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SNPpy - Database Management for SNP Data from Genome Wide Association Studies

Author

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  • Faheem Mitha
  • Herodotos Herodotou
  • Nedyalko Borisov
  • Chen Jiang
  • Josh Yoder
  • Kouros Owzar

Abstract

Background: We describe SNPpy, a hybrid script database system using the Python SQLAlchemy library coupled with the PostgreSQL database to manage genotype data from Genome-Wide Association Studies (GWAS). This system makes it possible to merge study data with HapMap data and merge across studies for meta-analyses, including data filtering based on the values of phenotype and Single-Nucleotide Polymorphism (SNP) data. SNPpy and its dependencies are open source software. Results: The current version of SNPpy offers utility functions to import genotype and annotation data from two commercial platforms. We use these to import data from two GWAS studies and the HapMap Project. We then export these individual datasets to standard data format files that can be imported into statistical software for downstream analyses. Conclusions: By leveraging the power of relational databases, SNPpy offers integrated management and manipulation of genotype and phenotype data from GWAS studies. The analysis of these studies requires merging across GWAS datasets as well as patient and marker selection. To this end, SNPpy enables the user to filter the data and output the results as standardized GWAS file formats. It does low level and flexible data validation, including validation of patient data. SNPpy is a practical and extensible solution for investigators who seek to deploy central management of their GWAS data.

Suggested Citation

  • Faheem Mitha & Herodotos Herodotou & Nedyalko Borisov & Chen Jiang & Josh Yoder & Kouros Owzar, 2011. "SNPpy - Database Management for SNP Data from Genome Wide Association Studies," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-8, October.
    • Handle: RePEc:plo:pone00:0024982
    DOI: 10.1371/journal.pone.0024982
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    References listed on IDEAS

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    1. Bryan N Howie & Peter Donnelly & Jonathan Marchini, 2009. "A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 5(6), pages 1-15, June.
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    1. Yikun Zhao & Bin Jiang & Yongxue Huo & Hongmei Yi & Hongli Tian & Haotian Wu & Rui Wang & Jiuran Zhao & Fengge Wang, 2021. "A High-Performance Database Management System for Managing and Analyzing Large-Scale SNP Data in Plant Genotyping and Breeding Applications," Agriculture, MDPI, vol. 11(11), pages 1-21, October.

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