High-resolution genetic mapping with pooled sequencing
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 de...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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BioMed Central Ltd
2013
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| Online Access: | http://hdl.handle.net/1721.1/78673 https://orcid.org/0000-0002-5845-748X https://orcid.org/0000-0003-1709-4034 |
| _version_ | 1811078187680006144 |
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| author | Edwards, Matthew Douglas Gifford, David K. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Edwards, Matthew Douglas Gifford, David K. |
| author_sort | Edwards, Matthew Douglas |
| collection | MIT |
| description | 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/ |
| first_indexed | 2024-09-23T10:55:18Z |
| format | Article |
| id | mit-1721.1/78673 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:55:18Z |
| publishDate | 2013 |
| publisher | BioMed Central Ltd |
| record_format | dspace |
| spelling | mit-1721.1/786732022-09-30T23:54:24Z High-resolution genetic mapping with pooled sequencing Edwards, Matthew Douglas Gifford, David K. 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 Edwards, Matthew Douglas Gifford, David K. 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/ 2013-05-02T17:39:45Z 2013-05-02T17:39:45Z 2012-04 2012-04-19T11:02:24Z Article http://purl.org/eprint/type/JournalArticle 1471-2105 http://hdl.handle.net/1721.1/78673 Edwards, Matthew D., and David K. Gifford. "High-resolution genetic mapping with pooled sequencing." BMC Bioinformatics 13.6 (2012). https://orcid.org/0000-0002-5845-748X https://orcid.org/0000-0003-1709-4034 en http://www.biomedcentral.com/1471-2105/13/S6/S8/ BMC Bioinformatics Creative Commons Attribution http://creativecommons.org/licenses/by/2.0 et al.; licensee BioMed Central Ltd. application/pdf BioMed Central Ltd BioMed Central Ltd |
| spellingShingle | Edwards, Matthew Douglas Gifford, David K. High-resolution genetic mapping with pooled sequencing |
| title | High-resolution genetic mapping with pooled sequencing |
| title_full | High-resolution genetic mapping with pooled sequencing |
| title_fullStr | High-resolution genetic mapping with pooled sequencing |
| title_full_unstemmed | High-resolution genetic mapping with pooled sequencing |
| title_short | High-resolution genetic mapping with pooled sequencing |
| title_sort | high resolution genetic mapping with pooled sequencing |
| url | http://hdl.handle.net/1721.1/78673 https://orcid.org/0000-0002-5845-748X https://orcid.org/0000-0003-1709-4034 |
| work_keys_str_mv | AT edwardsmatthewdouglas highresolutiongeneticmappingwithpooledsequencing AT gifforddavidk highresolutiongeneticmappingwithpooledsequencing |