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.
Highlights
Chromosomal inversions play an important role in ecological adaptation by enabling the accumulation of beneficial alleles [1,2,3,4] and, in some cases, lead to reproductive isolation [5]
Principal component analysis (PCA) of single-nucleotide polymorphism (SNP) data is attractive for detecting inversions
For the principal components (PCs)-SNP association tests, we looked for a step-like function in the resulting Manhattan plots indicating an inversion; we did not require that an inversion was associated with a specific PC to count as detected
Summary
Chromosomal inversions play an important role in ecological adaptation by enabling the accumulation of beneficial alleles [1,2,3,4] and, in some cases, lead to reproductive isolation [5]. Inversions enable multiple mutually-exclusive traits to be maintained in the same population; inversion genotype frequencies and expressions of traits can vary seasonally [10] or spatially [6, 7, 11]. Principal component analysis (PCA) of single-nucleotide polymorphism (SNP) data is attractive for detecting inversions (see Table 7 for a comparison of existing software).
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