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A refined map of positive selection in the human genome obtained through a sensitiv composite method
|Citation:||Human Genome Meeting (2014)|
|Abstract:||[Objectives] A major challenge in population genetics is the inference ofnatural positive selection, the base for understanding
adaptation. Recent progresses in the development ofnew statistics as well as unprecedented amounts of data hold great promise for
[Methods] We used extensive coalescent simulations ofneutral and selected genomic regions in order to evaluate each ofthe main and most used statistics for their sensitivity in detecting selection, for their sensitivity in localizing selection and their robustness in diverse demographic scenarios. By combining all the statistics in a single composite score through machine leaming (boosting), we improve the sensitivity and facilitate the interpretation of results; we ha ve al so obtained and tested classifiers that are optimized for differentiating between complete and partía! sweeps as well as between time-depth events.
[Results] We have applied our methods to experimental data from the 1 OOOgenomes project and could confirma large number of known selective sweeps.
[Conclusion] Using these different approaches. we have found intriguing novel pattems of selection, showing that different layers of selection can be observed in the human genome.
|Description:||Trabajo presentado en la Human Genome Meeting (Genome Variation and Human Health), celebrada en Ginebra del 27 al 30 de abril de 2014.|
|Appears in Collections:||(IBE) Comunicaciones congresos|