ArticleBMC bioinformatics2019
Manhattan Harvester and Cropper: a system for GWAS peak detection.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- A basic framework to explain splice-site choice in eukaryotes.Nature communications · 2025Article
- The Estonian Biobank's journey from biobanking to personalized medicine.Nature communications · 2025Review
- Convergence and divergence of diploid and tetraploid cotton genomes.Nature genetics · 2024Article
- A pan-Zea genome map for enhancing maize improvement.Genome biology · 2022Article
- Suitability of GWAS as a Tool to Discover SNPs Associated with Tick Resistance in Cattle: A Review.Pathogens (Basel, Switzerland) · 2021Review
- GWAS for Meat and Carcass Traits Using Imputed Sequence Level Genotypes in Pooled F2-Designs in Pigs.G3 (Bethesda, Md.) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundSelection of interesting regions from genome wide association studies (GWAS) is typically performed by eyeballing of Manhattan Plots. This is no longer possible with thousands of different phenotypes. There is a need for tools that can automatically detect genomic regions that correspond to what the experienced researcher perceives as peaks worthwhile of further study.
resultsWe developed Manhattan Harvester, a tool designed for "peak extraction" from GWAS summary files and computation of parameters characterizing various aspects of individual peaks. We present the algorithms used and a model for creating a general quality score that evaluates peaks similarly to that of a human researcher. Our tool Cropper utilizes a graphical interface for inspecting, cropping and subsetting Manhattan Plot regions. Cropper is used to validate and visualize the regions detected by Manhattan Harvester.
conclusionsWe conclude that our tools fill the current void in automatically screening large number of GWAS output files in batch mode. The interesting regions are detected and quantified by various parameters by Manhattan Harvester. Cropper offers graphical tools for in-depth inspection of the regions. The tools are open source and freely available.
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Registered trials
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