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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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Volume 10, Issue 1 (Jan 2011)

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Learning Monotonic Genotype-Phenotype Maps

Niko Beerenwinkel
  • ETH Zürich
/ Patrick Knupfer
  • ETH Zürich
/ Achim Tresch
  • Ludwig-Maximilians-Universität München
Published Online: 2011-01-06 | DOI: https://doi.org/10.2202/1544-6115.1603

Evolutionary escape of pathogens from the selective pressure of immune responses and from medical interventions is driven by the accumulation of mutations. We introduce a statistical model for jointly estimating the dynamics and dependencies among genetic alterations and the associated phenotypic changes. The model integrates conjunctive Bayesian networks, which define a partial order on the occurrences of genetic events, with isotonic regression. The resulting genotype-phenotype map is non-decreasing in the lattice of genotypes. It describes evolutionary escape as a directed process following a phenotypic gradient, such as a monotonic fitness landscape. We present efficient algorithms for parameter estimation and model selection. The model is validated using simulated data and applied to HIV drug resistance data. We find that the effect of many resistance mutations is non-linear and depends on the genetic background in which they occur.

Keywords: genotype-phenotype map; conjunctive Bayesian networks; HIV drug resistance; isotonic regression

About the article

Published Online: 2011-01-06



Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1603. Export Citation

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