Inferring the shape of global epistasis

By Jakub Otwinowski, David M. McCandlish, Joshua B. Plotkin. 2018

249 citations recorded by the source corpus.

Significance How does an organism’s genetic sequence govern its measurable characteristics? New technologies provide libraries of randomized sequences to study this relationship in unprecedented detail for proteins and other molecules. Deriving insight from these data is difficult, though, because the space of possible sequences is enormous, so even the largest experiments sample a tiny minority of sequences. Moreover, the effects of mutations may combine in unexpected ways. We present a statistical framework to analyze such mutagenesis data. The key assumption is that mutations contribute in a simple way to some unobserved trait, which is related to the observed trait by a nonlinear mapping. Analyzing three proteins, we show that this model is easily interpretable and yet fits the data remarkably well. Genotype–phenotype relationships are notoriously complicated. Idiosyncratic interactions between specific combinations of mutations occur and are difficult to predict. Yet it is increasingly clear that many interactions can be understood in terms of global epistasis. That is, mutations may act additively on some underlying, unobserved trait, and this trait is then transformed via a nonlinear function to the observed phenotype as a result of subsequent biophysical and cellular processes. Here we infer the shape of such global epistasis in three proteins, based on published high-throughput mutagenesis data. To do so, we develop a maximum-likelihood inference procedure using a flexible family of monotonic nonlinear functions spanned by an I-spline basis. Our analysis uncovers dramatic nonlinearities in all three proteins; in some proteins a model with global epistasis accounts for virtually all of the measured variation, whereas in others we find substantial local epistasis as well. This method allows us to test hypotheses about the form of global epistasis and to distinguish variance components attributable to global epistasis, local epistasis, and measurement error.

Subfields: Genetics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

Revealing evolutionary constraints on proteins through sequence analysis (2018, background): “This phenomenon is known as global [10,14] or nonspecific [9] epistasis, and its relevance has been shown in evolution experiments [14], over and above contributions from specific epistasis [9, 15].”
Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding (2020, unknown): “Therefore, we used global epistasis models (Otwinowski et al., 2018) to decompose single-mutation effects from the set of all single- and multi-mutant backgrounds (Figures S2E–S2K).”
Comprehensive mapping of mutations to the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human serum antibodies (2021, methodology): “We next deconvolved variant-level escape scores into escape fraction estimates for single mutations using global epistasis models (Otwinowski et al., 2018) implemented in the dms_variants package, as detailed at (https://jbloomlab.”
AdaLead: A simple and robust adaptive greedy search algorithm for sequence design (2020, methodology): “While a large body of work has focused on building better local approximate models φ (cid:48) on already published data (Otwinowski et al., 2018; Alipanahi et al., 2015; Riesselman et al., 2017; Sinai et al., 2017), the more recent work is being done on optimization in this setting (Biswas et al.,…”
On the Theory of Transfer Learning: The Importance of Task Diversity (2020, methodology): “As ﬂexible tools for general-purpose, non-linear dimensionality reduction, index models have found broad applications in economics, ﬁnance, biology and the social sciences [Bickel et al., 1993, Li and Racine, 2007, Otwinowski et al., 2018].”
Complete mapping of mutations to the SARS-CoV-2 spike receptor-binding domain that escape antibody recognition (2020, unknown): “To do this we used global epistasis models (Otwinowski et al., 2018) as implemented in the the dms_variants package as detailed at (https://jbloomlab.”
Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding (2020, unknown): “Because many variants contain multiple mutations, we used global epistasis models to determine the effects of individual mutations from both singly and multiply mutated variants (Otwinowski et al., 2018) (Figure S3).”