Genotype-phenotype Landscapes Research Articles

Genotypic-phenotypic landscape computation based on first principle and deep learning.

The relationship between genotype and fitness is fundamental to evolution, but quantitatively mapping genotypes to fitness has remained challenging. We propose the Phenotypic-Embedding theorem (P-E theorem) that bridges genotype-phenotype through an encoder-decoder deep learning framework. Inspired by this, we proposed a more general first principle for correlating genotype-phenotype, and the P-E theorem provides a computable basis for the application of first principle. As an application example of the P-E theorem, we developed the Co-attention based Transformer model to bridge Genotype and Fitness model, a Transformer-based pre-train foundation model with downstream supervised fine-tuning that can accurately simulate the neutral evolution of viruses and predict immune escape mutations. Accordingly, following the calculation path of the P-E theorem, we accurately obtained the basic reproduction number (${R}_0$) of SARS-CoV-2 from first principles, quantitatively linked immune escape to viral fitness and plotted the genotype-fitness landscape. The theoretical system we established provides a general and interpretable method to construct genotype-phenotype landscapes, providing a new paradigm for studying theoretical and computational biology.

Towards interpretable imaging genomics analysis: Methodological developments and applications

Identifying the relationship between imaging features and genetic variation (a term coined“imaging genomics”) offers valuable insight into the pathogenesis of cancer, as well as cognitive and psychiatric disorders. However, how to integrate -omics and imaging data in a biologically meaningful approach to both provide pathobiological underpinning and achieve clinical applications remain challenging. In this review, we aim to discuss the difficulties in combining different sources of data and explaining the genotype-phenotype association, and we attempt to discuss the potential applications of imaging genomics in several (patho)physiological questions to better understand human physiology and disease. Future efforts on deciphering the genotype-phenotype landscapes by interpretable imaging genomics analyses are needed to provide new insights into molecular biology and molecular medicine.

Expanding the genotype-phenotype landscape of PDE10A-associated movement disorders (S42.004)

<h3>Objective:</h3> To report novel PDE10A genotype-phenotype in four affected family members <h3>Background:</h3> Phosphodiesterase 10A (PDE10A) controls body movements by regulating cyclic adenosine monophosphate signaling in the basal ganglia. Two classes of PDE10A variants are reported with distinctive genotype-phenotype correlation. The autosomal recessive mutations in the GAF-A and catalytic domains are associated with compromised membrane localization, and manifested with infantile onset chorea, developmental, and cognition delay with normal brain MRI. Conversely, autosomal dominant mutations in the GAF-B domain cause protein aggregates which results in childhood onset chorea in the context of normal cognition and development despite striatal nigral lesions. Herein, we report four cases of <i>PDE10A</i>-related chorea in a native Arab family with biallelic mutations in PDE10A. <h3>Design/Methods:</h3> Phenotypic characteristics of affected family members with PDE10A mutations were recorded, in addition to Sanger sequencing and in silico analysis to identify the mutations. <h3>Results:</h3> Four individuals from a consanguineous family affected with PDE10A mutations were observed for up to 40 years. Although these individuals displayed a clinical phenotype attributed to the recessive GAF-A mutations, they revealed an autosomal recessive GAF-B mutation (c.883G&gt;A:p. D295N; p.Asp295Asn) that was segregated from all affected individuals. In addition to chorea, we observed peculiar foot deformities and pronounced social phobia. In silico structural analysis suggested that the GAF-B mutation blocked allosteric PDE10A activation. The resulting lack of PDE10A activity without protein aggregation photocopies GAFA-mutations but is achieved through a distinct mechanism. <h3>Conclusions:</h3> Collectively, our work explains the recessive and dominant phenotypes of known variants and expands the genotype-phenotype landscape of PDE10A-associated movement disorders. <b>Disclosure:</b> Saeed Bohlega has nothing to disclose. Zainah Al- Qahtani has nothing to disclose. Francisco J. Guzmán-Vega has nothing to disclose. Dr. Ramakrishnan has nothing to disclose. Dr. Abusrair has nothing to disclose. Haya Aldosari has nothing to disclose. Amaal AlDakheel has nothing to disclose. Dr. Al Qahtani has nothing to disclose. Dorota Monies has nothing to disclose. Dr. Arold has nothing to disclose.

Transient mutation bias increases the predictability of evolution on an empirical genotype-phenotype landscape.

