Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species
Abstract Background Diversifying animal cultivation demands efficient genotyping for enabling genomic selection, but non-model species lack efficient genotyping solutions. The aim of this study was to optimize a genotyping-by-sequencing (GBS) double-digest RAD-sequencing (ddRAD) pipeline. Bovine dat...
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2025-02-01
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| Online Access: | https://doi.org/10.1186/s12864-025-11296-4 |
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| author | Daniel Fischer Miika Tapio Oliver Bitz Terhi Iso-Touru Antti Kause Ilma Tapio |
| author_facet | Daniel Fischer Miika Tapio Oliver Bitz Terhi Iso-Touru Antti Kause Ilma Tapio |
| author_sort | Daniel Fischer |
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| description | Abstract Background Diversifying animal cultivation demands efficient genotyping for enabling genomic selection, but non-model species lack efficient genotyping solutions. The aim of this study was to optimize a genotyping-by-sequencing (GBS) double-digest RAD-sequencing (ddRAD) pipeline. Bovine data was used to automate the bioinformatic analysis. The application of the optimization was demonstrated on non-model European whitefish data. Results DdRAD data generation was designed for a reliable estimation of relatedness and is scalable to up to 384 samples. The GBS sequencing yielded approximately one million reads for each of the around 100 assessed samples. Optimizing various strategies to create a de-novo reference genome for variant calling (mock reference) showed that using three samples outperformed other building strategies with single or very large number of samples. Adjustments to most pipeline tuning parameters had limited impact on high-quality data, except for the identity criterion for merging mock reference genome clusters. For each species, over 15k GBS variants based on the mock reference were obtained and showed comparable results with the ones called using an existing reference genome. Repeatability analysis showed high concordance over replicates, particularly in bovine while in European whitefish data repeatability did not exceed earlier observations. Conclusions The proposed cost-effective ddRAD strategy, coupled with an efficient bioinformatics workflow, enables broad adoption of ddRAD GBS across diverse farmed species. While beneficial, a reference genome is not obligatory. The integration of Snakemake streamlines the pipeline usage on computer clusters and supports customization. This user-friendly solution facilitates genotyping for both model and non-model species. |
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| institution | Kabale University |
| issn | 1471-2164 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-f5a88a88d00441c58604ab9f7fd7a3c12025-02-09T12:13:48ZengBMCBMC Genomics1471-21642025-02-0126111710.1186/s12864-025-11296-4Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed speciesDaniel Fischer0Miika Tapio1Oliver Bitz2Terhi Iso-Touru3Antti Kause4Ilma Tapio5Applied Statistical Methods, Natural Resources, Natural Resources Institute Finland (Luke)Genomics and Breeding, Production Systems, Natural Resources Institute Finland (Luke)Genomics and Breeding, Production Systems, Natural Resources Institute Finland (Luke)Genomics and Breeding, Production Systems, Natural Resources Institute Finland (Luke)Genomics and Breeding, Production Systems, Natural Resources Institute Finland (Luke)Genomics and Breeding, Production Systems, Natural Resources Institute Finland (Luke)Abstract Background Diversifying animal cultivation demands efficient genotyping for enabling genomic selection, but non-model species lack efficient genotyping solutions. The aim of this study was to optimize a genotyping-by-sequencing (GBS) double-digest RAD-sequencing (ddRAD) pipeline. Bovine data was used to automate the bioinformatic analysis. The application of the optimization was demonstrated on non-model European whitefish data. Results DdRAD data generation was designed for a reliable estimation of relatedness and is scalable to up to 384 samples. The GBS sequencing yielded approximately one million reads for each of the around 100 assessed samples. Optimizing various strategies to create a de-novo reference genome for variant calling (mock reference) showed that using three samples outperformed other building strategies with single or very large number of samples. Adjustments to most pipeline tuning parameters had limited impact on high-quality data, except for the identity criterion for merging mock reference genome clusters. For each species, over 15k GBS variants based on the mock reference were obtained and showed comparable results with the ones called using an existing reference genome. Repeatability analysis showed high concordance over replicates, particularly in bovine while in European whitefish data repeatability did not exceed earlier observations. Conclusions The proposed cost-effective ddRAD strategy, coupled with an efficient bioinformatics workflow, enables broad adoption of ddRAD GBS across diverse farmed species. While beneficial, a reference genome is not obligatory. The integration of Snakemake streamlines the pipeline usage on computer clusters and supports customization. This user-friendly solution facilitates genotyping for both model and non-model species.https://doi.org/10.1186/s12864-025-11296-4Genotyping by sequencingSnakemakeVariant callingCattleAquacultureRepeatability |
| spellingShingle | Daniel Fischer Miika Tapio Oliver Bitz Terhi Iso-Touru Antti Kause Ilma Tapio Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species BMC Genomics Genotyping by sequencing Snakemake Variant calling Cattle Aquaculture Repeatability |
| title | Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species |
| title_full | Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species |
| title_fullStr | Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species |
| title_full_unstemmed | Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species |
| title_short | Fine-tuning GBS data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species |
| title_sort | fine tuning gbs data with comparison of reference and mock genome approaches for advancing genomic selection in less studied farmed species |
| topic | Genotyping by sequencing Snakemake Variant calling Cattle Aquaculture Repeatability |
| url | https://doi.org/10.1186/s12864-025-11296-4 |
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