Mariana Gabriela López1,2,3, Mónica Fass1,2, Juan Gabriel Rivas2, José Carbonell-Caballero4, Pablo Vera2, Andrea Puebla2, Raquel Defacio5, Joaquín Dopazo6, Norma Paniego1,2, Horacio Esteban Hopp2,7, Verónica Viviana Lia1,2,7. 1. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Hurlingham, Pcia. Buenos Aires, Argentina. 2. Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas (CICVyA), Instituto Nacional de Tecnología Agropecuaria (INTA), Instituto de Agrobiotecnología y Biología Molecular (IABIMO), INTA-CONICET, Hurlingham, Pcia. Buenos Aires, Argentina. 3. Instituto de Biomedicina de Valencia (IBV-CSIC), c/Jaume Roig 11, 46010, Valencia, Spain. 4. Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), Dr Aiguader 88, 08003 Barcelona, Spain. 5. Estación Experimental Agropecuaria INTA Pergamino, Pergamino Buenos Aires, Argentina. 6. Clinical Bioinformatics Area, Fundación Progreso y Salud, CDCA, Hospital Virgen del Rocío, c/Manuel Siurot s/n, 41013, Sevilla, Spain. 7. Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Intendente Güiraldes 2160, (1428), Ciudad Autónoma de Buenos Aires, Argentina.
Abstract
BACKGROUND AND AIMS: The number of plastome sequences has increased exponentially during the last decade. However, there is still little knowledge of the levels and distribution of intraspecific variation. The aims of this study were to estimate plastome diversity within Zea mays and analyse the distribution of haplotypes in connection with the landrace groups previously delimited for South American maize based on nuclear markers. METHODS: We obtained the complete plastomes of 30 South American maize landraces and three teosintes by means of next-generation sequencing (NGS) and used them in combination with data from public repositories. After quality filtering, the curated data were employed to search for single-nucleotide polymorphisms, indels and chloroplast simple sequence repeats. Exact permutational contingency tests were performed to assess associations between plastome and nuclear variation. Network and Bayesian phylogenetic analyses were used to infer evolutionary relationships among haplotypes. KEY RESULTS: Our analyses identified a total of 124 polymorphic plastome loci, with the intergenic regions psbE-rps18, petN-rpoB, trnL_UAG-ndhF and rpoC2-atpI exhibiting the highest marker densities. Although restricted in number, these markers allowed the discrimination of 27 haplotypes in a total of 51 Zea mays individuals. Andean and lowland South American landraces differed significantly in haplotype distribution. However, overall differentiation patterns were not informative with respect to subspecies diversification, as evidenced by the scattered distribution of maize and teosinte plastomes in both the network and Bayesian phylogenetic reconstructions. CONCLUSIONS: Knowledge of intraspecific plastome variation provides the framework for a more comprehensive understanding of evolutionary processes at low taxonomic levels and may become increasingly important for future plant barcoding efforts. Whole-plastome sequencing provided useful variability to contribute to maize phylogeographic studies. The structuring of haplotype diversity in the maize landraces examined here clearly reflects the distinction between the Andean and South American lowland gene pools previously inferred based on nuclear markers.
BACKGROUND AND AIMS: The number of plastome sequences has increased exponentially during the last decade. However, there is still little knowledge of the levels and distribution of intraspecific variation. The aims of this study were to estimate plastome diversity within Zea mays and analyse the distribution of haplotypes in connection with the landrace groups previously delimited for South American maize based on nuclear markers. METHODS: We obtained the complete plastomes of 30 South American maize landraces and three teosintes by means of next-generation sequencing (NGS) and used them in combination with data from public repositories. After quality filtering, the curated data were employed to search for single-nucleotide polymorphisms, indels and chloroplast simple sequence repeats. Exact permutational contingency tests were performed to assess associations between plastome and nuclear variation. Network and Bayesian phylogenetic analyses were used to infer evolutionary relationships among haplotypes. KEY RESULTS: Our analyses identified a total of 124 polymorphic plastome loci, with the intergenic regions psbE-rps18, petN-rpoB, trnL_UAG-ndhF and rpoC2-atpI exhibiting the highest marker densities. Although restricted in number, these markers allowed the discrimination of 27 haplotypes in a total of 51 Zea mays individuals. Andean and lowland South American landraces differed significantly in haplotype distribution. However, overall differentiation patterns were not informative with respect to subspecies diversification, as evidenced by the scattered distribution of maize and teosinte plastomes in both the network and Bayesian phylogenetic reconstructions. CONCLUSIONS: Knowledge of intraspecific plastome variation provides the framework for a more comprehensive understanding of evolutionary processes at low taxonomic levels and may become increasingly important for future plant barcoding efforts. Whole-plastome sequencing provided useful variability to contribute to maize phylogeographic studies. The structuring of haplotype diversity in the maize landraces examined here clearly reflects the distinction between the Andean and South American lowland gene pools previously inferred based on nuclear markers.
Authors: Aaron McKenna; Matthew Hanna; Eric Banks; Andrey Sivachenko; Kristian Cibulskis; Andrew Kernytsky; Kiran Garimella; David Altshuler; Stacey Gabriel; Mark Daly; Mark A DePristo Journal: Genome Res Date: 2010-07-19 Impact factor: 9.043
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Authors: Jazmín Ramos-Madrigal; Bruce D Smith; J Víctor Moreno-Mayar; Shyam Gopalakrishnan; Jeffrey Ross-Ibarra; M Thomas P Gilbert; Nathan Wales Journal: Curr Biol Date: 2016-11-17 Impact factor: 10.834