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Detecting inversions with PCA in the presence of population structure

Author

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  • Ronald J Nowling
  • Krystal R Manke
  • Scott J Emrich

Abstract

Chromosomal inversions can lead to reproductive isolation and adaptation in insects such as Drosophila melanogaster and the non-model malaria vector Anopheles gambiae. Inversions can be detected and characterized using principal component analysis (PCA) of single nucleotide polymorphisms (SNPs). To aid in developing such methods, we formed a new benchmark derived from three publicly-available insect data. We then used this benchmark to perform an extended validation of our software for inversion analysis (Asaph). Through that process, we identified and characterized several problematic test cases liable to misinterpretation that can help guide PCA-based inversion detection. Lastly, we re-analyzed the 2R chromosome arm of 150 An. gambiae and coluzzii samples and observed two inversions (2Rc and 2Rd) that were previously known but not annotated in these particular individuals. The resulting benchmark data set and methods will be useful for future inversion detection based solely on SNP data.

Suggested Citation

  • Ronald J Nowling & Krystal R Manke & Scott J Emrich, 2020. "Detecting inversions with PCA in the presence of population structure," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-20, October.
    • Handle: RePEc:plo:pone00:0240429
    DOI: 10.1371/journal.pone.0240429
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    References listed on IDEAS

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    1. Nick Patterson & Alkes L Price & David Reich, 2006. "Population Structure and Eigenanalysis," PLOS Genetics, Public Library of Science, vol. 2(12), pages 1-20, December.
    2. Peristera Paschou & Elad Ziv & Esteban G Burchard & Shweta Choudhry & William Rodriguez-Cintron & Michael W Mahoney & Petros Drineas, 2007. "PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations," PLOS Genetics, Public Library of Science, vol. 3(9), pages 1-15, September.
    3. Jianzhong Ma & Christopher I Amos, 2012. "Investigation of Inversion Polymorphisms in the Human Genome Using Principal Components Analysis," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-12, July.
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