and identify high-confidence effector genes. We provide evidence for genetic correlation with phenotypes related to pain, the main disease symptom, and identify likely causal genes linked to neuronal processes. Our results provide insights into key molecular players in disease processes and highlight attractive drug targets to accelerate translation

Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles

Genome-wide association analyses identify 123 susceptibility loci for migraine and implicate neurovascular mechanisms in its pathophysiology. Subtype analyses highlight risk loci specific for migraine with or without aura in addition to shared risk variants.Migraine affects over a billion individuals worldwide but its genetic underpinning remains largely unknown. Here, we performed a genome-wide association study of 102,084 migraine cases and 771,257 controls and identified 123 loci, of which 86 are previously unknown. These loci provide an opportunity to evaluate shared and distinct genetic components in the two main migraine subtypes: migraine with aura and migraine without aura. Stratification of the risk loci using 29,679 cases with subtype information indicated three risk variants that seem specific for migraine with aura (in HMOX2, CACNA1A and MPPED2), two that seem specific for migraine without aura (near SPINK2 and near FECH) and nine that increase susceptibility for migraine regardless of subtype. The new risk loci include genes encoding recent migraine-specific drug targets, namely calcitonin gene-related peptide (CALCA/CALCB) and serotonin 1F receptor (HTR1F). Overall, genomic annotations among migraine-associated variants were enriched in both vascular and central nervous system tissue/cell types, supporting unequivocally that neurovascular mechanisms underlie migraine pathophysiology

Genetic effects on the timing of parturition and links to fetal birth weight.

This is the final version. Available from Nature Research via the DOI in this record. Data availability: Cohorts should be contacted individually for access to raw genotype and phenotype data, as each cohort has different data access policies. Summary statistics from the meta-analysis, excluding 23andMe, are available at the EGG website (https://egg-consortium.org/), and access to the weights for constructing the polygenic score of gestational duration excluding 23andMe are available at the PGS Catalog (https://www.pgscatalog.org/, score ID: PGS002806). Access to the full set, including 23andMe results, can be obtained after approval from 23andMe is presented to the corresponding author or by completion of a Data Transfer Agreement (https://research.23andme.com/dataset-access/), which exists to protect the privacy of 23andMe participants. Access to the Danish National Birth Cohort (phs000103.v1.p1), Hyperglycemia and Adverse Pregnancy Outcome (phs000096.v4.p1) and Genomic and Proteomic Network (phs000714.v1.p1) individual-level phenotype and genetic data can be obtained through dbGaP Authorized Access portal (https://dbgap.ncbi.nlm.nih.gov/dbgap/aa/wga.cgi?page=login). The informed consent under which the data or samples were collected is the basis for determining the appropriateness of sharing data through unrestricted-access databases or NIH-designated controlled-access data repositories. The summary statistics used in this publication other than the one generated are available at the following links: fetal GWAS of gestational duration (http://egg-consortium.org/gestational-duration-2019.html), fetal and maternal GWAS of birth weight (http://egg-consortium.org/birth-weight-2019.html), miscarriage (http://www.geenivaramu.ee/tools/misc_sumstats.zip), age at first birth, estradiol (women), endometriosis, number of live births and age at menarche (http://www.nealelab.is), age at menopause (https://www.reprogen.org), testosterone (women)58, SHBG, testosterone and CBAT (https://doi.org/10.6084/m9.figshare.c.5304500.v1), pelvic organ prolapse and leiomyoma of the uterus (https://www.finngen.fi/fi), polycystic ovary syndrome (https://www.repository.cam.ac.uk/handle/1810/283491 and https://www.finngen.fi/fi) and pre-eclampsia (European Genome-phenome Archive, https://ega-archive.org, EGAD00010001984). Pan-UK Biobank data are available at https://pan.ukbb.broadinstitute.org/. Precomputed LD scores for European populations (https://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2) and multi-tissue gene expression precomputed stratified LD scores (https://alkesgroup.broadinstitute.org/LDSCORE/LDSC_SEG_ldscores/Multi_tissue_gene_expr_1000Gv3_ldscores.tgz) are available. eQTL data from GTEx are available at https://gtexportal.org/home/ and from endometrium at http://reproductivegenomics.com.au/shiny/endo_eqtl_rna/. Protein QTL data were obtained from https://www.omicscience.org/apps/pgwas/. Genome Reference Consortium Human Build 37 (hg19) available at https://www.ncbi.nlm.nih.gov/data-hub/genome/GCF_000001405.13/.Code availability: Code for this project has been structured using a Snakemake workflow65 and is available at https://github.com/PerinatalLab/metaGWAS. A public release of it has been deposited in Zenodo (https://doi.org/10.5281/zenodo.7311977).The timing of parturition is crucial for neonatal survival and infant health. Yet, its genetic basis remains largely unresolved. We present a maternal genome-wide meta-analysis of gestational duration (n = 195,555), identifying 22 associated loci (24 independent variants) and an enrichment in genes differentially expressed during labor. A meta-analysis of preterm delivery (18,797 cases, 260,246 controls) revealed six associated loci and large genetic similarities with gestational duration. Analysis of the parental transmitted and nontransmitted alleles (n = 136,833) shows that 15 of the gestational duration genetic variants act through the maternal genome, whereas 7 act both through the maternal and fetal genomes and 2 act only via the fetal genome. Finally, the maternal effects on gestational duration show signs of antagonistic pleiotropy with the fetal effects on birth weight: maternal alleles that increase gestational duration have negative fetal effects on birth weight. The present study provides insights into the genetic effects on the timing of parturition and the complex maternal-fetal relationship between gestational duration and birth weight.Swedish Research CouncilSwedish Research CouncilResearch Council of NorwayResearch Council of NorwayMarch of Dimesunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of HealthNorwegian Diabetes AssociationNils Normans minnegaveNorwegian Research CouncilMedical Research CouncilBritish Heart FoundationResearch Council of NorwayBritish Heart FoundationDaniel B. Burke Chair for Diabetes Research and NIHCHOPEuropean Regional Development Fund and the programme Mobilitas PlussWellcome Trust and Royal Society Sir Henry Dale FellowshipWellcome TrustOak FoundationFonds de la recherche du Québec en santéUS National Institutes of HealthNovo Nordisk FoundationNovo Nordisk FoundationNovo Nordisk Foundatio

