ArticlePLoS computational biology2024
An extension of the Walsh-Hadamard transform to calculate and model epistasis in genetic landscapes of arbitrary shape and complexity.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Deconstructing empirical fitness seascapes across scales of granularity.G3 (Bethesda, Md.) · 2026Article
- Gaugefixer: overcoming parameter non-identifiability in models of sequence-function relationships.BMC bioinformatics · 2026Article
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- GaugeFixer: overcoming parameter non-identifiability in models of sequence-function relationships.bioRxiv : the preprint server for biology · 2026Article
- On learning functions over biological sequence space: relating Gaussian process priors, regularization, and gauge fixing.Journal of mathematical biology · 2026Article
- Deconstructing empirical fitness seascapes across scales of granularity.bioRxiv : the preprint server for biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- Higher-order epistasis drives evolutionary unpredictability toward novel antibiotic resistance.bioRxiv : the preprint server for biology · 2025Article
- Gauge fixing for sequence-function relationships.PLoS computational biology · 2025Article
- MoCHI: neural networks to fit interpretable models and quantify energies, energetic couplings, epistasis, and allostery from deep mutational scanning data.Genome biology · 2024Article
- The simplicity of protein sequence-function relationships.Nature communications · 2024Article
- Gauge fixing for sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Article
- Article
- The simplicity of protein sequence-function relationships.bioRxiv : the preprint server for biology · 2024Article
- Epistasis and pleiotropy shape biophysical protein subspaces associated with drug resistance.Physical review. E · 2023Article
- Evolutionary druggability: leveraging low-dimensional fitness landscapes towards new metrics for antimicrobial applications.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Accurate models describing the relationship between genotype and phenotype are necessary in order to understand and predict how mutations to biological sequences affect the fitness and evolution of living organisms. The apparent abundance of epistasis (genetic interactions), both between and within genes, complicates this task and how to build mechanistic models that incorporate epistatic coefficients (genetic interaction terms) is an open question. The Walsh-Hadamard transform represents a rigorous computational framework for calculating and modeling epistatic interactions at the level of individual genotypic values (known as genetical, biological or physiological epistasis), and can therefore be used to address fundamental questions related to sequence-to-function encodings. However, one of its main limitations is that it can only accommodate two alleles (amino acid or nucleotide states) per sequence position. In this paper we provide an extension of the Walsh-Hadamard transform that allows the calculation and modeling of background-averaged epistasis (also known as ensemble epistasis) in genetic landscapes with an arbitrary number of states per position (20 for amino acids, 4 for nucleotides, etc.). We also provide a recursive formula for the inverse matrix and then derive formulae to directly extract any element of either matrix without having to rely on the computationally intensive task of constructing or inverting large matrices. Finally, we demonstrate the utility of our theory by using it to model epistasis within both simulated and empirical multiallelic fitness landscapes, revealing that both pairwise and higher-order genetic interactions are enriched between physically interacting positions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.