scholarly journals Bayesian Multi-SNP Genetic Association Analysis: Control of FDR and Use of Summary Statistics

2018 ◽  
Author(s):  
Yeji Lee ◽  
Francesca Luca ◽  
Roger Pique-Regi ◽  
Xiaoquan Wen
Keyword(s):  
Genetic Association ◽  
Association Analysis ◽  
Quantitative Trait ◽  
Snp Analysis ◽  
Summary Statistics ◽  
Genetic Association Analysis ◽  
Computational Approaches ◽  
Individual Level ◽  
Genome Wide ◽  
Level Data

AbstractMulti-SNP genetic association analysis has become increasingly important in analyzing data from genome-wide association studies (GWASs) and molecular quantitative trait loci (QTL) mapping studies. In this paper, we propose novel computational approaches to address two outstanding issues in Bayesian multi-SNP genetic association analysis: namely, the control of false positive discoveries of identified association signals and the maximization of the efficiency of statistical inference by utilizing summary statistics. Quantifying the strength and uncertainty of genetic association signals has been a long-standing theme in statistical genetics. However, there is a lack of formal statistical procedures that can rigorously control type I errors in multi-SNP analysis. We propose an intuitive hierarchical representation of genetic association signals based on Bayesian posterior probabilities, which subsequently enables rigorous control of false discovery rate (FDR) and construction of Bayesian credible sets. From the perspective of statistical data reduction, we examine the computational approaches of multi-SNP analysis using z-statistics from single-SNP association testing and conclude that they likely yield conservative results comparing to using individual-level data. Built on this result, we propose a set of sufficient summary statistics that can lead to identical results as individual-level data without sacrificing power. Our novel computational approaches are implemented in the software package, DAP-G (https://github.com/xqwen/dap), which applies to both GWASs and genome-wide molecular QTL mapping studies. It is highly computationally efficient and approximately 20 times faster than the state-of-the-art implementation of Bayesian multi-SNP analysis software. We demonstrate the proposed computational approaches using carefully constructed simulation studies and illustrate a complete workflow for multi-SNP analysis of cis expression quantitative trait loci using the whole blood data from the GTEx project.

scholarly journals Integrating Molecular QTL Data into Genome-wide Genetic Association Analysis: Probabilistic Assessment of Enrichment and Colocalization

2016 ◽  
Author(s):  
Xiaoquan Wen ◽  
Roger Pique-Regi ◽  
Francesca Luca
Keyword(s):  
Genetic Association ◽  
Association Analysis ◽  
Complex Traits ◽  
Association Studies ◽  
Enrichment Analysis ◽  
Computational Procedure ◽  
Candidate Snps ◽  
Power Calculation ◽  
Genetic Association Analysis ◽  
Genome Wide

AbstractWe propose a novel statistical framework for integrating genetic data from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs for complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and the analysis of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals, which is analogous to the power calculation in genetic association studies. Using this utility, we further illustrate the importance of enrichment analysis on the ability of discovering colocalized signals and the potential limitations of currently available molecular QTL data.

Genetic association analysis between IL9 and coronary artery disease in a Chinese Han population

Cytokine ◽  
2022 ◽  
Vol 150 ◽  
pp. 155761
Author(s):  
Lingfeng Zha ◽  
Jiangtao Dong ◽  
Qianwen Chen ◽  
Yuhua Liao ◽  
Hongsong Zhang ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Genetic Association ◽  
Association Analysis ◽  
Chinese Han Population ◽  
Chinese Han ◽  
Genetic Association Analysis ◽  
Han Population ◽  
Artery Disease

scholarly journals Prevalence, Patterns, and Genetic Association Analysis of Modic Vertebral Endplate Changes

2017 ◽  
Vol 11 (4) ◽  
pp. 594-600 ◽  
Author(s):  
Rishi Mugesh Kanna ◽  
Rajasekaran Shanmuganathan ◽  
Veera Ranjani Rajagopalan ◽  
Senthil Natesan ◽  
Raveendran Muthuraja ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Genetic Association ◽  
Association Analysis ◽  
Disc Degeneration ◽  
Lumbar Disc ◽  
Nucleotide Polymorphisms ◽  
Lumbar Disc Degeneration ◽  
Vertebral Endplate ◽  
Genetic Association Analysis ◽  
Study Population

<sec><title>Study Design</title><p>A prospective genetic association study.</p></sec><sec><title>Purpose</title><p>The etiology of Modic changes (MCs) is unclear. Recently, the role of genetic factors in the etiology of MCs has been evaluated. However, studies with a larger patient subset are lacking, and candidate genes involved in other disc degeneration phenotypes have not been evaluated. We studied the prevalence of MCs and genetic association of 41 candidate genes in a large Indian cohort.</p></sec><sec><title>Overview of Literature</title><p>MCs are vertebral endplate signal changes predominantly observed in the lumbar spine. A significant association between MCs and lumbar disc degeneration and nonspecific low back pain has been described, with the etiopathogenesis implicating various mechanical, infective, and biochemical factors.</p></sec><sec><title>Methods</title><p>We studied 809 patients using 1.5-T magnetic resonance imaging to determine the prevalence, patterns, distribution, and type of lumbar MCs. Genetic association analysis of 71 single nucleotide polymorphisms (SNPs) of 41 candidate genes was performed based on the presence or absence of MCs. SNPs were genotyped using the Sequenome platform, and an association test was performed using PLINK software.</p></sec><sec><title>Results</title><p>The mean age of the study population (n=809) was 36.7±10.8 years. Based on the presence of MCs, the cohort was divided into 702 controls and 107 cases (prevalence, 13%). MCs were more commonly present in the lower (149/251, 59.4%) than in the upper (102/251, 40.6%) endplates. L4–5 endplates were the most commonly affected levels (30.7%). Type 2 MCs were the most commonly observed pattern (n=206, 82%). The rs2228570 SNP of VDR (<italic>p</italic>=0.02) and rs17099008 SNP of MMP20 (<italic>p</italic>=0.03) were significantly associated with MCs.</p></sec><sec><title>Conclusions</title><p>Genetic polymorphisms of SNPs of VDR and MMP20 were significantly associated with MCs. Understanding the etiopathogenetic mechanisms of MCs is important for planning preventive and therapeutic strategies.</p></sec>

scholarly journals Bioinformatic and Genetic Association Analysis of MicroRNA Target Sites in One-Carbon Metabolism Genes

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e21851 ◽  
Author(s):  
Nicole Stone ◽  
Faith Pangilinan ◽  
Anne M. Molloy ◽  
Barry Shane ◽  
John M. Scott ◽  
...  
Keyword(s):  
Genetic Association ◽  
Association Analysis ◽  
Carbon Metabolism ◽  
Microrna Target ◽  
Genetic Association Analysis ◽  
Target Sites ◽  
One Carbon Metabolism
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