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

Abstract

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.

Frequency, Origins, and Evolutionary Role of Chromosomal Inversions in Plants

“…With the development of DNA markers in the latter part of the 20th century, it became feasible to develop genetic linkage maps that were sufficiently dense to permit detection of chromosomal rearrangements between plant genomes. More recently, advances in high throughput sequencing and computational methods permit high-resolution genetic mapping and inference of large structural variants from low coverage sequence data (e.g., Flagel et al, 2019). Comparative genetic mapping has been broadly applied, especially in crop rich families such as Solanaceae, Poaceae, and Brassicaceae.…”

Ancestral reconstruction of sunflower karyotypes reveals non-random chromosomal evolution

“…We also believe that syntR provides a baseline for the development of further computational and 485 statistical methods for the study of chromosomal rearrangements. One fruitful direction would be to 486 integrate the syntR algorithm for synteny block detection directly into the genetic map building 487 process (much like GOOGA, Flagel et al 2019). Another key extension would be to allow syntR to 488 compare multiple genetic maps simultaneously to detect synteny blocks in a group of species (e.g., by 489 leveraging information across species).…”

The genetic architecture and evolution of life history divergence among perennials in theMimulus guttatusspecies complex

“…We implemented GOOGA [60] to estimate individual genotyping error rates, construct a linkage map, and estimate genotype posterior probabilities for each individual at each marker. We defined genotypes using a posterior probability >/=0.95.…”