This article is part of the supplement: Proceedings of the Second Annual RECOMB Satellite Workshop on Massively Parallel Sequencing (RECOMB-seq 2012)
High-resolution genetic mapping with pooled sequencing
1 Computer Science and Artificial Intelligence Laboratory, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2 Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
3 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
BMC Bioinformatics 2012, 13(Suppl 6):S8 doi:10.1186/1471-2105-13-S6-S8Published: 19 April 2012
Modern genetics has been transformed by high-throughput sequencing. New experimental designs in model organisms involve analyzing many individuals, pooled and sequenced in groups for increased efficiency. However, the uncertainty from pooling and the challenge of noisy sequencing data demand advanced computational methods.
We present MULTIPOOL, a computational method for genetic mapping in model organism crosses that are analyzed by pooled genotyping. Unlike other methods for the analysis of pooled sequence data, we simultaneously consider information from all linked chromosomal markers when estimating the location of a causal variant. Our use of informative sequencing reads is formulated as a discrete dynamic Bayesian network, which we extend with a continuous approximation that allows for rapid inference without a dependence on the pool size. MULTIPOOL generalizes to include biological replicates and case-only or case-control designs for binary and quantitative traits.
Our increased information sharing and principled inclusion of relevant error sources improve resolution and accuracy when compared to existing methods, localizing associations to single genes in several cases. MULTIPOOL is freely available at http://cgs.csail.mit.edu/multipool/ webcite.