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Background: Over the last 10 years, there have been over 3300 genome-wide association studies (GWAS). Almost every GWAS study provides a Manhattan plot either as a main figure or in the supplement. Several software packages can generate a Manhattan plot, but they are all limited in the extent to which they can annotate gene-names, allele frequencies, and variants having high impact on gene function or provide any other added information or flexibility. Furthermore, in a conventional Manhattan plot, there is no way of distinguishing a locus identified due to a single variant with very significant p-value from a locus with multiple variants which appear to be in a haplotype block having very similar p-values.
Results: Here we present a software tool written in R, which generates a transposed Manhattan plot along with additional features like variant consequence and minor allele frequency to annotate the plot and addresses these limitations. The software also gives flexibility on how and where the user wants to display the annotations. The software can be downloaded from CRAN repository and also from the GitHub project page.
Conclusions: We present a major step up to the existing conventional Manhattan plot generation tools. We hope this form of display along with the added annotations will bring more insight to the reader from this new Manhattan++ plot.
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http://dx.doi.org/10.1186/s12859-019-3201-y | DOI Listing |
bioRxiv
August 2025
Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University. 630 West 168 Street, New York, NY 10032, USA.
Genome-wide association studies (GWAS) have enabled clinicians and researchers to identify genetic variants linked to complex traits and diseases(1). However, conducting GWAS remains technically challenging without bioinformatics expertise due to required data preprocessing, software installation, and analysis scripting (2,3). SAGA is a BASH-based, open-source, fully automated pipeline that integrates three widely adopted tools-PLINK(4), GMMAT(5), and SAIGE(6)-for accessible, robust, and reproducible GWAS.
View Article and Find Full Text PDFSci Rep
August 2025
School of Physical Sciences, Amrita Vishwa Vidyapeetham, Mysuru Campus, Mysuru, Karnataka, 570 026, India.
A simple and robust colorimetric and fluorescent eugenol-based chemical sensor, namely, (E)-N'-(5-allyl-2-hydroxy-3-methoxybenzylidene)-2-hydroxybenzohydrazide (EABH) was synthesized and characterized using spectroscopic techniques such as, NMR (H and C) and mass spectra. The chemosensor shows dual behavior for the colorimetric detection of Fe and fluorometric detection of Pb ions with high sensitivity and selectivity towards both the ions. The EABH detects Fe by "naked eye" color change from lime yellow to brown and displayed fluorescence "Turn-off" response to Pb ion.
View Article and Find Full Text PDFJACC Cardiovasc Imaging
August 2025
Division of Cardiovascular Diseases and Hypertension, Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA. Electronic address:
Discov Oncol
June 2025
Department of Respiratory and Critical Care Medicine, The First Hospital of Changsha (The Affiliated Changsha Hospital of XiangyaSchool of Medicine, Central South University), No. 311, Yingpan Road, Kaifu District, Changsha City, 410005, Hunan Province, China.
Background: Our investigation seeks to elucidate the underlying biological mechanisms by which HIV infection might contribute to lung cancer development, thereby providing valuable insights for developing targeted prevention and treatment strategies for HIV-positive populations.
Methods: We integrated genetic datasets, identified causal SNPs through SMR/HEIDI analysis, and developed predictive models using multiple machine learning approaches with TCGA and GEO cohorts. Survival differences between risk groups were assessed with Kaplan-Meier analysis.
Sci Rep
April 2025
Institute of Economic Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang, China.
This study aims to enhance the coloration of pepper fruit by identifying valuable genetic resources through the analysis of single nucleotide polymorphism (SNP). markers and candidate genes associated with fruit pigmentation. Utilizing 197 natural populations of both hot and sweet peppers, we employed specific-locus amplified fragment sequencing (SLAF-seq) to examine 1496 high-quality SNP markers, thereby identifying significant loci contributing to fruit color variation.
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