Identification of SNP and SSR Markers in Finger Millet Using Next Generation Sequencing Technologies

被引:46
|
作者
Gimode, Davis [1 ]
Odeny, Damaris A. [2 ]
de Villiers, Etienne P. [3 ]
Wanyonyi, Solomon [4 ]
Dida, Mathews M. [5 ]
Mneney, Emmarold E. [6 ]
Muchugi, Alice [1 ,7 ]
Machuka, Jesse [1 ]
de Villiers, Santie M. [8 ]
机构
[1] Kenyatta Univ, POB 43844-00100, Nairobi, Kenya
[2] ICRISAT Nairobi, POB 39063-00623, Nairobi, Kenya
[3] Univ Oxford, Ctr Trop Med, Oxford OX3 7BN, England
[4] Univ Eldoret, POB 1125-30100, Eldoret, Kenya
[5] Maseno Univ, Maseno, Kenya
[6] Mikocheni Agr Res Inst, POB 6226, Dar Es Salaam, Tanzania
[7] ICRAF Nairobi, POB 30677, Nairobi, Kenya
[8] Pwani Univ, POB 195-80108, Kilifi, Kenya
来源
PLOS ONE | 2016年 / 11卷 / 07期
关键词
NUCLEOTIDE POLYMORPHISM DISCOVERY; WIDE DNA POLYMORPHISMS; GENETIC-LINKAGE MAP; MICROSATELLITE MARKERS; GENOTYPING APPLICATIONS; POPULATION-STRUCTURE; ALIGNMENT; RICE; TRANSFERABILITY; CONSTRUCTION;
D O I
10.1371/journal.pone.0159437
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Finger millet is an important cereal crop in eastern Africa and southern India with excellent grain storage quality and unique ability to thrive in extreme environmental conditions. Since negligible attention has been paid to improving this crop to date, the current study used Next Generation Sequencing (NGS) technologies to develop both Simple Sequence Repeat (SSR) and Single Nucleotide Polymorphism (SNP) markers. Genomic DNA from cultivated finger millet genotypes KNE755 and KNE796 was sequenced using both Roche 454 and Illumina technologies. Non-organelle sequencing reads were assembled into 207 Mbp representing approximately 13% of the finger millet genome. We identified 10,327 SSRs and 23,285 non-homeologous SNPs and tested 101 of each for polymorphism across a diverse set of wild and cultivated finger millet germplasm. For the 49 polymorphic SSRs, the mean polymorphism information content (PIC) was 0.42, ranging from 0.16 to 0.77. We also validated 92 SNP markers, 80 of which were polymorphic with a mean PIC of 0.29 across 30 wild and 59 cultivated accessions. Seventy-six of the 80 SNPs were polymorphic across 30 wild germplasm with a mean PIC of 0.30 while only 22 of the SNP markers showed polymorphism among the 59 cultivated accessions with an average PIC value of 0.15. Genetic diversity analysis using the polymorphic SNP markers revealed two major clusters; one of wild and another of cultivated accessions. Detailed STRUCTURE analysis confirmed this grouping pattern and further revealed 2 sub-populations within wild E. coracana subsp. africana. Both STRUCTURE and genetic diversity analysis assisted with the correct identification of the new germplasm collections. These polymorphic SSR and SNP markers are a significant addition to the existing 82 published SSRs, especially with regard to the previously reported low polymorphism levels in finger millet. Our results also reveal an unexploited finger millet genetic resource that can be included in the regional breeding programs in order to efficiently optimize productivity.
引用
收藏
页数:23
相关论文
共 50 条
  • [31] Next generation sequencing technologies, their implications, and prospects for next-next gen technologies
    Schloss, J. A.
    IN VITRO CELLULAR & DEVELOPMENTAL BIOLOGY-ANIMAL, 2008, 44 : S2 - S3
  • [32] Detection and identification of transgenic events by next generation sequencing combined with enrichment technologies
    Frédéric Debode
    Julie Hulin
    Benoît Charloteaux
    Wouter Coppieters
    Marc Hanikenne
    Latifa Karim
    Gilbert Berben
    Scientific Reports, 9
  • [33] Molecular Variability of Finger Millet Isolates of Pyricularia grisea from Different Regions of India using SSR Markers
    Anjum, Syeda Samina
    Nagaraja, A.
    Nagamma, Gowdra
    Patil, Suresh
    Konda, Somashekhar
    Gowda, M. V. Channabyre
    JOURNAL OF PURE AND APPLIED MICROBIOLOGY, 2016, 10 (01): : 437 - 446
  • [34] SSR markers developed using next-generation sequencing technology in pineapple, Ananas comosus (L.) Merr.
    Nashima, Kenji
    Hosaka, Fumiko
    Terakami, Shingo
    Kunihisa, Miyuki
    Nishitani, Chikako
    Moromizato, Chie
    Takeuchi, Makoto
    Shoda, Moriyuki
    Tarora, Kazuhiko
    Urasaki, Naoya
    Yamamoto, Toshiya
    BREEDING SCIENCE, 2020, 70 (03) : 415 - 421
  • [35] Characterizing natural variation using next-generation sequencing technologies
    Gilad, Yoav
    Pritchard, Jonathan K.
    Thornton, Kevin
    TRENDS IN GENETICS, 2009, 25 (10) : 463 - 471
  • [36] The next generation: Using new sequencing technologies to analyse gene regulation
    Cullum, Rebecca
    Alder, Olivia
    Hoodless, Pamela A.
    RESPIROLOGY, 2011, 16 (02) : 210 - 222
  • [37] Isolation and characterization of 30 SNP markers in Guangdong bream (Megalobrama terminalis) by next-generation sequencing
    Yang, Jiping
    Li, Xinhui
    Li, Yuefei
    Zhu, Shuli
    Chen, Weitao
    Li, Jie
    CONSERVATION GENETICS RESOURCES, 2020, 12 (03) : 399 - 402
  • [38] Next-generation sequencing technologies: An overview
    Hu, Taishan
    Chitnis, Nilesh
    Monos, Dimitri
    Dinh, Anh
    HUMAN IMMUNOLOGY, 2021, 82 (11) : 801 - 811
  • [39] Landscape of Next-Generation Sequencing Technologies
    Niedringhaus, Thomas P.
    Milanova, Denitsa
    Kerby, Matthew B.
    Snyder, Michael P.
    Barron, Annelise E.
    ANALYTICAL CHEMISTRY, 2011, 83 (12) : 4327 - 4341
  • [40] Next-Generation DNA Sequencing Technologies
    Kurekci, Gulsum Kayman
    Dincer, Pervin
    ERCIYES MEDICAL JOURNAL, 2014, 36 (03) : 99 - 103