Background information

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PBG/MCB 620 – DNA Fingerprinting

Fall 2012

Hands-on Computer Lab Project

October 15th 2012

Background information
Jointed goatgrass (Aegilops cylindrica Host) is an allotetraploid (2n=4x=28; genome CCDD) that formed through amphidiploidization of a hybrid or hybrids between two diploid species - Ae. tauschii Coss. (2n=2x=14; genome DD) and Ae. markgrafii (Greuter) Hammer (syn. Ae. caudata L.; 2n=2x=14; genome CC) (Chennaveeraiah, 1960; Johnson, 1967; Kihara and Matsumura, 1941). Studies on phenotypic (Maan, 1976; Tsunewaki, 1996) and organellar DNA variation among alloplasmic lines of wheat (Triticum aestivum L.) (Ogihara and Tsunewaki, 1988; Wang et al., 2000; Wang et al., 1997) suggested that Ae. cylindrica had received its cytoplasm from Ae. tauschii (D-type cytoplasm). A more recent analysis with chloroplast microsatellite markers had shown that although most accession of Ae. cylindrica received their cytoplasm fom Ae. tauschii, at least one accession had received its cytoplasm from Ae. markgrafii (Gandhi et al., 2005). Thus, both Ae. tauschii (D-type cytoplasm) and Ae. markgrafii (C-type cytoplasm) have contributed their cytoplasms to Ae. cylindrica.

The assignment
Using chloroplast microsatellite marker data for a collection of Ae. cylindrica accessions collected in the USA, determine genetic relationships and classify accessions based on cytoplasmic types (C-type versus D-type cytoplasms).
The txt file ‘JGG_data’ contains the marker data (20 markers) for one accession of wheat, 8 accessions of Ae. markgrafii, 19 accessions of Ae. tauschii, and 87 accessions of Ae. cylindrica.
The file ‘JGG_PCA.txt’ contains command lines to perform principal components analysis using a genetic distance matrix.
The text file ‘JGG_STR’ contains genotypic data for 8 accessions of Ae. markgrafii, 19 accessions of Ae. tauschii, and 87 accessions of Ae. cylindrica in a format that the program Structure 2.2 can use.
Components of the project

  1. Using the data in the ‘JGG_data.txt file, use PowerMarker to:

    1. produce a shared-allele genetic distance matrix

      1. save this matrix as a .txt file and name it ‘JGG_dist.txt’

    1. produce a neighbor-joining cladogram (tree)

      1. in the tree drawing program (MEGA) use wheat (1AE) as the outgroup or root of the cladogram

      2. print the cladogram

  1. Using the genetic distance matrix, perform a principal components analysis using SAS.

    1. Start SAS and paste the command lines from “JGG_pca.txt” in the SAS program editor window and run it.

      1. Save the output and name it “jgg_pca_out”.

      2. Compare the three graphs at the end of the output file to the cladogram that you generated with PowerMarker. To assist in the interpretation of the data, entries belonging to a particular species are given the same alphanumeric character.

3. Using the ‘JGG_STR’ text file, use Structure to assign lines to subpopulations.

      1. For this analysis assume that individuals belong to three subpopulations (K = 3)

      2. Save the output file in text format

      3. Open the output file with Excel

      4. Group lines based on their subpopulation membership

4. Please answer the following questions.

a. With respect to the PCA analysis:

i. Are patterns of relationship similar among the PCA graphs and the cladogram?

ii. Does a principal components analysis give any additional insight beyond that of a cladistic analysis? (Another way to think about this question is how does a cladogram versus PCA graphs constrain your perception of the data?)

iii. What percentage of the variation is captured in the first three eigenvalues? Would it be worthwhile to examine additional eigenvalues? (Note: SAS was instructed to limit the output to the first 30 eigenvalues and eigenvectors; the default is to generate as many eigenvalues and eigenvectors as there are variables).

b. With respect to the Structure analysis:

  1. What do the groupings obtained in Structure represent?

ii. What are the relationship between the PCA graphs, the cladogram, and the subpopulation groupings?

c. Finally, how do you think Ae. cylindrica ended up with two cytoplasms?


Chennaveeraiah, M.S. 1960. Karyomorphologic and cytotaxonomic studies in Aegilops. Acta Horti Gotob 23:85-178.

Gandhi, H.T., M.I. Vales, C.J. Watson, C.A. Mallory-Smith, N. Mori, M. Rehman, R.S. Zemetra, and O. Riera-Lizarazu. 2005. Chloroplast and nuclear microsatellite analysis of Aegilops cylindrica. Theor Appl Genet 111:561-72.

Johnson, B.L. 1967. Confirmation of the genome donors of Aegilops cylindrica. Nature 216:859 - 862

Kihara, H., and S. Matsumura. 1941. Genomanalyse bei Triticum und Aegilops. VIII. Rückkreuzung des Bastards A. caudata × A. cylindrica zu den Eltern und seine Nachkommen. Cytologia 11:493-506.

Maan, S.S. 1976. Cytoplasmic homology between Aegilops squarrosa L. and Ae. cylindrica Host. Crop Sci 16:757-761.

Ogihara, Y., and K. Tsunewaki. 1988. Diversity and evolution of chloroplast DNA in Triticum and Aegilops as revealed by restriction fragment analysis. Theor Appl Genet 76:321-322.

Tsunewaki, K. 1996. Plasmon analysis as the counterpart of genome analysis, p. 271-300, In P. P. Jauhar, ed. Methods of Genome Analysis in Plants. CRC Press Inc, Boca Raton, FL, USA.

Wang, G.-Z., Y. Matsuoka, and K. Tsunewaki. 2000. Evolutionary features of chondriome divergence in Triticum (wheat) and Aegilops shown by RFLP analysis of mitochondrial DNAs. Theor Appl Genet 100:221-231.

Wang, G.Z., N.T. Miyashita, and K. Tsunewaki. 1997. Plasmon analyses of triticum (wheat) and aegilops: PCR-single-strand conformational polymorphism (PCR-SSCP) analyses of organellar DNAs. Proc Natl Acad Sci U S A 94:14570-7.

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