GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity

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.Author summary: Genome sequences are an essential resource for genetic research in many species. However, most species exhibit considerable variation in genomic organization, making a single reference sequence inadequate. This variation complicates quantitative trait mapping and population genomics. We introduce a new statistical method and computational tools that use linkage information to improve genome assembly and identify structural differences between individuals or populations. We first use the method to correct many assembly errors in the reference genome of Mimulus guttatus. Analyzing five crosses from the M. guttatus species complex, we detect eight large chromosomal inversions and improve the resolution of a trait mapping study. This work illustrates how genetic mapping can be applied to a greater diversity of species to address genetic and evolutionary questions.