Should evolutionary geneticists worry about higher-order epistasis?
By Daniel M. Weinreich, Yinghong Lan, C. Scott Wylie, Robert B. Heckendorn. 2013
299 citations recorded by the source corpus.
Natural selection drives evolving populations up the fitness landscape, the projection from nucleotide sequence space to organismal reproductive success. While it has long been appreciated that topographic complexities on fitness landscapes can arise only as a consequence of epistatic interactions between mutations, evolutionary genetics has mainly focused on epistasis between pairs of mutations. Here we propose a generalization to the classical population genetic treatment of pairwise epistasis that yields expressions for epistasis among arbitrary subsets of mutations of all orders (pairwise, three-way, etc.). Our approach reveals substantial higher-order epistasis in almost every published fitness landscape. Furthermore we demonstrate that higher-order epistasis is critically important in two systems we know best. We conclude that higher-order epistasis deserves empirical and theoretical attention from evolutionary geneticists.
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Citations in the corpus
- Inferring fitness landscapes by regression produces biased estimates of epistasis (2014, unknown): “, higher-order interactions between loci) (59).”
- Networks beyond pairwise interactions: structure and dynamics (2020, background): “[703] Daniel M Weinreich, Yinghong Lan, C Scott Wylie, and Robert B Heckendorn.”
- Adaptation in protein fitness landscapes is facilitated by indirect paths (2016, unknown): “A total of 526 subgraphs were analyzed.”
- Predicting evolution (2017, background): “The latter are dominated by ‘microscopic’ epistasis, which decreases the number of accessible evolutionary paths 66,72 , but the local peaks and valleys in a given system can hardly be captured by a predictive model with few parameters 70,77–80 .”
- Identification of cis-suppression of human disease mutations by comparative genomics (2015, unknown): “0)% Largest estimate (12)% HumVar ‘disease’ (22,207 variants) ClinVar ‘pathogenic’ (10,596 variants)”