Mutation mapping and identification by whole genome sequencing
- Authors
- Leshchiner, I., Alexa, K., Kelsey, P., Adzhubei, I., Austin, C., Cooney, J., Anderson, H., King, M., Stottman, R., Ha, S., Drummond, I., Paw, B.H., North, T., Beier, D., Goessling, W., and Sunyaev, S.
- ID
- ZDB-PUB-120507-9
- Date
- 2012
- Source
- Genome research 22(8): 1541-1548 (Journal)
- Registered Authors
- Beier, David R., Goessling, Wolfram, North, Trista, Paw, Barry
- Keywords
- none
- MeSH Terms
-
- Chromosome Mapping/methods
- Male
- Recombination, Genetic
- Gene Frequency
- Mice, Inbred C57BL
- Chromosomes/genetics
- Alleles
- Zebrafish/genetics*
- Mice
- Time Factors
- Polymorphism, Single Nucleotide
- Animals
- Genomics/methods
- Homozygote
- Markov Chains
- Mutation
- DNA Mutational Analysis/methods*
- Software*
- Crosses, Genetic
- Female
- PubMed
- 22555591 Full text @ Genome Res.
Genetic mapping of mutations in model systems has facilitated the identification of genes contributing to fundamental biological processes, including human diseases. However, this approach has historically required the prior characterization of informative markers. Here, we report a fast and cost-effective method for genetic mapping using Next Generation Sequencing that combines single nucleotide polymorphism discovery, mutation localization, and potential identification of causal sequence variants. In contrast to prior approaches, we have developed a Hidden Markov Model to narrowly define the mutation area by inferring recombination breakpoints of chromosomes in the mutant pool. In addition, we created an interactive online software resource to facilitate automated analysis of sequencing data and demonstrate its utility in the zebrafish and mouse models. Our novel methodology and online tools will make Next Generation Sequencing an easily applicable resource for mutation mapping in all model systems.