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Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps
Cristiane Hayumi Taniguti1,2, Lucas Mitsuo Taniguti1,3, Rodrigo Rampazo Amadeu1
1Department of Genetics, University of São Paulo, São Paulo 13418-900, Brazil.
Gigascience
|October 27, 2023
Summary
Genotyping-by-sequencing (GBS) errors complicate linkage map construction. The Reads2Map workflow identifies and filters these errors, improving genetic map accuracy for plant and animal breeding.
Area of Science:
- Genomics and Bioinformatics
- Population Genetics
- Computational Biology
Background:
- Genotyping-by-sequencing (GBS) offers cost-effective genotyping for large populations.
- GBS data present bioinformatic challenges, including polymerase chain reaction duplicate bias and sequencing errors.
- Genotyping errors can lead to inaccurate linkage maps, hindering genetic analysis and breeding applications.
Purpose of the Study:
- To develop and evaluate a bioinformatics workflow (Reads2Map) for building accurate linkage maps from GBS data.
- To identify and mitigate common errors in GBS sequencing data.
- To assess the impact of various bioinformatic tools and parameters on linkage map quality.
Main Methods:
- The Reads2Map workflow integrates multiple single-nucleotide polymorphism (SNP) and genotype calling software (GATK, Stacks, TASSEL, Freebayes, updog, polyRAD, SuperMASSA).
- Linkage maps were constructed using OneMap and GUSMap.
- Simulated and empirical GBS data from diploid outcrossing populations were used for evaluation, alongside analysis of segregation distortion, contaminant samples, and haplotype-based markers.
Main Results:
- Evaluation of genotype calling software identified common GBS errors and informed the development of specific filters.
- The study assessed the effectiveness of genotype probabilities and global error rates in estimating genetic distances.
- Some bioinformatic approaches yielded dataset-dependent results, while others provided consistent, advantageous outcomes (dataset-independent).
Conclusions:
- The Reads2Map workflow defaults to dataset-independent approaches for GBS data, streamlining pipeline and parameter selection.
- This workflow reduces the need for extensive testing on new empirical datasets.
- The associated Reads2MapApp provides a user-friendly interface for interpreting results and selecting optimal parameters for specific data contexts.
