High-resolution genetic mapping with pooled sequencing
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1471-2105-13-S6-S8.pdf
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879.5 KB
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Author(s)
Edwards, Matthew Douglas
Gifford, David K.
Date Issued
April 2012
Journal
BMC Bioinformatics
Publisher
BioMed Central Ltd
Citation
Edwards, Matthew D., and David K. Gifford. "High-resolution genetic mapping with pooled sequencing." BMC Bioinformatics 13.6 (2012).
Version
Final published version
Abstract
Background: 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.
Results: 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.
Conclusions: 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/
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Whitehead Institute for Biomedical Research
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DOI of Published Version
http://www.biomedcentral.com/1471-2105/13/S6/S8/
