Should evolutionary geneticists worry about higher-order epistasis?
01 Dec 2013-Current Opinion in Genetics & Development (Curr Opin Genet Dev)-Vol. 23, Iss: 6, pp 700-707
TL;DR: This work proposes a generalization to the classical population genetic treatment of pairwise epistasis that yields expressions for epistasis among arbitrary subsets of mutations of all orders, and demonstrates that higher-order epistasis is critically important in two systems the authors know best.
About: This article is published in Current Opinion in Genetics & Development.The article was published on 2013-12-01 and is currently open access. It has received 262 citations till now. The article focuses on the topics: Epistasis & Epistasis and functional genomics.
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TL;DR: A complete overview of the emerging field of networks beyond pairwise interactions, and focuses on novel emergent phenomena characterizing landmark dynamical processes, such as diffusion, spreading, synchronization and games, when extended beyond Pairwise interactions.
740 citations
Cites background from "Should evolutionary geneticists wor..."
...[703] Daniel M Weinreich, Yinghong Lan, C Scott Wylie, and Robert B Heckendorn....
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...[703] have defined as “the surprise at the phenotype when mutations are combined, given the constituent mutations individual effects”....
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TL;DR: This work reviews recent empirical and theoretical developments of the genotype–fitness map, identifies methodological issues and organizing principles, and discusses possibilities to develop more realistic fitness landscape models.
Abstract: A central topic in biology concerns how genotypes determine phenotypes and functions of organisms that affect their evolutionary fitness. This Review discusses recent advances in the development of empirical fitness landscapes and their contribution to theoretical analyses of the predictability of evolution. The genotype–fitness map (that is, the fitness landscape) is a key determinant of evolution, yet it has mostly been used as a superficial metaphor because we know little about its structure. This is now changing, as real fitness landscapes are being analysed by constructing genotypes with all possible combinations of small sets of mutations observed in phylogenies or in evolution experiments. In turn, these first glimpses of empirical fitness landscapes inspire theoretical analyses of the predictability of evolution. Here, we review these recent empirical and theoretical developments, identify methodological issues and organizing principles, and discuss possibilities to develop more realistic fitness landscape models.
608 citations
TL;DR: This work presents EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions and shows that it outperforms methods that do not account for epistasis.
Abstract: Many high-throughput experimental technologies have been developed to assess the effects of large numbers of mutations (variation) on phenotypes. However, designing functional assays for these methods is challenging, and systematic testing of all combinations is impossible, so robust methods to predict the effects of genetic variation are needed. Most prediction methods exploit evolutionary sequence conservation but do not consider the interdependencies of residues or bases. We present EVmutation, an unsupervised statistical method for predicting the effects of mutations that explicitly captures residue dependencies between positions. We validate EVmutation by comparing its predictions with outcomes of high-throughput mutagenesis experiments and measurements of human disease mutations and show that it outperforms methods that do not account for epistasis. EVmutation can be used to assess the quantitative effects of mutations in genes of any organism. We provide pre-computed predictions for ∼7,000 human proteins at http://evmutation.org/.
515 citations
Central European Institute of Technology1, Pompeu Fabra University2, Moscow Institute of Physics and Technology3, Nizhny Novgorod State Medical Academy4, Moscow State University5, Russian Academy of Sciences6, Russian National Research Medical University7, University of Michigan8, Weizmann Institute of Science9, Catalan Institution for Research and Advanced Studies10
TL;DR: An extensive region of the local fitness landscape of the green fluorescent protein from Aequorea victoria (avGFP) is visualize by measuring the native function (fluorescence) of tens of thousands of derivative genotypes of avGFP, indicating congruence between the fitness landscape properties at the local and global scales.
