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Optimization of the genotyping-by-sequencing strategy for population genomic analysis in conifers
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences. State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing, China.
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2015 (English)In: Molecular Ecology Resources, ISSN 1755-098X, E-ISSN 1755-0998, Vol. 15, no 4, 711-722 p.Article in journal (Refereed) Published
Abstract [en]

Flexibility and low cost make genotyping-by-sequencing (GBS) an ideal tool for population genomic studies of nonmodel species. However, to utilize the potential of the method fully, many parameters affecting library quality and single nucleotide polymorphism (SNP) discovery require optimization, especially for conifer genomes with a high repetitive DNA content. In this study, we explored strategies for effective GBS analysis in pine species. We constructed GBS libraries using HpaII, PstI and EcoRI-MseI digestions with different multiplexing levels and examined the effect of restriction enzymes on library complexity and the impact of sequencing depth and size selection of restriction fragments on sequence coverage bias. We tested and compared UNEAK, Stacks and GATK pipelines for the GBS data, and then developed a reference-free SNP calling strategy for haploid pine genomes. Our GBS procedure proved to be effective in SNP discovery, producing 7000-11000 and 14751 SNPs within and among three pine species, respectively, from a PstI library. This investigation provides guidance for the design and analysis of GBS experiments, particularly for organisms for which genomic information is lacking.

Place, publisher, year, edition, pages
2015. Vol. 15, no 4, 711-722 p.
Keyword [en]
conifer genome, genotyping-by-sequencing, library preparation parameters, sequencing depth, SNP lidation
National Category
Cell and Molecular Biology
Identifiers
URN: urn:nbn:se:umu:diva-106317DOI: 10.1111/1755-0998.12342ISI: 000356679800004PubMedID: 25367371OAI: oai:DiVA.org:umu-106317DiVA: diva2:842261
Available from: 2015-07-17 Created: 2015-07-10 Last updated: 2017-12-04Bibliographically approved

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Pan, JinWang, BaoshengWang, Xiao-Ru

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