Detection and classification of positive selection in human populations
Detecting positive selection in genomic regions is a recurrent topic in human population genetics studies. Over the years, many positive selection tests have been implemented to highlight specific genomic patterns left by a selective event when compared to neutral expectations. However, there is lit...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/384314 |
| Acceso en línea: | http://hdl.handle.net/10803/384314 |
| Access Level: | acceso abierto |
| Palabra clave: | Positive selection Machine-learnong Human population genetics Simulations Selective sweep Selecció positiva Aprenentatge supervisat Genètica de poblacions humanes Simulacions 575 |
| Sumario: | Detecting positive selection in genomic regions is a recurrent topic in human population genetics studies. Over the years, many positive selection tests have been implemented to highlight specific genomic patterns left by a selective event when compared to neutral expectations. However, there is little consistency among the regions detected in several genome-wide scans using different tests and/or populations: population-specific demographic dynamics, local genomic features or different types of selection acting along the genome at different times and selective coefficients might explain such discrepancies. The present doctoral thesis is focused in the study of this problem and the development of a innovative solution: a machine-learning classification framework that exploits the combined ability of some selection tests to uncover the different features expected under the hard sweep model, such as sweep completeness and age of onset. The method was calibrated and applied to three reference populations from The 1000 Genome Project to generate a genome-wide classification map of hard selective sweeps. This study improves the way a selective sweep is detected by overcoming the classical selection vs. no-selection classification strategy, and offers an explanation to the lack of consistency observed among selection tests when applied to real data. |
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