GBSathon Benchmarking reproducibility of Genotyping by sequencing analysis workflows through comparison with SNP chip and pedigree data
Rachael Ashby
10.6084/m9.figshare.11929500.v1
https://eresearchnz.figshare.com/articles/presentation/GBSathon_Benchmarking_reproducibility_of_Genotyping_by_sequencing_analysis_workflows_through_comparison_with_SNP_chip_and_pedigree_data/11929500
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<p>The advent of reduced representation genotyping-by-sequencing (GBS) provides a cost-
effective high-throughput genotyping platform to many ‘orphan’ species. This enables
downstream analyses including genomic selection, parentage assignment, conservation
genetics, population genetics and genome wide association studies. There are many
different workflows available for deriving SNPs from GBS data. Key aspects of any
bioinformatic workflow include accuracy, reproducibility and reliability. Few independent
studies benchmark multiple workflows to biological ‘gold standards’, such as pedigree or
SNP chip data, to assess these key aspects. Here, we benchmark open source SNP-calling
workflows for GBS data to assess their accuracy and reproducibility. To do this, we
generated GBS data for a cohort of 333 sheep. These have also been genotyped using a 50k
or 600k SNP chip. Furthermore, the cohort comprised 125 parent-offspring trios and all
individuals had multigenerational pedigree data. The SNPs called from the GBS workflows
were compared back to the gold standards to assess the accuracy, reproducibility and
reliability of SNP callers. Focusing on the bigger picture, we derived genomic relationship
matrices (GRMs) from all methods to compare the accuracy of the SNPs called for
downstream biological applications including relationship estimates among parents and
progeny.
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<p><b><u>ABOUT THE AUTHOR</u></b></p><p>Rachael Ashby is a postdoctoral researcher with the Bioinformatics team at AgResearch and
Genomics Aoteroa. Her research focusses on the use of next generation sequencing for
applications including genome assembly and genotyping-by-sequencing for genomic
management of highly diverse species. <br></p>
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2020-03-10 03:55:34
NeSI
eResearch
eResearch NZ 2020