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Maximum likelihood estimation of fitness components in experimental evolution

Jingxian Liu, Jackson Champer, Chen Liu, Joan Chung, Riona Reeves, Anisha Luthra, Yoo Lim Lee, Andrew G. Clark, View ORCID ProfilePhilipp W. Messer
doi: https://doi.org/10.1101/345660
Jingxian Liu
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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Jackson Champer
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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  • For correspondence: jc3248@cornell.edu messer@cornell.edu.
Chen Liu
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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Joan Chung
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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Riona Reeves
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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Anisha Luthra
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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Yoo Lim Lee
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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Andrew G. Clark
1Department of Biological Statistics and Computational Biology
2Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853
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Philipp W. Messer
1Department of Biological Statistics and Computational Biology
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  • ORCID record for Philipp W. Messer
  • For correspondence: jc3248@cornell.edu messer@cornell.edu.
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Abstract

Estimating fitness differences between allelic variants is a central goal of experimental evolution. Current methods for inferring selection from allele frequency time series typically assume that evolutionary dynamics at the locus of interest can be described by a fixed selection coefficient. However, fitness is an aggregate of several components including mating success, fecundity, and viability, and distinguishing between these components could be critical in many scenarios. Here we develop a flexible maximum likelihood framework that can disentangle different components of fitness and estimate them individually in males and females from genotype frequency data. As a proof-of-principle, we apply our method to experimentally-evolved cage populations of Drosophila melanogaster, in which we tracked the relative frequencies of a loss-of-function and wild-type allele of yellow. This X-linked gene produces a recessive yellow phenotype when disrupted and is involved in male courtship ability. We find that the fitness costs of the yellow phenotype take the form of substantially reduced mating preference of wild-type females for yellow males, together with a modest reduction in the viability of yellow males and females. Our framework should be generally applicable to situations where it is important to quantify fitness components of specific genetic variants, including quantitative characterization of the population dynamics of CRISPR gene drives.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 14, 2018.
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Maximum likelihood estimation of fitness components in experimental evolution
Jingxian Liu, Jackson Champer, Chen Liu, Joan Chung, Riona Reeves, Anisha Luthra, Yoo Lim Lee, Andrew G. Clark, Philipp W. Messer
bioRxiv 345660; doi: https://doi.org/10.1101/345660
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Maximum likelihood estimation of fitness components in experimental evolution
Jingxian Liu, Jackson Champer, Chen Liu, Joan Chung, Riona Reeves, Anisha Luthra, Yoo Lim Lee, Andrew G. Clark, Philipp W. Messer
bioRxiv 345660; doi: https://doi.org/10.1101/345660

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