Shared genetic etiology of hypertension and stroke: evidence from bioinformatics analysis of genome-wide association studies

2017 ◽  
Vol 32 (1) ◽  
pp. 34-39 ◽  
Author(s):  
LD Ji ◽  
SP Hu ◽  
JY Li ◽  
BB Yao ◽  
QJ Shen ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Etiology ◽  
Genome Wide ◽  
Shared Genetic

scholarly journals A large-scale genome-wide association analysis of lung function in the Chinese population identifies novel loci and highlights shared genetic etiology with obesity

2021 ◽  
pp. 2100199
Author(s):  
Zhaozhong Zhu ◽  
Jiachen Li ◽  
Jiahui Si ◽  
Baoshan Ma ◽  
Huwenbo Shi ◽  
...  
Keyword(s):  
Lung Function ◽  
Large Scale ◽  
Causal Effect ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Etiology ◽  
Trait Analysis ◽  
Genome Wide ◽  
Shared Genetic

Lung function is a heritable complex phenotype with obesity being one of its important risk factors. However, the knowledge of their shared genetic basis is limited. Most genome-wide association studies (GWASs) for lung function have been based on European populations, limiting the generalisability across populations. Large-scale lung function GWAS in other populations are lacking.We included 100 285 subjects from China Kadoorie Biobank (CKB). To identify novel loci for lung function, single-trait GWAS were performed on FEV1, FVC, FEV1/FVC in CKB. We then performed genome-wide cross-trait analysis between the lung function and obesity traits (body mass index [BMI], BMI-adjusted waist-to-hip ratio, and BMI-adjusted waist circumference) to investigate the shared genetic effects in CKB. Finally, polygenic risk scores (PRSs) of lung function were developed in CKB and its interaction with BMI's association on lung function were examined. We also conducted cross-trait analysis in parallel with CKB using 457 756 subjects from UK Biobank (UKB) for replication and investigation of ancestry specific effect.We identified 9 genome-wide significant novel loci for FEV1, 6 for FVC and 3 for FEV1/FVC in CKB. FEV1 and FVC showed significant negative genetic correlation with obesity traits in both CKB and UKB. Genetic loci shared between lung function and obesity traits highlighted important pathways, including cell proliferation, embryo and tissue development. Mendelian randomisation analysis suggested significant negative causal effect of BMI on FEV1 and on FVC in both CKB and UKB. Lung function PRSs significantly modified the effect of change-in-BMI on change-in-lung function during an average follow-up of 8 years.This large-scale GWAS of lung function identified novel loci and shared genetic etiology between lung function and obesity. Change-in-BMI might affect change-in-lung function differently according to a subject's polygenic background. These findings may open new avenue for the development of molecular-targeted therapies for obesity and lung function improvement.

scholarly journals Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel L. McCartney ◽  
Josine L. Min ◽  
Rebecca C. Richmond ◽  
Ake T. Lu ◽  
Maria K. Sobczyk ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Biological Aging ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Biomarkers Of Aging ◽  
Genome Wide ◽  
Shared Genetic

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

scholarly journals Statistical challenges for genome-wide association studies of suicidality using family data

2010 ◽  
Vol 25 (5) ◽  
pp. 307-309 ◽  
Author(s):  
J. Lasky-Su ◽  
C. Lange
Keyword(s):  
Association Studies ◽  
Statistical Tests ◽  
Genetic Association Studies ◽  
Genetic Effect ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Etiology ◽  
Genome Wide ◽  
Family Based ◽  
Study Designs

AbstractThe etiology of suicide is complex in nature with both environmental and genetic causes that are extremely diverse. This extensive heterogeneity weakens the relationship between genotype and phenotype and as a result, we face many challenges when studying the genetic etiology of suicide. We are now in the midst of a genetics revolution, where genotyping costs are decreasing and genotyping speed is increasing at a fast rate, allowing genetic association studies to genotype thousands to millions of SNPs that cover the entire human genome. As such, genome-wide association studies (GWAS) are now the norm. In this article we address several statistical challenges that occur when studying the genetic etiology of suicidality in the age of the genetics revolution. These challenges include: (1) the large number of statistical tests; (2) complex phenotypes that are difficult to quantify; and (3) modest genetic effect sizes. We address these statistical issues in the context of family-based study designs. Specifically, we discuss several statistical extensions of family-based association tests (FBATs) that work to alleviate these challenges. As our intention is to describe how statistical methodology may work to identify disease variants for suicidality, we avoid the mathematical details of the methodologies presented.

scholarly journals Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
Melissa A. Munn‐Chernoff ◽  
Emma C. Johnson ◽  
Yi‐Ling Chou ◽  
Jonathan R.I. Coleman ◽  
Laura M. Thornton ◽  
...  
Keyword(s):  
Substance Use ◽  
Eating Disorder ◽  
Genetic Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Shared Genetic

scholarly journals Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences1

2018 ◽  
Author(s):  
Richard Karlsson Linnér ◽  
Pietro Biroli ◽  
Edward Kong ◽  
S Fleur W Meddens ◽  
Robbee Wedow ◽  
...  
Keyword(s):  
Association Studies ◽  
Risky Behaviors ◽  
Risk Tolerance ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Bioinformatics Analyses ◽  
Association Analyses ◽  
Gabaergic Neurotransmission ◽  
Genome Wide ◽  
Shared Genetic

AbstractHumans vary substantially in their willingness to take risks. In a combined sample of over one million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. We identified 611 approximately independent genetic loci associated with at least one of our phenotypes, including 124 with general risk tolerance. We report evidence of substantial shared genetic influences across general risk tolerance and risky behaviors: 72 of the 124 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is moderately to strongly genetically correlated ( to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near general-risk-tolerance-associated SNPs are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We find no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

scholarly journals Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Antoine R. Baldassari ◽  
Colleen M. Sitlani ◽  
Heather M. Highland ◽  
Dan E. Arking ◽  
Steve Buyske ◽  
...  
Keyword(s):  
Qt Interval ◽  
Association Studies ◽  
Genetic Effects ◽  
P Wave ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Shared Genetic

Background: We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci. Methods: We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test. Results: We identified 6 novels ( CD36, PITX2, EMB, ZNF592, YPEL2 , and BC043580 ) and 87 known loci (adaptive sum of powered score test P <5×10 −9 ). Lead single-nucleotide polymorphism rs3211938 at CD36 was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci. Conclusions: Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

Integrating disease and drug-related phenotypes for improved identification of pharmacogenomic variants

2021 ◽  
Vol 22 (5) ◽  
pp. 251-261
Author(s):  
Tom W Ouellette ◽  
Galen EB Wright ◽  
Britt I Drögemöller ◽  
Colin JD Ross ◽  
Bruce C Carleton
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Genome Wide Association ◽  
Research Network ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Inflammatory Bowel ◽  
The Uk ◽  
Shared Genetic ◽  
New Onset

Aim: To improve the identification and interpretation of pharmacogenetic variants through the integration of disease and drug-related traits. Materials & methods: We hypothesized that integrating genome-wide disease and pharmacogenomic data may drive new insights into drug toxicity and response by identifying shared genetic architecture. Pleiotropic variants were identified using a methodological framework incorporating colocalization analysis. Results: Using genome-wide association studies summary statistics from the UK Biobank, European Bioinformatics Institute genome-wide association studies catalog and the Pharmacogenomics Research Network, we validated pleiotropy at the ABCG2 locus between allopurinol response and gout and identified novel pleiotropy between antihypertensive-induced new-onset diabetes, Crohn’s disease and inflammatory bowel disease at the IL18RAP/SLC9A4 locus. Conclusion: New mechanistic insights and genetic loci can be uncovered by identifying pleiotropy between disease and drug-related traits.

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