Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps
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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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and 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 required of 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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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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for …
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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and 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 required of 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.
This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giad092), which carries out open, named peer-review. This review is published under a CC-BY 4.0 license:
Reviewer name: Peter M. Bourke
I read with interest the manuscript on Reads2Map, a really impressive amount of work went into this and I congratulate the authors on it. However, it is precisely this almost excessive amount of results that for me was the major drawback with this paper. I got lost in all the detail, and therefore I have suggested a Major Revision to reflect that I think the paper could be somehow made more stream lined with a clearer central message and fewer figures in the text. Line numbers would have been helpful, I have tried to give the best indication of page number and position, but in future @GigaScience please stick to line numbers for reviewers, it's a pain in the neck without them.
Overall I think this is an excellent manuscript of general interest to anyone working in genomics, and definitely worthy of publication.Here are my more detailed comments:
General comment: if a user would like to use GBS data for other population types than those amenable for linkage mapping (e.g. GWAS or genomic prediction, so a diversity panel or a breeding panel), how could your tool be useful for them?
Other general comment: the manuscript is long with an exhaustive amount of figures and supplementary materials. Does it really need to be this detailed? It appears like the authors lost the run of themselves a little bit and tried to cram everything in, and in doing so risk losing the point of the endeavour. What is the central message of this manuscript? Regarding the figures, the reader cannot refer to the figures easily as they are now mainly contained on another page. Do you really need Figures 16-18 for example?
Figures 13 and 14 could be combined perhaps? I am sure that at most 10 figures and maybe even less are needed in the main text, otherwise figures will always be on different pages and hence lose their impact in the text call-out.
Abstract and page 4: "global error rate of 0.05" - How do you motivate the use of a global error rate of 5%? Surely this is dataset-dependent?
Page 4 - how can a user estimate an error per marker per individual? The description of the create_probs function suggests there is an automatic methodology to do this, but I don't see it described. You could perhaps refer to Zheng et al's software polyOrigin, which actually locally optimises the error prior per datapoint. Maybe something for the discussion.
Page 6 "recombination fraction giving the genomic order" do you mean "given"?Page 10 section Effects of contaminant samples - if you look at Figure 9 you can see that the presence of contaminant samples seems to have an impact on the genotypes of other, non-contaminant samples, especially using GATK and 5% global error. With the contaminants present, the number of XO points decreases in many other samples. This is very odd behaviour I would have thought. Is it known whether this apparent suppresion of recombination breakpoints in non-contaminant individuals is likely to be "correct"? Perhaps the SNP caller was running under the assumption that all individuals were part of the same F1? If the SNP caller was run without this assumption (eg. specifying only HW equilibrium, or model-free) would we still see the same effect? This is for me a quite worrying result but something that you make no reference to as far as I can tell.
Page 12 "Effects of segregation distortion" In your study you only considered a single linkage group. One of the primary issues with segregation distortion in mapping is that it can lead to linkage disequilibrium between chromosomes, if selection has occurred on multiple loci. This can then lead to false linkages across linkage groups. Perhaps good to mention this.Page 12 "have difficulty missing linkage information" - missing word "with"
Page 17 I see no mention of the impact of errors in the multi-allelic markers on the efficiency, particularly of order_seq which seems to be very poorly-performing with only bi-allelics (Fig 20). If bi-allelic SNPs have errors then it is not obvious why multi-SNP haplotypes should not also have errors.
Page 3 Figure 1 - here the workflow shows multiple options for a number of the steps, which can lead to the creation of many map variants (e.g. 816 maps as mentioned on Page 4). Should all users produce 816 variants of their maps? With potentially millions of markers, this is going to take a huge amount of time (most users will want 100% of all chromosomes, not 37% of a single chromosome). Or should this be done for only a subset of markers? What if there is no reference sequence available to select a subset? As there are no clear recommendations, I suspect that the specific combination of pipeline choices will usually be datasetdependent. You actually mention this in the discussion
page 17. And with only 2 real datasets from 2 different species, there is also no way to tell if eg. GATK works best in rose, or updog should be used for monocots but not dicots etc. It would be helpful if the authors were more explicit about how their tool informs "best practices for GBS analysis" for ordinary users. Perhaps it is there, but for me this message gets lost.
