Genetics, Vol. 152, 553-566, June 1999, Copyright © 1999

Toward Reconciling Inferences Concerning Genetic Variation in Senescence in Drosophila melanogaster

Frank H. Shawa, Daniel E. L. Promislowb, Marc Tatarc, Kimberly A. Hughesd, and Charles J. Geyere
a Department of Ecology, Evolution, and Behavior, University of Minnesota, Saint Paul, Minnesota 55108,
b Department of Genetics, University of Georgia, Athens, Georgia 30602-7223,
c Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912,
d Department of Life Sciences, Arizona State University West, Phoenix, Arizona 85069-7100
e School of Statistics, University of Minnesota, Minneapolis, Minnesota 55455

Corresponding author: Frank H. Shaw, Department of Ecology, Evolution and Behavior, University of Minnesota, 1987 Upper Buford Circle, St. Paul, MN 55108., fshaw{at}superb.ecology.umn.edu (E-mail)

Communicating editor: P. D. KEIGHTLEY

Standard models for senescence predict an increase in the additive genetic variance for log mortality rate late in the life cycle. Variance component analysis of age-specific mortality rates of related cohorts is problematic. The actual mortality rates are not observable and can be estimated only crudely at early ages when few individuals are dying and at late ages when most are dead. Therefore, standard quantitative genetic analysis techniques cannot be applied with confidence. We present a novel and rigorous analysis that treats the mortality rates as missing data following two different parametric senescence models. Two recent studies of Drosophila melanogaster, the original analyses of which reached different conclusions, are reanalyzed here. The two-parameter Gompertz model assumes that mortality rates increase exponentially with age. A related but more complex three-parameter logistic model allows for subsequent leveling off in mortality rates at late ages. We find that while additive variance for mortality rates increases for late ages under the Gompertz model, it declines under the logistic model. The results from the two studies are similar, with differences attributable to differences between the experiments.





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