ArticleGigaScience2022
Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 10 citations in OpenAlex.
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- Genotyping by sequencing reveals a chromosome A04 QTL governing whitefly resistance in upland cotton.Scientific reports · 2025Article
- Integrated genomic analysis of a diverse maize population reveals novel genes and superior predictive models for ear diameter.BMC genomics · 2025Article
- A major and stable QTL confers impatiens necrotic spot virus resistance in lettuce cv. Eruption.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- The evaluation of different combinations of enzyme set, aligner and caller in GBS sequencing of soybean.Plant methods · 2025Article
- Diurnal Regulation of SOS Pathway and Sodium Excretion Underlying Salinity Tolerance of Vigna marina.Plant, cell & environment · 2025Article
- Genetic architecture and genomic prediction of vase life in carnation.Frontiers in plant science · 2025Article
- Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps.GigaScience · 2022Article
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Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenotyping-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.
resultsWe 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).
conclusionsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.