Cluster headache genome-wide association study and meta-analysis identifies eight loci and implicates smoking as causal risk factor.

OBJECTIVE: Aggregating data for the first genome-wide association study meta-analysis of cluster headache, to identify genetic risk variants and gain biological insights. METHODS: A total of 4,777 cases (3,348 men and 1,429 women) with clinically diagnosed cluster headache were recruited from ten European and one East Asian cohorts. We first performed an inverse-variance genome-wide association meta-analysis of 4,043 cases and 21,729 controls of European ancestry. In a secondary trans-ancestry meta-analysis we included 734 cases and 9,846 controls of East Asian ancestry. Candidate causal genes were prioritized by five complementary methods: expression quantitative trait loci, transcriptome-wide association, fine-mapping of causal gene sets, genetically driven DNA methylation, and effects on protein structure. Gene set and tissue enrichment analyses, genetic correlation, genetic risk score analysis and Mendelian randomization were part of the downstream analyses. RESULTS: The estimated SNP-based heritability of cluster headache was 14.5%. We identified nine independent signals in seven genome-wide significant loci in the primary meta-analysis, and one additional locus in the trans-ethnic meta-analysis. Five of the loci were previously known. The 20 genes prioritized as potentially causal for cluster headache showed enrichment to artery and brain tissue. Cluster headache was genetically correlated with cigarette smoking, risk-taking behavior, ADHD, depression and musculoskeletal pain. Mendelian randomization analysis indicated a causal effect of cigarette smoking intensity on cluster headache. Three of the identified loci were shared with migraine. INTERPRETATION: This first genome-wide association study meta-analysis gives clues to the biological basis of cluster headache and indicates that smoking is a causal risk factor. This article is protected by copyright. All rights reserved

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders. They are heritable and etiologically related behaviors that have been resistant to gene discovery efforts. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry(1,2). Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis(3), and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach(4), we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry(5). Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.</p