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Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental

Timothy P Bilton1,2, Matthew R Schofield3, Michael A Black4

  • 1Department of Mathematics and Statistics, University of Otago, Dunedin 9054, New Zealand tbilton@maths.otago.ac.nz.

Genetics
|March 1, 2018
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Summary

This study introduces GUSMap, a new method for creating accurate genetic linkage maps from low-coverage sequencing data. It effectively handles errors, preventing inflated maps and improving genomic studies in nonmodel species.

Keywords:
genetic linkage mapsgenotyping-by-sequencinghidden Markov modelmap inflationsequencing errors

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) enables high-density genetic maps crucial for nonmodel species genomics.
  • Sequencing and genotyping errors in low-coverage data inflate genetic maps if not addressed.
  • Full-sibling populations present challenges like unknown parental phase and segregation types.

Purpose of the Study:

  • To develop a novel methodology for constructing accurate genetic linkage maps from low-coverage sequencing data.
  • To address challenges posed by sequencing errors and population structures in genetic mapping.
  • To implement this methodology in a user-friendly package called GUSMap.

Main Methods:

  • Developed a new model extending the Lander-Green hidden Markov model to incorporate sequencing error models.
  • Applied the methodology to construct genetic linkage maps using full-sibling populations of diploid species.
  • Implemented the model in the GUSMap software package.

Main Results:

  • GUSMap accurately estimates recombination fractions and genetic map distances, unlike existing methods.
  • The new methodology successfully corrects for inflated genetic maps caused by sequencing errors.
  • Demonstrated the feasibility of using low-coverage sequencing data without extensive genotype filtering.

Conclusions:

  • GUSMap provides a robust approach for genetic map construction using error-prone, low-coverage sequencing data.
  • Accurate genetic maps can be generated by explicitly modeling errors in the analysis.
  • This facilitates genomic assembly and gene investigation in a wider range of species.