Page 17 "updates in this version 3.0 to resolve issues with inflated genetic maps" - if I look at Figure 20, it seems that issues with inflated map length have not yet been fully resolved!
Page 17 "we provide users tools to select the best approaches" - similar comment as before - does this mean users should build > 800 maps with a subset of their dataset first, and then use this single approach for the whole dataset? It is not explicitly stated whether this is the guidance given. What is the eventual aim - to produce a good linkage map, or to use the linkage map to critically compare genotyping tools?
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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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for …
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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and 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 required of 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.
This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giad092), which carries out open, named peer-review. This review is published under a CC-BY 4.0 license:
Reviewer Name: Ramil Mauleon
The paper titled "Developing best practices for genotyping-by-sequencing analysis using linkage maps as benchmarks" aims to present an end to end workflow uses GBS genotyping datasets to generate genetic linkage maps. This is a valuable tool for geneticists intending to generate a high confidence linkage map from a mapping population with GBS data as input.I got confused on reading the MS though, is this a workflow paper or is this a review of the component software for each step of genetic mapping and how parameter/use differences affect the output ? If it's a review, then the choice of software reviewed are not comprehensive enough, esp on SNP calling, and linkage mapping.There is no clear justification why each component software was used,example the use of GATK and freebayes for SNP calling I am familiar with using TASSEL GBS and STACKS for SNP calling using GBS data, why weren't they included in the SNP calling software. The MS would benefit greatly from including these SNP calling software in their benchmarking.Onemap and gusmap seems also pre-selected for linkage mapping, without reason for use, or maybe the reason(s) were not highlighted in the text. I've had experience in the venerable MAPMAKER and MSTMap, and would like to see more comparisons of the chosen genetic linkage mapping software with others, if this is the intent of the MS.The MS also clearly focuses on genetic linkage mapping using GBS, which should be more explicitly stated in the title. GBS is also extensively used in diversity collections and there is scant mention of this in the MS, and whether the workflow could be adapted to such populations.Versions of sofware used in the workflow are also not explicitly stated within the MS.The shiny app is also not demonstrated well in the MS, it could be presented better with screenshots of the interface , with one or two sample use cases.
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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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for …
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 PCR 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 SNP calling and updog, polyRAD, and SuperMASSA for genotype calling, and 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 required of 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.
This work has been peer reviewed in GigaScience (see https://doi.org/10.1093/gigascience/giad092), which carries out open, named peer-review. This review is published under a CC-BY 4.0 license:
**Reviewer Name: Zhenbin Hu **
In this MS, the authors tried to develop a framework for using GBS data for downstream analysis and reduce the impact of sequence errors caused by GBS. However, sequence error is an issue not specific to GBS, it is also for whole genome sequences. Actually, I think the major issue for GBS is the missing data. However, in this MS, the authors did not test the impact of missing data on downstream analysis.The authors also mentioned that sequencing error may cause distortion segregation in linkage map construction, however, distortion segregation in linkage map construction can also happen for correct genotyping data. The distortion segregation can be caused by individual selection during the construction of the population. So I don't think it is correct to use distortion segregation to correct sequence errors.The authors need to clear the major question of this MS, in the abstract, the authors highlight the sequence errors, while in the introduction, the authors highlight the package for linkage map construction (the last paragraph). Actually, from the MS, authors were assembling a framework for genotyping-by-sequencing data.Two major reduced-represented sequencing approaches, GBS and RADseq, have specific tools for genotype calling, such as Tassel and Stack. However, the authors used the GATK and Freebayes pipeline for variant calling, authors need to present the reason they were not using TASSEL and Stack.In the genotyping-by-sequencing data, individuals were barcoded and mixed during sequencing, what package/code was used to split the individuals (demultiplex) from the fastq for GATK and Freebayes pipeline?The maximum missing data was allowed at 25% for markers data, how about for the individual missing rate?On page 6, the authors mentioned 'seuqnece size of 350', what that means?
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