Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps

Cristiane Hayumi Taniguti; Lucas Mitsuo Taniguti; Rodrigo Rampazo Amadeu; Jeekin Lau; Gabriel de Siqueira Gesteira; Thiago de Paula Oliveira; Getulio Caixeta Ferreira; Guilherme da Silva Pereira; David Byrne; Marcelo Mollinari; Oscar Riera-Lizarazu; Antonio Augusto Franco Garcia

doi:10.1093/gigascience/giad092

Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps

Gigascience. 2022 Dec 28:12:giad092. doi: 10.1093/gigascience/giad092.

Authors

Affiliations

¹ Department of Genetics, University of São Paulo, São Paulo 13418-900, Brazil.
² Department of Horticultural Sciences, Texas A&M University, College Station, TX 77843-0001, USA.
³ Mendelics Genomic Analysis, São Paulo 02511-000, Brazil.
⁴ Bioinformatics Research Center, Department of Horticultural Sciences, North Carolina State University, Raleigh, NC 27695-7566, USA.
⁵ Roslin Institute, University of Edinburgh, Edinburgh EH25 9RG, Scotland.
⁶ Department of Agronomy, Federal University of Viçosa, 36570-900, Brazil.

Abstract

Background: Genotyping-by-sequencing (GBS) provides affordable methods for genotyping hundreds of individuals using millions of markers. However, this challenges bioinformatic procedures that must overcome possible artifacts such as the bias generated by polymerase chain reaction duplicates and sequencing errors. Genotyping errors lead to data that deviate from what is expected from regular meiosis. This, in turn, leads to difficulties in grouping and ordering markers, resulting in inflated and incorrect linkage maps. Therefore, genotyping errors can be easily detected by linkage map quality evaluations.

Results: We developed and used the Reads2Map workflow to build linkage maps with simulated and empirical GBS data of diploid outcrossing populations. The workflows run GATK, Stacks, TASSEL, and Freebayes for single-nucleotide polymorphism calling and updog, polyRAD, and SuperMASSA for genotype calling, as well as OneMap and GUSMap to build linkage maps. Using simulated data, we observed which genotype call software fails in identifying common errors in GBS sequencing data and proposed specific filters to better handle them. We tested whether it is possible to overcome errors in a linkage map using genotype probabilities from each software or global error rates to estimate genetic distances with an updated version of OneMap. We also evaluated the impact of segregation distortion, contaminant samples, and haplotype-based multiallelic markers in the final linkage maps. Through our evaluations, we observed that some of the approaches produce different results depending on the dataset (dataset dependent) and others produce consistent advantageous results among them (dataset independent).

Conclusions: We set as default in the Reads2Map workflows the approaches that showed to be dataset independent for GBS datasets according to our results. This reduces the number of required tests to identify optimal pipelines and parameters for other empirical datasets. Using Reads2Map, users can select the pipeline and parameters that best fit their data context. The Reads2MapApp shiny app provides a graphical representation of the results to facilitate their interpretation.

Keywords: genetic maker; genotyping error; haplotype; multiallelic.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Chromosome Mapping / methods
Genotype
Genotyping Techniques* / methods
High-Throughput Nucleotide Sequencing* / methods
Humans
Polymorphism, Single Nucleotide
Software