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Proc Natl Acad Sci U S A. 2014 Jun 3;111(22):E2301-9. doi: 10.1073/pnas.1400849111. Epub 2014 May 19.

Inferring fitness landscapes by regression produces biased estimates of epistasis.

Author information

1
Department of Biology, University of Pennsylvania, Philadelphia, PA 19104.
2
Department of Biology, University of Pennsylvania, Philadelphia, PA 19104 jplotkin@sas.upenn.edu.

Abstract

The genotype-fitness map plays a fundamental role in shaping the dynamics of evolution. However, it is difficult to directly measure a fitness landscape in practice, because the number of possible genotypes is astronomical. One approach is to sample as many genotypes as possible, measure their fitnesses, and fit a statistical model of the landscape that includes additive and pairwise interactive effects between loci. Here, we elucidate the pitfalls of using such regressions by studying artificial but mathematically convenient fitness landscapes. We identify two sources of bias inherent in these regression procedures, each of which tends to underestimate high fitnesses and overestimate low fitnesses. We characterize these biases for random sampling of genotypes as well as samples drawn from a population under selection in the Wright-Fisher model of evolutionary dynamics. We show that common measures of epistasis, such as the number of monotonically increasing paths between ancestral and derived genotypes, the prevalence of sign epistasis, and the number of local fitness maxima, are distorted in the inferred landscape. As a result, the inferred landscape will provide systematically biased predictions for the dynamics of adaptation. We identify the same biases in a computational RNA-folding landscape as well as regulatory sequence binding data treated with the same fitting procedure. Finally, we present a method to ameliorate these biases in some cases.

KEYWORDS:

experimental evolution; molecular evolution; penalized regression

PMID:
24843135
PMCID:
PMC4050575
DOI:
10.1073/pnas.1400849111
[Indexed for MEDLINE]
Free PMC Article

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