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GWAS

Summary Table

NAME CITATION YEAR
Cmplot Yin L, Zhang H, Tang Z, Xu J, ...&, Liu X. (2021) RMVP: A memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study Genomics Proteomics Bioinformatics, 19 (4) 619-628. doi:10.1016/j.gpb.2020.10.007. PMID 33662620 2021
gwas diversity monitor Mills MC, Rahal C. (2020) The GWAS Diversity Monitor tracks diversity by disease in real time Nat. Genet., 52 (3) 242-243. doi:10.1038/s41588-020-0580-y. PMID 32139905 2020
gwaslab He, Y., Koido, M., Shimmori, Y., & Kamatani, Y. (2023). GWASLab: a Python package for processing and visualizing GWAS summary statistics. NA
locuszoom Pruim RJ, Welch RP, Sanna S, Teslovich TM, ...&, Willer CJ. (2010) LocusZoom: regional visualization of genome-wide association scan results Bioinformatics, 26 (18) 2336-2337. doi:10.1093/bioinformatics/btq419. PMID 20634204 2010
pheweb Gagliano Taliun SA, VandeHaar P, Boughton AP, Welch RP, ...&, Abecasis GR. (2020) Exploring and visualizing large-scale genetic associations by using PheWeb Nat. Genet., 52 (6) 550-552. doi:10.1038/s41588-020-0622-5. PMID 32504056 2020
popgen Marcus JH, Novembre J. (2017) Visualizing the geography of genetic variants Bioinformatics, 33 (4) 594-595. doi:10.1093/bioinformatics/btw643. PMID 27742697 2017

Cmplot

  • NAME : Cmplot
  • SHORT NAME : Cmplot
  • FULL NAME : Cmplot
  • DESCRIPTION : an easy-to-use open-source web-based tool for visualizing, navigating and sharing GWAS and PheWAS results
  • URL : https://github.com/YinLiLin/Cmplot
  • TITLE : RMVP: A memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study
  • DOI : 10.1016/j.gpb.2020.10.007
  • ABSTRACT : Along with the development of high-throughput sequencing technologies, both sample size and SNP number are increasing rapidly in genome-wide association studies (GWAS), and the associated computation is more challenging than ever. Here, we present a memory-efficient, visualization-enhanced, and parallel-accelerated R package called "rMVP" to address the need for improved GWAS computation. rMVP can 1) effectively process large GWAS data, 2) rapidly evaluate population structure, 3) efficiently estimate variance components by Efficient Mixed-Model Association eXpedited (EMMAX), Factored Spectrally Transformed Linear Mixed Models (FaST-LMM), and Haseman-Elston (HE) regression algorithms, 4) implement parallel-accelerated association tests of markers using general linear model (GLM), mixed linear model (MLM), and fixed and random model circulating probability unification (FarmCPU) methods, 5) compute fast with a globally efficient design in the GWAS processes, and 6) generate various visualizations of GWAS-related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are significantly faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.
  • COPYRIGHT : https://creativecommons.org/licenses/by/4.0/
  • CITATION : Yin L, Zhang H, Tang Z, Xu J, ...&, Liu X. (2021) RMVP: A memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study Genomics Proteomics Bioinformatics, 19 (4) 619-628. doi:10.1016/j.gpb.2020.10.007. PMID 33662620
  • JOURNAL_INFO : Genomics, proteomics & bioinformatics ; Genomics Proteomics Bioinformatics ; 2021 ; 19 ; 4 ; 619-628
  • PUBMED_LINK : 33662620

gwas diversity monitor

  • NAME : gwas diversity monitor
  • SHORT NAME : gwas diversity monitor
  • FULL NAME : gwas diversity monitor
  • URL : http://www.gwasdiversitymonitor.com/
  • TITLE : The GWAS Diversity Monitor tracks diversity by disease in real time
  • DOI : 10.1038/s41588-020-0580-y
  • CITATION : Mills MC, Rahal C. (2020) The GWAS Diversity Monitor tracks diversity by disease in real time Nat. Genet., 52 (3) 242-243. doi:10.1038/s41588-020-0580-y. PMID 32139905
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2020 ; 52 ; 3 ; 242-243
  • PUBMED_LINK : 32139905

gwaslab

  • NAME : gwaslab
  • SHORT NAME : gwaslab
  • FULL NAME : gwaslab
  • URL : https://github.com/Cloufield/gwaslab
  • CITATION : He, Y., Koido, M., Shimmori, Y., & Kamatani, Y. (2023). GWASLab: a Python package for processing and visualizing GWAS summary statistics.

