scholarly journals qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots

2014 ◽  
Author(s):  
Stephen D. Turner
Keyword(s):  
Association Studies ◽  
Source Code ◽  
R Package ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Human Trait ◽  
General Public License

Summary: Genome-wide association studies (GWAS) have identified thousands of human trait-associated single nucleotide polymorphisms. Here, I describe a freely available R package for visualizing GWAS results using Q-Q and manhattan plots. The qqman package enables the flexible creation of manhattan plots, both genome-wide and for single chromosomes, with optional highlighting of SNPs of interest. Availability: qqman is released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=qqman). The source code is available on GitHub (https://github.com/stephenturner/qqman).

Detecting associated single-nucleotide polymorphisms on the X chromosome in case control genome-wide association studies

2014 ◽  
Vol 26 (2) ◽  
pp. 567-582 ◽  
Author(s):  
Zhongxue Chen ◽  
Hon Keung Tony Ng ◽  
Jing Li ◽  
Qingzhong Liu ◽  
Hanwen Huang
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
X Chromosome ◽  
Association Studies ◽  
Statistical Tests ◽  
Real Data ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide

In the past decade, hundreds of genome-wide association studies have been conducted to detect the significant single-nucleotide polymorphisms that are associated with certain diseases. However, most of the data from the X chromosome were not analyzed and only a few significant associated single-nucleotide polymorphisms from the X chromosome have been identified from genome-wide association studies. This is mainly due to the lack of powerful statistical tests. In this paper, we propose a novel statistical approach that combines the information of single-nucleotide polymorphisms on the X chromosome from both males and females in an efficient way. The proposed approach avoids the need of making strong assumptions about the underlying genetic models. Our proposed statistical test is a robust method that only makes the assumption that the risk allele is the same for both females and males if the single-nucleotide polymorphism is associated with the disease for both genders. Through simulation study and a real data application, we show that the proposed procedure is robust and have excellent performance compared to existing methods. We expect that many more associated single-nucleotide polymorphisms on the X chromosome will be identified if the proposed approach is applied to current available genome-wide association studies data.

EpiGEN: an epistasis simulation pipeline

2020 ◽  
Vol 36 (19) ◽  
pp. 4957-4959
Author(s):  
David B Blumenthal ◽  
Lorenzo Viola ◽  
Markus List ◽  
Jan Baumbach ◽  
Paolo Tieri ◽  
...  
Keyword(s):  
Arbitrary Order ◽  
Association Studies ◽  
Simulated Data ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Supplementary Data ◽  
Single Nucleotide ◽  
Genome Wide

Abstract Summary Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes. Availability and implementation EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen. Supplementary information Supplementary data are available at Bioinformatics online.

Where in the Genome Are Significant Single Nucleotide Polymorphisms from Genome-Wide Association Studies Located?

2011 ◽  
Vol 15 (7-8) ◽  
pp. 507-512 ◽  
Author(s):  
Torsten Günther ◽  
Armin O. Schmitt ◽  
Ralf H. Bortfeldt ◽  
Anke Hinney ◽  
Johannes Hebebrand ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Genome Wide
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