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Quantitative structure analysis of genetic diversity among spring bread wheats Triticum aestivum L. from different geographical regions

by: Lin Hai, Carola Wagner, and Wolfgang Friedt

In: GENETICA, Vol. 130, Nr. 3 (2007) , p. 213-225.

Abstract

Genetic diversity in spring bread wheat T.aestivum L. was studied in a total of 69 accessions. For this purpose, 52 microsatellite SSR markers were used and a total of 406 alleles were detected, of which 182 44.8% occurred at a frequency of < 5% rare alleles. The number of alleles per locus ranged from 2 to 14 with an average of 7.81. The largest number of alleles per locus occurred in the B genome 8.65 as compared to the A 8.43 and D 5.93 genomes, respectively. The polymorphism index content PIC value varied from 0.24 to 0.89 with an average of 0.68. The highest PIC for all accessions was found in the B genome 0.71 as compared to the A 0.68 and D genomes 0.63. Genetic distance-based method standard UPGMA clustering and a model-based method structure analysis were used for cluster analysis. The two methods led to analogical results. Analysis of molecular variance AMOVA showed that 80.6% of the total variation could be explained by the variance within the geographical groups. In comparison

BibTeX record

@article{ISI:000247239100001,
  abstract = {Genetic diversity in spring bread wheat (T.aestivum L.) was studied in
a total of 69 accessions. For this purpose, 52 microsatellite (SSR)
markers were used and a total of 406 alleles were detected, of which
182 (44.8%) occurred at a frequency of < 5% (rare alleles). The
number of alleles per locus ranged from 2 to 14 with an average of
7.81. The largest number of alleles per locus occurred in the B genome
(8.65) as compared to the A (8.43) and D (5.93) genomes, respectively.
The polymorphism index content (PIC) value varied from 0.24 to 0.89
with an average of 0.68. The highest PIC for all accessions was found
in the B genome (0.71) as compared to the A (0.68) and D genomes
(0.63). Genetic distance-based method (standard UPGMA clustering) and a
model-based method (structure analysis) were used for cluster analysis.
The two methods led to analogical results. Analysis of molecular
variance (AMOVA) showed that 80.6% of the total variation could be
explained by the variance within the geographical groups. In comparison},
  added-at = {2008-06-04T14:34:51.000+0200},
  author = {Hai, Lin and Wagner, Carola and Friedt, Wolfgang},
  biburl = {http://www.bibsonomy.org/bibtex/2c878c6af1b0a05ea365cdb93d343ea0a/ipzgiessen},
  interhash = {65ca08fc92a6dc6f7ca41d49335586fb},
  intrahash = {c878c6af1b0a05ea365cdb93d343ea0a},
  issn = {0016-6707},
  journal = {GENETICA},
  keywords = {IFZ SSRs Triticum aestivum_L. genetic_diversity microsatellites quantitative_structure_analysis spring_bread_wheat},
  number = 3,
  pages = {213-225},
  timestamp = {2008-06-04T14:34:51.000+0200},
  title = {Quantitative structure analysis of genetic diversity among spring bread wheats (Triticum aestivum L.) from different geographical regions},
  volume = 130,
  year = 2007
}

Endnote record

%0 Journal Article
%1 ISI:000247239100001
%A Hai, Lin
%A Wagner, Carola
%A Friedt, Wolfgang
%D 2007
%J GENETICA
%K 
%N 3
%P 213-225
%T Quantitative structure analysis of genetic diversity among spring bread wheats (Triticum aestivum L.) from different geographical regions
%V 130
%X Genetic diversity in spring bread wheat (T.aestivum L.) was studied in
a total of 69 accessions. For this purpose, 52 microsatellite (SSR)
markers were used and a total of 406 alleles were detected, of which
182 (44.8%) occurred at a frequency of < 5% (rare alleles). The
number of alleles per locus ranged from 2 to 14 with an average of
7.81. The largest number of alleles per locus occurred in the B genome
(8.65) as compared to the A (8.43) and D (5.93) genomes, respectively.
The polymorphism index content (PIC) value varied from 0.24 to 0.89
with an average of 0.68. The highest PIC for all accessions was found
in the B genome (0.71) as compared to the A (0.68) and D genomes
(0.63). Genetic distance-based method (standard UPGMA clustering) and a
model-based method (structure analysis) were used for cluster analysis.
The two methods led to analogical results. Analysis of molecular
variance (AMOVA) showed that 80.6% of the total variation could be
explained by the variance within the geographical groups. In comparison