On the three methods for estimating deleterious genomic mutation parameters

Genet Res. 1998 Jun;71(3):223-36. doi: 10.1017/s0016672398003255.


Due to the tremendous cost of the traditional mutation-accumulation approach (the Bateman-Mukai technique), data are rare for deleterious mutation parameters such as genomic mutation rate, selection and dominance coefficients. Two alternative approaches have been developed (the Morton-Charlesworth and Deng-Lynch techniques). Except for the Deng-Lynch method, the statistical properties (bias and sampling variance) of these techniques are poorly understood; therefore we investigated them using computer simulation. With constant fitness effects of mutations, the Bateman-Mukai (assuming additive effects) and Deng-Lynch (assuming multiplicative effects) techniques are unbiased; the Morton-Charlesworth technique (assuming multiplicative effects) is very biased if fitness is used in the regression to estimate h, but slightly biased if the logarithm of fitness is used. With variable fitness effects, all techniques are biased. The Deng-Lynch technique is statistically better than the others except when fitness is used to estimate the average degree of dominance in selfing populations with the Morton-Charlesworth technique. If fitness effects are multiplicative but additivity is assumed, the Bateman-Mukai technique is biased under constant fitness effects, and less biased under variable fitness effects relative to when fitness effects are additive (as assumed by the technique). Our study not only quantifies the degree of bias under the biologically plausible situations investigated, thus forming a basis for correct inference of the true parameters by using these techniques, but also provides insights into the relative efficiencies of these techniques when the same number of genotypes are handled experimentally.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Computer Simulation*
  • Models, Genetic*
  • Models, Statistical*
  • Mutagenesis
  • Mutation*