Maladaptation as a byproduct of adaptation: a genomic scale study
| Grant number: | 12/19563-2 |
| Support type: | Scholarships abroad - Research Internship - Doctorate |
| Effective date (Start): | February 01, 2013 |
| Effective date (End): | January 31, 2014 |
| Field of knowledge: | Biological Sciences - Genetics - Human and Medical Genetics |
| Principal Investigator: | Tatiana Teixeira Torres |
| Grantee: | Bárbara Domingues Bitarello |
| Supervisor abroad: | Aida Andrés |
| Home Institution: | Instituto de Biociências (IB). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
| Local de pesquisa : | Max Planck Society, Leipzig, Germany |
| Associated to the scholarship: | 11/12500-2 - Maladaptation as a byproduct of adaptation: a genomic scale study, BP.DR |
Abstract Although accepted as an important evolutionary process, balancing selection has been explored in few genomewide scans. The studies that did search for balancing selection in the human genome used SNP data and underpowered approaches, or sequence data restricted to the protein coding portion of the genome from few populations. Our first goal is to identify coding and non-coding regions of the genome which are candidates for balancing selection, by searching the genome for regions of increased variation and altered site frequency spectra. Having defined these targets, we will discuss the biological features of these genes compatible with balancing selection. To date, no study has scanned non-coding regions for balancing selection using resequencing data, and a genomewide study using resequencing genotypes represents an original goal. Our second goal is to take advantage of the fact that complete resequencing data is available for four human populations (1000 genomes project). This will allow us to compare whether genes under balancing selection are shared among populations, and if this sharing is related to geography. We will analyze the 1000 genomes data for 12 populations sequenced at low coverage, using SFS and HKA outlier approaches as previously implemented by Andres et. al (2009). We will evaluate NGS ascertainment bias by comparing our results to those obtained with a high-coverage dataset. (AU) | |