Abstract: Fitness landscapes depict how genotypes manifest at the phenotypic level and form the basis of our understanding of many areas of biology, yet their properties remain elusive. Previous studies have analysed specific genes, often using their function as a proxy for fitness, experimentally assessing the effect on function of single mutations and their combinations in a specific sequence or in different sequences. However, systematic high-throughput studies of the local fitness landscape of an entire protein have not yet been reported. Here we visualize an extensive region of the local fitness landscape of the green fluorescent protein from Aequorea victoria (avGFP) by measuring the native function (fluorescence) of tens of thousands of derivative genotypes of avGFP. We show that the fitness landscape of avGFP is narrow, with 3/4 of the derivatives with a single mutation showing reduced fluorescence and half of the derivatives with four mutations being completely non-fluorescent. The narrowness is enhanced by epistasis, which was detected in up to 30% of genotypes with multiple mutations and mostly occurred through the cumulative effect of slightly deleterious mutations causing a threshold-like decrease in protein stability and a concomitant loss of fluorescence. A model of orthologous sequence divergence spanning hundreds of millions of years predicted the extent of epistasis in our data, indicating congruence between the fitness landscape properties at the local and global scales. The characterization of the local fitness landscape of avGFP has important implications for several fields including molecular evolution, population genetics and protein design.
453 citations
TL;DR: DeepSequence is an unsupervised deep latent-variable model that predicts the effects of mutations on the basis of evolutionary sequence information that is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space.
Abstract: The functions of proteins and RNAs are defined by the collective interactions of many residues, and yet most statistical models of biological sequences consider sites nearly independently Recent approaches have demonstrated benefits of including interactions to capture pairwise covariation, but leave higher-order dependencies out of reach Here we show how it is possible to capture higher-order, context-dependent constraints in biological sequences via latent variable models with nonlinear dependencies We found that DeepSequence ( https://githubcom/debbiemarkslab/DeepSequence ), a probabilistic model for sequence families, predicted the effects of mutations across a variety of deep mutational scanning experiments substantially better than existing methods based on the same evolutionary data The model, learned in an unsupervised manner solely on the basis of sequence information, is grounded with biologically motivated priors, reveals the latent organization of sequence families, and can be used to explore new parts of sequence space
385 citations
References
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Book•
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01 Jan 1993
3,272 citations
"Should evolutionary geneticists wor..." refers background in this paper
...Current Opinion in Genetics & Development 2013, 23:700–707 Critically, such higher-order interactions cannot be captured by pairwise epistasis [6,7]....
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TL;DR: A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function.
Abstract: A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
2,225 citations
"Should evolutionary geneticists wor..." refers background in this paper
...For example, data on pairwise epistasis between gene deletions have provided insight into metabolic networks in yeast [8 ,9,10] and E....
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TL;DR: There is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner with the advent of high-throughput functional genomics.
Abstract: Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.
1,387 citations
"Should evolutionary geneticists wor..." refers background in this paper
...The recognition of epistasis between pairs of mutations in both discrete, Mendelian [1] and continuous [2,3] traits goes back roughly 100 years, but recent experimental advances draw attention to interactions between more than two mutations....
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TL;DR: The findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.
Abstract: The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.
1,319 citations
"Should evolutionary geneticists wor..." refers background in this paper
...Recently published analyses based on alignments of naturally occurring protein-coding sequences demonstrate that a great deal of evolutionary information is already present in pairwise epistasis [53,54 ,55]....
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Book•
26 Jul 2004
TL;DR: This book builds for the first time a general, quantitative theory for the origin of species based on the notion of fitness landscapes introduced by Sewall Wright in 1932, generalizing this notion to explore the consequences of the huge dimensionality of Fitness landscapes that correspond to biological systems.
Abstract: The origin of species has fascinated both biologists and the general public since the publication of Darwin's Origin of Species in 1859. Significant progress in understanding the process was achieved in the "modern synthesis," when Theodosius Dobzhansky, Ernst Mayr, and others reconciled Mendelian genetics with Darwin's natural selection. Although evolutionary biologists have developed significant new theory and data about speciation in the years since the modern synthesis, this book represents the first systematic attempt to summarize and generalize what mathematical models tell us about the dynamics of speciation. Fitness Landscapes and the Origin of Species presents both an overview of the forty years of previous theoretical research and the author's new results. Sergey Gavrilets uses a unified framework based on the notion of fitness landscapes introduced by Sewall Wright in 1932, generalizing this notion to explore the consequences of the huge dimensionality of fitness landscapes that correspond to biological systems. In contrast to previous theoretical work, which was based largely on numerical simulations, Gavrilets develops simple mathematical models that allow for analytical investigation and clear interpretation in biological terms. Covering controversial topics, including sympatric speciation and the effects of sexual conflict on speciation, this book builds for the first time a general, quantitative theory for the origin of species.
1,319 citations
Additional excerpts
...[14,15]], the evolutionary advantage of recombination [e....
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