Higher-order epistasis and phenotypic prediction

Zhou, Juannan, Wong, Mandy S, Chen, Wei-Chia, Krainer, Adrian R, Kinney, Justin B, McCandlish, David M (September 2022) Higher-order epistasis and phenotypic prediction. Proceedings of the National Academy of Sciences of USA, 119 (39). e2204233119. ISSN 0027-8424

[thumbnail of 2022-McCandlish-Higher-order-epistasis-and-phenotypic-prediction.pdf] PDF
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (3MB)
URL: https://www.ncbi.nlm.nih.gov/pubmed/36129941
DOI: 10.1073/pnas.2204233119


Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects not only genetic interactions between pairs of sites but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA [Formula: see text] splice sites, for which we also validate our model predictions via additional low-throughput experiments.

Item Type: Paper
Subjects: bioinformatics > genomics and proteomics > annotation > phenotyping
CSHL Authors:
Communities: CSHL labs > Kinney lab
CSHL labs > Krainer lab
CSHL labs > McCandlish lab
SWORD Depositor: CSHL Elements
Depositing User: CSHL Elements
Date: 27 September 2022
Date Deposited: 29 Sep 2022 21:08
Last Modified: 10 Oct 2022 14:26
URI: https://repository.cshl.edu/id/eprint/40723

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving