scholarly journals Glycosaminoglycan biosynthesis pathway in host genome is associated with Helicobacter pylori infection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dingxue Hu ◽  
Yueqi Lu ◽  
Daoming Wang ◽  
Chao Nie ◽  
Yan Li
Keyword(s):  
Helicobacter Pylori ◽  
Chinese Population ◽  
Association Studies ◽  
Host Susceptibility ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gastric Diseases ◽  
Association Analyses ◽  
Genome Wide ◽  
H Pylori

AbstractHelicobacter pylori is a causative pathogen of many gastric and extra-gastric diseases. It has infected about half of the global population. There were no genome-wide association studies (GWAS) for H. pylori infection conducted in Chinese population, who carried different and relatively homogenous strain of H. pylori. In this work, we performed SNP (single nucleotide polymorphism)-based, gene-based and pathway-based genome-wide association analyses to investigate the genetic basis of host susceptibility to H. pylori infection in 480 Chinese individuals. We also profiled the composition and function of the gut microbiota between H. pylori infection cases and controls. We found several genes and pathways associated with H. pylori infection (P < 0.05), replicated one previously reported SNP rs10004195 in TLR1 gene region (P = 0.02). We also found that glycosaminoglycan biosynthesis related pathway was associated with both onset and progression of H. pylori infection. In the gut microbiome association study, we identified 2 species, 3 genera and several pathways had differential abundance between H. pylori infected cases and controls. This paper is the first GWAS for H. pylori infection in Chinese population, and we combined the genetic and microbial data to comprehensively discuss the basis of host susceptibility to H. pylori infection.

scholarly journals Estimation of heritability for nine common cancers using data from genome-wide association studies in Chinese population

2016 ◽  
Vol 140 (2) ◽  
pp. 329-336 ◽  
Author(s):  
Juncheng Dai ◽  
Wei Shen ◽  
Wanqing Wen ◽  
Jiang Chang ◽  
Tongmin Wang ◽  
...  
Keyword(s):  
Chinese Population ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Using Data

scholarly journals Bayesian multivariate reanalysis of large genetic studies identifies many new associations

2019 ◽  
Author(s):  
Michael C. Turchin ◽  
Matthew Stephens
Keyword(s):  
Genetic Variation ◽  
Multivariate Analyses ◽  
Association Studies ◽  
Plasma Lipid ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Association Analyses ◽  
New Associations ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is de-spite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.1Author SummaryGenome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

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