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Keywords:

  • density kernel;
  • measurement error;
  • mutation of small and large effect;
  • Yeast deletion database

Abstract

The development of high-throughput fitness measurement methods provides unprecedented power to test evolutionary theories. However, with this comes new challenges regarding data quality and data analysis. We illustrate this by reanalysing the fitness distribution in several environments of yeast mutants (homo- and heterozygous) from the yeast deletion project. Originally created to study functional properties of genes, evolutionary biologists took advantage of this database to study evolutionary questions, such as dominance for fitness of mutations. We uncover several problems in this data set strongly affecting these questions that have remained unnoticed despite the numerous studies based on it. High-throughput methodologies are necessarily challenging, both experimentally and for data analysis: our point is not to criticize these approaches, but to pinpoint these challenges and to propose several improvements that may help avoid several shortcomings. Further, in the light of this finding, we question the conclusions regarding theories of dominance that have been made using this data set. We show that the data on deletion of small effects are not sufficiently reliable to be informative on this question. On the other hand, deletions of large effect exhibit no correlation between homo- and heterozygous fitness effects, a pattern that sheds new light on the hs correlation issue, with several consequences for the debate over the different theories of dominance.