Genetic Susceptibility Loci in Genomewide Association Study of Cluster Headache

Cefalea; Estudio de asociación del genoma completoCefalea; Estudi de l'associació del genoma completHeadache; Genomewide Association StudyObjective Identifying common genetic variants that confer genetic risk for cluster headache. Methods We conducted a case–control study in the Dutch Leiden University Cluster headache neuro-Analysis program (LUCA) study population (n = 840) and unselected controls from the Netherlands Epidemiology of Obesity Study (NEO; n = 1,457). Replication was performed in a Norwegian sample of 144 cases from the Trondheim Cluster headache sample and 1,800 controls from the Nord-Trøndelag Health Survey (HUNT). Gene set and tissue enrichment analyses, blood cell-derived RNA-sequencing of genes around the risk loci and linkage disequilibrium score regression were part of the downstream analyses. Results An association was found with cluster headache for 4 independent loci (r2 < 0.1) with genomewide significance (p < 5 × 10−8), rs11579212 (odds ratio [OR] = 1.51, 95% confidence interval [CI] = 1.33–1.72 near RP11-815 M8.1), rs6541998 (OR = 1.53, 95% CI = 1.37–1.74 near MERTK), rs10184573 (OR = 1.43, 95% CI = 1.26–1.61 near AC093590.1), and rs2499799 (OR = 0.62, 95% CI = 0.54–0.73 near UFL1/FHL5), collectively explaining 7.2% of the variance of cluster headache. SNPs rs11579212, rs10184573, and rs976357, as proxy SNP for rs2499799 (r2 = 1.0), replicated in the Norwegian sample (p < 0.05). Gene-based mapping yielded ASZ1 as possible fifth locus. RNA-sequencing indicated differential expression of POLR1B and TMEM87B in cluster headache patients. Interpretation This genomewide association study (GWAS) identified and replicated genetic risk loci for cluster headache with effect sizes larger than those typically seen in complex genetic disorders. ANN NEUROL 2021;90:203–21

Dissecting the shared genetic basis of migraine and mental disorders using novel statistical tools

Migraine is three times more prevalent in people with bipolar disorder or depression. The relationship between schizophrenia and migraine is less certain although glutamatergic and serotonergic neurotransmission are implicated in both. A shared genetic basis to migraine and mental disorders has been suggested but previous studies have reported weak or non-significant genetic correlations and five shared risk loci. Using the largest samples to date and novel statistical tools, we aimed to determine the extent to which migraine’s polygenic architecture overlaps with bipolar disorder, depression and schizophrenia beyond genetic correlation, and to identify shared genetic loci. Summary statistics from genome-wide association studies were acquired from large-scale consortia for migraine (n cases = 59 674; n controls = 316 078), bipolar disorder (n cases = 20 352; n controls = 31 358), depression (n cases = 170 756; n controls = 328 443) and schizophrenia (n cases = 40 675, n controls = 64 643). We applied the bivariate causal mixture model to estimate the number of disorder-influencing variants shared between migraine and each mental disorder, and the conditional/conjunctional false discovery rate method to identify shared loci. Loci were functionally characterized to provide biological insights. Univariate MiXeR analysis revealed that migraine was substantially less polygenic (2.8 K disorder-influencing variants) compared to mental disorders (8100–12 300 disorder-influencing variants). Bivariate analysis estimated that 800 (SD = 300), 2100 (SD = 100) and 2300 (SD = 300) variants were shared between bipolar disorder, depression and schizophrenia, respectively. There was also extensive overlap with intelligence (1800, SD = 300) and educational attainment (2100, SD = 300) but not height (1000, SD = 100). We next identified 14 loci jointly associated with migraine and depression and 36 loci jointly associated with migraine and schizophrenia, with evidence of consistent genetic effects in independent samples. No loci were associated with migraine and bipolar disorder. Functional annotation mapped 37 and 298 genes to migraine and each of depression and schizophrenia, respectively, including several novel putative migraine genes such as L3MBTL2, CACNB2 and SLC9B1. Gene-set analysis identified several putative gene sets enriched with mapped genes including transmembrane transport in migraine and schizophrenia. Most migraine-influencing variants were predicted to influence depression and schizophrenia, although a minority of mental disorder-influencing variants were shared with migraine due to the difference in polygenicity. Similar overlap with other brain-related phenotypes suggests this represents a pool of ‘pleiotropic’ variants that influence vulnerability to diverse brain-related disorders and traits. We also identified specific loci shared between migraine and each of depression and schizophrenia, implicating shared molecular mechanisms and highlighting candidate migraine genes for experimental validation

Cluster Headache Genomewide Association Study and Meta-Analysis Identifies Eight Loci and Implicates Smoking as Causal Risk Factor

Objective: The objective of this study was to aggregate data for the first genomewide 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 10 European and 1 East Asian cohorts. We first performed an inverse-variance genomewide 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 5 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 single nucleotide polymorphism (SNP)-based heritability of cluster headache was 14.5%. We identified 9 independent signals in 7 genomewide 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, attention deficit hyperactivity disorder (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 genomewide association study meta-analysis gives clues to the biological basis of cluster headache and indicates that smoking is a causal risk factor. ANN NEUROL 2023

Publisher Correction: Stroke genetics informs drug discovery and risk prediction across ancestries (Nature, (2022), 611, 7934, (115-123), 10.1038/s41586-022-05165-3)

In the version of this article initially published, the name of the PRECISE4Q Consortium was misspelled as “PRECISEQ” and has now been amended in the HTML and PDF versions of the article. Further, data in the first column of Supplementary Table 55 were mistakenly shifted and have been corrected in the file accompanying the HTML version of the article

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 1 . They are heritable 2,3 and etiologically related 4,5 behaviors that have been resistant to gene discovery efforts 6–11 . 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

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Common Variant Burden Contributes to the Familial Aggregation of Migraine in 1,589 Families

© 2018 Elsevier Inc. Complex traits, including migraine, often aggregate in families, but the underlying genetic architecture behind this is not well understood. The aggregation could be explained by rare, penetrant variants that segregate according to Mendelian inheritance or by the sufficient polygenic accumulation of common variants, each with an individually small effect, or a combination of the two hypotheses. In 8,319 individuals across 1,589 migraine families, we calculated migraine polygenic risk scores (PRS) and found a significantly higher common variant burden in familial cases (n = 5,317, OR = 1.76, 95% CI = 1.71–1.81, p = 1.7 × 10−109) compared to population cases from the FINRISK cohort (n = 1,101, OR = 1.32, 95% CI = 1.25–1.38, p = 7.2 × 10−17). The PRS explained 1.6% of the phenotypic variance in the population cases and 3.5% in the familial cases (including 2.9% for migraine without aura, 5.5% for migraine with typical aura, and 8.2% for hemiplegic migraine). The results demonstrate a significant contribution of common polygenic variation to the familial aggregation of migraine. Gormley et al. use polygenic risk scores to show that common variation, captured by genome-wide association studies, in combination contributes to the aggregation of migraine in families. The results may have similar implications for other complex traits in general

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

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