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Theoretical and Applied Genetics

, Volume 110, Issue 1, pp 157–166 | Cite as

Detection and quantification of in vitro-culture induced chimerism using simple sequence repeat (SSR) analysis in Theobroma cacao (L.)

  • Carlos M. Rodríguez López
  • Andrew C. Wetten
  • Michael J. WilkinsonEmail author
Original Paper

Abstract

Mutation rates are often elevated in plants regenerated from in vitro culture, giving rise to so-called ‘somaclonal variation’. Detailed characterisation of mutation profiles that arise during culture should improve our understanding of processes influencing mutation and allow the selection of protocols yielding the fewest/least severe changes. Somatic mutations will usually produce genetic chimeras where unchanged alleles are retained by some cells. Such chimeras are difficult to detect but likely to form a significant proportion of any regenerant population. We present a simple protocol that enables the provisional diagnosis of both homogenous and chimeric mutants among large regenerant populations, together with a semi-quantitative means of estimating the proportion of mutant cells. The assay exploits consistent differential amplification of alternate simple sequence repeat alleles at heterozygous loci. Calibration of the relative amplification of alleles from two genotypes—and the synthetic chimeras created from them—revealed a strong linear relationship between ‘peak heights’ representing alternate alleles following capillary electrophoresis. The assay predicts chimeric composition to a reasonable level of confidence (±5%) so long as the infrequent allele exceeds 15% of the template. The system was applied to 233 regenerants of cocoa somatic embryogenesis and identified 72 (31%) putative chimeric mutants for slippage mutation or allele loss across two loci.

Keywords

Somatic Embryogenesis Simple Sequence Repeat Marker Somaclonal Variation Allele Loss Peak Ratio 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Cocoa Research UK for funding this study.

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Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Carlos M. Rodríguez López
    • 1
  • Andrew C. Wetten
    • 1
  • Michael J. Wilkinson
    • 1
    Email author
  1. 1.School of Plant SciencesThe University of ReadingReadingUK

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