locuszoom

  • NAME : locuszoom
  • SHORT NAME : locuszoom
  • FULL NAME : locuszoom
  • URL : http://locuszoom.org/
  • TITLE : LocusZoom: regional visualization of genome-wide association scan results
  • DOI : 10.1093/bioinformatics/btq419
  • ABSTRACT : UNLABELLED: Genome-wide association studies (GWAS) have revealed hundreds of loci associated with common human genetic diseases and traits. We have developed a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format. LocusZoom visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns and the positions of genes in the region. AVAILABILITY: LocusZoom can be accessed from a web interface at http://csg.sph.umich.edu/locuszoom. Users may generate a single plot using a web form, or many plots using batch mode. The software utilizes LD information from HapMap Phase II (CEU, YRI and JPT+CHB) or 1000 Genomes (CEU) and gene information from the UCSC browser, and will accept SNP identifiers in dbSNP or 1000 Genomes format. Single plots are generated in approximately 20 s. Source code and associated databases are available for download and local installation, and full documentation is available online.
  • COPYRIGHT : http://creativecommons.org/licenses/by-nc/2.0/uk/
  • CITATION : Pruim RJ, Welch RP, Sanna S, Teslovich TM, ...&, Willer CJ. (2010) LocusZoom: regional visualization of genome-wide association scan results Bioinformatics, 26 (18) 2336-2337. doi:10.1093/bioinformatics/btq419. PMID 20634204
  • JOURNAL_INFO : Bioinformatics (Oxford, England) ; Bioinformatics ; 2010 ; 26 ; 18 ; 2336-2337
  • PUBMED_LINK : 20634204

pheweb

  • NAME : pheweb
  • SHORT NAME : pheweb
  • FULL NAME : pheweb
  • URL : https://github.com/statgen/pheweb
  • TITLE : Exploring and visualizing large-scale genetic associations by using PheWeb
  • DOI : 10.1038/s41588-020-0622-5
  • CITATION : Gagliano Taliun SA, VandeHaar P, Boughton AP, Welch RP, ...&, Abecasis GR. (2020) Exploring and visualizing large-scale genetic associations by using PheWeb Nat. Genet., 52 (6) 550-552. doi:10.1038/s41588-020-0622-5. PMID 32504056
  • JOURNAL_INFO : Nature genetics ; Nat. Genet. ; 2020 ; 52 ; 6 ; 550-552
  • PUBMED_LINK : 32504056

popgen

  • NAME : popgen
  • SHORT NAME : popgen
  • FULL NAME : Geography of Genetic Variants Browser
  • URL : https://popgen.uchicago.edu/ggv/
  • TITLE : Visualizing the geography of genetic variants
  • DOI : 10.1093/bioinformatics/btw643
  • ABSTRACT : Summary: One of the key characteristics of any genetic variant is its geographic distribution. The geographic distribution can shed light on where an allele first arose, what populations it has spread to, and in turn on how migration, genetic drift, and natural selection have acted. The geographic distribution of a genetic variant can also be of great utility for medical/clinical geneticists and collectively many genetic variants can reveal population structure. Here we develop an interactive visualization tool for rapidly displaying the geographic distribution of genetic variants. Through a REST API and dynamic front-end, the Geography of Genetic Variants (GGV) browser ( http://popgen.uchicago.edu/ggv/ ) provides maps of allele frequencies in populations distributed across the globe. Availability and Implementation: GGV is implemented as a website ( http://popgen.uchicago.edu/ggv/ ) which employs an API to access frequency data ( http://popgen.uchicago.edu/freq_api/ ). Python and javascript source code for the website and the API are available at: http://github.com/NovembreLab/ggv/ and http://github.com/NovembreLab/ggv-api/ . Contact: jnovembre@uchicago.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
  • CITATION : Marcus JH, Novembre J. (2017) Visualizing the geography of genetic variants Bioinformatics, 33 (4) 594-595. doi:10.1093/bioinformatics/btw643. PMID 27742697
  • JOURNAL_INFO : Bioinformatics (Oxford, England) ; Bioinformatics ; 2017 ; 33 ; 4 ; 594-595
  • PUBMED_LINK : 27742697