GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity
2019
Understanding genomic
structural variationsuch 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 plantgenus
Mimulus. Application of GOOGA to the
Mimulusdata corrects
numerous errors(misplaced sequences) in the M. guttatus
reference genomeand 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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