GOOGA: A platform to synthesize mapping experiments and identify genomic structural diversity.
PLoS Comput Biol
; 15(4): e1006949, 2019 04.
Article
en En
| MEDLINE
| ID: mdl-30986215
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
1_ASSA2030
Problema de salud:
1_financiamento_saude
Asunto principal:
Variación Genética
/
Mapeo Cromosómico
/
Biología Computacional
Tipo de estudio:
Health_economic_evaluation
/
Prognostic_studies
Límite:
Animals
Idioma:
En
Revista:
PLoS Comput Biol
Asunto de la revista:
BIOLOGIA
/
INFORMATICA MEDICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Estados Unidos