GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity
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Issue Date
2019-04-15Author
Flagel, Lex E.
Blackman, Benjamin K.
Fishman, Lila
Monnahan, Patrick J.
Sweigart, Andrea
Kelly, John K.
Publisher
Public Library of Science
Type
Article
Article Version
Scholarly/refereed, publisher version
Rights
© 2019 Flagel et al.
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Show full item recordAbstract
Understanding genomic structural variation such as inversions and translocations is a key challenge in evolutionary genetics. We develop a novel statistical approach to comparative genetic mapping to detect large-scale structural mutations from low-level sequencing data. The procedure, called Genome Order Optimization by Genetic Algorithm (GOOGA), couples a Hidden Markov Model with a Genetic Algorithm to analyze data from genetic mapping populations. We demonstrate the method using both simulated data (calibrated from experiments on Drosophila melanogaster) and real data from five distinct crosses within the flowering plant genus Mimulus. Application of GOOGA to the Mimulus data corrects numerous errors (misplaced sequences) in the M. guttatus reference genome and confirms or detects eight large inversions polymorphic within the species complex. Finally, we show how this method can be applied in genomic scans to improve the accuracy and resolution of Quantitative Trait Locus (QTL) mapping.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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Citation
Flagel, L. E., Blackman, B. K., Fishman, L., Monnahan, P. J., Sweigart, A., & Kelly, J. K. (2019). GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity. PLoS computational biology, 15(4), e1006949. https://doi.org/10.1371/journal.pcbi.1006949
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