Genome-wide association study reveals susceptibility loci for self-reported headache in a large community-based Asian population

Cephalalgia ◽  
2021 ◽  
pp. 033310242110372
Author(s):  
Yu-Chien Tsao ◽  
Shuu-Jiun Wang ◽  
Chia-Lin Hsu ◽  
Yen-Feng Wang ◽  
Jong-Ling Fuh ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Enrichment Analysis ◽  
Asian Population ◽  
Genome Wide Association ◽  
Community Based ◽  
Gene Enrichment Analysis ◽  
Gene Enrichment ◽  
Genome Wide

Background The genetic substrate for headache in the general population has not been identified in Asians. We investigated susceptible genetic variants for self-reported headache in a large community-based Asian population. Methods We conducted a genome-wide association study in participants recruited from a community-based cohort to identify the genetic variants associated with headache in Taiwanese. All participants received a structured questionnaire for self-reported headache. A total of 2084 patients with “self-reported headache” and 11,822 age- and sex-matched controls were enrolled. Gene enrichment analysis using the Genotype-Tissue Expression version 6 database was performed to explore the potential function of the identified variants. Results We identified two novel loci, rs10493859 in TGFBR3 and rs13312779 in FGF23, that are functionally relevant to vascular function and migraine to be significantly associated with self-reported headache after adjusting age, sex and top 10 principal components ( p = 8.53 × 10−11 and p = 1.07 × 10−8, respectively). Gene enrichment analysis for genes with GWAS suggestive significance ( p < 10−6) demonstrated that the expression of these genes was significantly enriched in the artery ( p = 8.18 × 10−4) and adipose tissue ( p = 8.95 × 10−4). Conclusion Our results suggest that vascular dysfunction might play important roles in the pathogenesis of self-reported headache in Asian populations.

scholarly journals Genome-wide association study identifies 48 common genetic variants associated with handedness

2019 ◽  
Author(s):  
Gabriel Cuellar Partida ◽  
Joyce Y Tung ◽  
Nicholas Eriksson ◽  
Eva Albrecht ◽  
Fazil Aliev ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Variants ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Left Handedness ◽  
Left Handed ◽  
Genome Wide ◽  
Common Genetic Variants

AbstractHandedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and 32 studies from the International Handedness Consortium, we conducted the world’s largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals). We found 41 genetic loci associated with left-handedness and seven associated with ambidexterity at genome-wide levels of significance (P < 5×10−8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted. We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8% – 6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2% – 17.7%). Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08 – 0.43) implying that these traits are largely influenced by different genetic mechanisms. In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

scholarly journals Genome-wide Association Study Identifies Genetic Variants Associated With Early and Sustained Response to (Pegylated) Interferon in Chronic Hepatitis B Patients: The GIANT-B Study

2019 ◽  
Vol 69 (11) ◽  
pp. 1969-1979
Author(s):  
Willem P Brouwer ◽  
Henry L Y Chan ◽  
Pietro Lampertico ◽  
Jinlin Hou ◽  
Pisit Tangkijvanich ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis B ◽  
Association Study ◽  
Chronic Hepatitis B ◽  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Pegylated Interferon ◽  
Hbv Dna ◽  
Genome Wide Association ◽  
Genome Wide

AbstractBackground(Pegylated) Interferon ([Peg]IFN) therapy leads to response in a minority of chronic hepatitis B (CHB) patients. Host genetic determinants of response are therefore in demand.MethodsIn this genome-wide association study (GWAS), CHB patients, treated with (Peg)IFN for at least 12 weeks ± nucleos(t)ide analogues within randomized trials or as standard of care, were recruited at 21 centers from Europe, Asia, and North America. Response at 24 weeks after (Peg)IFN treatment was defined as combined hepatitis B e antigen (HBeAg) loss with hepatitis B virus (HBV) DNA <2000 IU/mL, or an HBV DNA <2000 IU/mL for HBeAg-negative patients.ResultsOf 1144 patients, 1058 (92%) patients were included in the GWAS analysis. In total, 282 (31%) patients achieved the response and 4% hepatitis B surface antigen (HBsAg) loss. GWAS analysis stratified by HBeAg status, adjusted for age, sex, and the 4 ancestry components identified PRELID2 rs371991 (B= −0.74, standard error [SE] = 0.16, P = 3.44 ×10–6) for HBeAg-positive patients. Importantly, PRELID2 was cross-validated for long-term response in HBeAg-negative patients. G3BP2 rs3821977 (B = 1.13, SE = 0.24, P = 2.46 × 10–6) was associated with response in HBeAg-negative patients. G3BP2 has a role in the interferon pathway and was further examined in peripheral blood mononuclear cells of healthy controls stimulated with IFNα and TLR8. After stimulation, less production of IP-10 and interleukin (IL)-10 proteins and more production of IL-8 were observed with the G3BP2 G-allele.ConclusionsAlthough no genome-wide significant hits were found, the current GWAS identified genetic variants associated with (Peg)IFN response in CHB. The current findings could pave the way for gene polymorphism-guided clinical counseling, both in the setting of (Peg)IFN and the natural history, and possibly for new immune-modulating therapies.Clinical Trials RegistationNCT01401400.

scholarly journals Improved Prediction of Bacterial Genotype-Phenotype Associations Using Interpretable Pangenome-Spanning Regressions

mBio ◽  
2020 ◽  
Vol 11 (4) ◽  
Author(s):  
John A. Lees ◽  
T. Tien Mai ◽  
Marco Galardini ◽  
Nicole E. Wheeler ◽  
Samuel T. Horsfield ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Association Study ◽  
Genetic Variants ◽  
Genome Wide Association Study ◽  
Joint Modeling ◽  
Genome Wide Association ◽  
Data Sets ◽  
Modeling Framework ◽  
Phenotype Prediction ◽  
Genome Wide

ABSTRACT Discovery of genetic variants underlying bacterial phenotypes and the prediction of phenotypes such as antibiotic resistance are fundamental tasks in bacterial genomics. Genome-wide association study (GWAS) methods have been applied to study these relations, but the plastic nature of bacterial genomes and the clonal structure of bacterial populations creates challenges. We introduce an alignment-free method which finds sets of loci associated with bacterial phenotypes, quantifies the total effect of genetics on the phenotype, and allows accurate phenotype prediction, all within a single computationally scalable joint modeling framework. Genetic variants covering the entire pangenome are compactly represented by extended DNA sequence words known as unitigs, and model fitting is achieved using elastic net penalization, an extension of standard multiple regression. Using an extensive set of state-of-the-art bacterial population genomic data sets, we demonstrate that our approach performs accurate phenotype prediction, comparable to popular machine learning methods, while retaining both interpretability and computational efficiency. Compared to those of previous approaches, which test each genotype-phenotype association separately for each variant and apply a significance threshold, the variants selected by our joint modeling approach overlap substantially. IMPORTANCE Being able to identify the genetic variants responsible for specific bacterial phenotypes has been the goal of bacterial genetics since its inception and is fundamental to our current level of understanding of bacteria. This identification has been based primarily on painstaking experimentation, but the availability of large data sets of whole genomes with associated phenotype metadata promises to revolutionize this approach, not least for important clinical phenotypes that are not amenable to laboratory analysis. These models of phenotype-genotype association can in the future be used for rapid prediction of clinically important phenotypes such as antibiotic resistance and virulence by rapid-turnaround or point-of-care tests. However, despite much effort being put into adapting genome-wide association study (GWAS) approaches to cope with bacterium-specific problems, such as strong population structure and horizontal gene exchange, current approaches are not yet optimal. We describe a method that advances methodology for both association and generation of portable prediction models.

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