Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-28T00:10:25.471Z Has data issue: false hasContentIssue false

Estimating numbers of EMS-induced mutations affecting life history traits in Caenorhabditis elegans in crosses between inbred sublines

Published online by Cambridge University Press:  24 February 2004

DANIEL L. HALLIGAN
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
ANDREW D. PETERS
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK Department of Zoology, University of British Columbia, 6270 University Blvd, Vancouver, BC, V6T 1Z4, Canada
PETER D. KEIGHTLEY
Affiliation:
Institute of Cell, Animal and Population Biology, University of Edinburgh, West Mains Road, Edinburgh EH9 3JT, UK
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Inbred lines of the nematode Caenorhabditis elegans containing independent EMS-induced mutations were crossed to the ancestral wild-type strain (N2). Replicated inbred sublines were generated from the F1 offspring under conditions of minimal selection and, along with the N2 and mutant progenitor lines, were assayed for several fitness correlates including relative fitness (w). A modification of the Castle–Wright estimator and a maximum-likelihood (ML) method were used to estimate the numbers and effects of detectable mutations affecting these characters. The ML method allows for variation in mutational effects by fitting either one or two classes of mutational effect, and uses a Box–Cox power transformation of residual values to account for a skewed distribution of residuals. Both the Castle–Wright and the ML analyses suggest that most of the variation among sublines was due to a few (~1·5–2·5 on average) large-effect mutations. Under ML, a model with two classes of mutational effects, including a class with small effects, fitted better than a single mutation class model, although not significantly better. Nonetheless, given that we expect there to be many mutations induced per line, our results support the hypothesis that mutations vary widely in their effects.

Type
Research Article
Copyright
© 2003 Cambridge University Press