Abstract
Admixture mapping is a gene mapping approach used for the identification of genomic regions harboring disease susceptibility genes in the case of recently admixed populations such as African Americans. We present a novel method for admixture mapping, called admixture aberration analysis (AAA) that uses a DNA pool of affected admixed individuals. We demonstrate through simulations that AAA is a powerful and economical mapping method under a range of scenarios, capturing complex human diseases such as hypertension and end-stage kidney disease. The method has a low false-positive rate and is robust to deviation from model assumptions. Finally, we apply AAA on 600 prostate cancer-affected African Americans, replicating a known risk locus. Simulation results indicate that the method can yield over 96% reduction in genotyping. Our method is implemented as a Java program called AAAmap and is freely available at http://bioinfo.cs.technion.ac.il/AAAmap.
| Original language | English |
|---|---|
| Pages (from-to) | 237-249 |
| Number of pages | 13 |
| Journal | Journal of Computational Biology |
| Volume | 18 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1 Mar 2011 |
Keywords
- Computational molecular biology
- Markov chains
- genetic mapping
- genetic variation
- machine learning
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Molecular Biology
- Genetics
- Computational Mathematics
- Computational Theory and Mathematics