Cost-Effective Discovery of Nucleotide Polymorphisms in Populations of an Allopolyploid Species Using Pool-Seq

Population genetics studies of allopolyploid species lag behind those of diploid species because of practical difficulties in analysis of homeologs-duplicated gene copies originating from hybridized parental species. Pool-Seq, i.e. massive parallel sequencing of pooled individuals, has high potential for detecting nucleotide polymorphisms within and among multiple populations; however, its use has been limited to diploid species. We applied Pool-Seq to an allopolyploid species by developing a bioinformatic pipeline that assigns reads to each homeolog as well as to each polymorphic allele within each homeolog. We simultaneously sequenced eight genes from twenty individuals from each of 24 populations, and found over 100 polymorphic sites in each homeolog. For two sites, we estimated allele frequencies using the number of reads and then validated these estimations by making individual-based estimations. Pool-Seq using our bioinformatic pipeline allows efficient evaluation of nucleotide polymorphisms in a large number of individuals, even in allopolyploid species. DOI: https://doi.org/10.4236/ajmb.2017.74012 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-139667 Published Version Originally published at: Hirao, Akira S; Onda, Yoshihiko; Shimizu-Inatsugi, Rie; Sese, Jun; Shimizu, Kentaro K; Kenta, Tanaka (2017). Cost-Effective Discovery of Nucleotide Polymorphisms in Populations of an Allopolyploid Species Using Pool-Seq. American Journal of Molecular Biology, 07(04):1031-1046. DOI: https://doi.org/10.4236/ajmb.2017.74012 American Journal of Molecular Biology, 2017, 7, 1031-1046 http://www.scirp.org/journal/ajmb ISSN Online: 2161-6663 ISSN Print: 2161-6620 DOI: 10.4236/ajmb.2017.74012 Sep. 14, 2017 1031 American Journal of Molecular Biology Cost-Effective Discovery of Nucleotide Polymorphisms in Populations of an Allopolyploid Species Using Pool-Seq Akira S. Hirao1*, Yoshihiko Onda2,3,4, Rie Shimizu-Inatsugi5, Jun Sese6, Kentaro K. Shimizu4,5, Tanaka Kenta1 1Sugadaira Research Station, Mountain Science Center, University of Tsukuba, Ueda, Japan 2Sugadaira Montane Research Center, University of Tsukuba, Ueda, Japan 3Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science, Yokohama, Japan 4Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan 5Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland 6Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan