ArticleProceedings of the National Academy of Sciences of the United States of America2014
Inferring fitness landscapes by regression produces biased estimates of epistasis.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed.
- Epistatic interactions inform rational design of synthetic microbial communities for bioremediation.Nature microbiology · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Global epistasis in ecosystems arises from resource constraints.bioRxiv : the preprint server for biology · 2026Article
- Epistasis and cryptic QTL identified using modified bulk segregant analysis of copper resistance in budding yeast.Genetics · 2025Article
- Replicating community dynamics reveals how initial composition shapes the functional outcomes of bacterial communities.Nature communications · 2025Article
- Collective Microbial Effects Drive Toxin Bioremediation and Enable Rational Design.bioRxiv : the preprint server for biology · 2025Article
- Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis.Nature communications · 2025Article
- On the Analysis of Protein Genetic Architecture: Response to "Protein sequence landscapes are not so simple".bioRxiv : the preprint server for biology · 2024Article
- Epistasis and cryptic QTL identified using modified bulk segregant analysis of copper resistance in budding yeast.bioRxiv : the preprint server for biology · 2024Article
- The simplicity of protein sequence-function relationships.Nature communications · 2024Article
- Unsupervised modeling of mutational landscapes of adeno-associated viruses viability.BMC bioinformatics · 2024Article
- The simplicity of protein sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Inference of annealed protein fitness landscapes with AnnealDCA.PLoS computational biology · 2024Article
- Statistically learning the functional landscape of microbial communities.Nature ecology & evolution · 2023Article
- Logistic Regression-Guided Identification of Cofactor Specificity-Contributing Residues in Enzyme with Sequence Datasets Partitioned by Catalytic Properties.ACS synthetic biology · 2022Article
- Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- On the sparsity of fitness functions and implications for learning.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Measurements drive progress in directed evolution for precise engineering of biological systems.Current opinion in systems biology · 2020Article
- Inferring a complete genotype-phenotype map from a small number of measured phenotypes.PLoS computational biology · 2020Article
- Evolutionary dynamics of competing phenotype-structured populations in periodically fluctuating environments.Journal of mathematical biology · 2020Article
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2 authors.
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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.
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