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A hidden Markov-model for gene mapping based on whole-genome next generation sequencing data
Statistical Applications in Genetics and Molecular Biology
|December 6, 2014
Summary
This study introduces a hidden Markov model for analyzing genetic marker data from bulk segregant next-generation sequencing. This approach aids in mapping multiple genetic loci and identifying trait-related genes across the genome.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Polygenic traits and inheritable diseases necessitate accurate scoring of numerous genome-wide genetic markers.
- High-throughput sequencing and bulk segregant analysis (BSA) offer advanced methods for evaluating single nucleotide polymorphisms (SNPs) and mapping genetic loci.
Purpose of the Study:
- To develop a novel hidden Markov model (HMM) for analyzing marker data from BSA using next-generation sequencing (NGS).
- To enable simultaneous mapping of multiple genetic loci and identification of genomic regions associated with specific traits.
Main Methods:
- A hidden Markov model (HMM) was developed to analyze SNP data from BSA-NGS.
- The model incorporates states representing nucleotide similarity/dissimilarity between parent and offspring.
- Transition and state-dependent probabilities were estimated to infer the most probable state for each SNP.
Main Results:
- The HMM successfully identified probable states for SNPs, indicating potential trait-related genomic regions.
- The model's application to yeast ethanol tolerance data demonstrated its utility in genetic mapping.
- Associated R software, functions, and scripts are available for public use.
Conclusions:
- The proposed HMM provides a robust method for analyzing BSA-NGS data to map quantitative trait loci (QTLs).
- This approach facilitates the identification of genes underlying complex traits and diseases.
- The developed software enhances the accessibility and application of advanced genetic analysis techniques.
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