Predicting how a population will likely navigate a genotype-phenotype landscape requires consideration of selection in combination with mutation bias, which can skew the likelihood of following a particular trajectory. Strong and persistent directional selection can drive populations to ascend toward a peak. However, with a greater number of peaks and more routes to reach them, adaptation inevitably becomes less predictable. Transient mutation bias, which operates only on one mutational step, can influence landscape navigability by biasing the mutational trajectory early in the adaptive walk. This sets an evolving population upon a particular path, constraining the number of accessible routes and making certain peaks and routes more likely to be realized than others. In this work, we employ a model system to investigate whether such transient mutation bias can reliably and predictably place populations on a mutational trajectory to the strongest selective phenotype or usher populations to realize inferior phenotypic outcomes. For this we use motile mutants evolved from ancestrally non-motile variants of the microbe Pseudomonas fluorescens SBW25, of which one trajectory exhibits significant mutation bias. Using this system, we elucidate an empirical genotype-phenotype landscape, where the hill-climbing process represents increasing strength of the motility phenotype, to reveal that transient mutation bias can facilitate rapid and predictable ascension to the strongest observed phenotype in place of equivalent and inferior trajectories. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Expanding the genotype-phenotype landscape of PDE10A-associated movement disorders

BackgroundPhosphodiesterase 10A (PDE10A) controls body movements by regulating cyclic adenosine monophosphate signaling in the basal ganglia. Two classes of PDE10A variants are reported with distinctive genotype-phenotype correlation. The autosomal recessive mutations in the GAF-A and catalytic domains are associated with compromised membrane localization, and manifest with infantile onset chorea, developmental, and cognition delay with normal brain MRI. Conversely, autosomal dominant mutations in the GAF-B domain cause protein aggregates which results in childhood onset chorea in the context of normal cognition and development, with striatal lesions. MethodsPhenotypic characteristics of affected individuals with PDE10A mutations belonging to a single family were recorded. In addition, Sanger sequencing and in silico analysis were used to identify the mutations. Homozygosity mapping was applied together with whole exome sequencing. ResultsFour individuals from a consanguineous family affected with PDE10A mutations were observed for up to 40 years. Although these individuals displayed a clinical phenotype attributed to the recessive GAF-A mutations, they revealed a bi-allelic GAF-B mutation (c.883G > A:p. D295 N; p.Asp295Asn) that was segregated with all affected individuals. In addition to chorea, we observed peculiar foot deformities and pronounced social phobia, with normal brain MRI. In silico structural analysis suggested that the GAF-B mutation blocked allosteric PDE10A activation. The resulting lack of PDE10A activity likely phenocopies GAF-A mutations, and this is achieved through a distinct mechanism. ConclusionsCollectively, our findings demonstrate the association of recessive and dominant phenotypes of known variants, and further expands the genotype-phenotype landscape of PDE10A-associated movement disorders.

On the incongruence of genotype-phenotype and fitness landscapes.

The mapping from genotype to phenotype to fitness typically involves multiple nonlinearities that can transform the effects of mutations. For example, mutations may contribute additively to a phenotype, but their effects on fitness may combine non-additively because selection favors a low or intermediate value of that phenotype. This can cause incongruence between the topographical properties of a fitness landscape and its underlying genotype-phenotype landscape. Yet, genotype-phenotype landscapes are often used as a proxy for fitness landscapes to study the dynamics and predictability of evolution. Here, we use theoretical models and empirical data on transcription factor-DNA interactions to systematically study the incongruence of genotype-phenotype and fitness landscapes when selection favors a low or intermediate phenotypic value. Using the theoretical models, we prove a number of fundamental results. For example, selection for low or intermediate phenotypic values does not change simple sign epistasis into reciprocal sign epistasis, implying that genotype-phenotype landscapes with only simple sign epistasis motifs will always give rise to single-peaked fitness landscapes under such selection. More broadly, we show that such selection tends to create fitness landscapes that are more rugged than the underlying genotype-phenotype landscape, but this increased ruggedness typically does not frustrate adaptive evolution because the local adaptive peaks in the fitness landscape tend to be nearly as tall as the global peak. Many of these results carry forward to the empirical genotype-phenotype landscapes, which may help to explain why low- and intermediate-affinity transcription factor-DNA interactions are so prevalent in eukaryotic gene regulation.

Interpretable modeling of genotype–phenotype landscapes with state-of-the-art predictive power

Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biophysical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (landscape interpretable nonparametric model, https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape (GPL) measurements into a low-dimensional feature space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, including deep neural networks. LANTERN furthermore extracts useful insights of the landscape, including its inherent dimensionality, a latent space of additive mutational effects, and metrics of landscape structure. LANTERN facilitates straightforward discovery of fundamental mechanisms in GPLs, while also reliably extrapolating to unexplored regions of genotypic space.

Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq

SUMMARYA central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena—from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.

A single synonymous nucleotide change impacts the male-killing phenotype of prophage WO gene wmk.

Wolbachia are the most widespread bacterial endosymbionts in animals. Within arthropods, these maternally transmitted bacteria can selfishly hijack host reproductive processes to increase the relative fitness of their transmitting females. One such form of reproductive parasitism called male killing, or the selective killing of infected males, is recapitulated to degrees by transgenic expression of the prophage WO-mediated killing (wmk) gene. Here, we characterize the genotype-phenotype landscape of wmk-induced male killing in D. melanogaster using transgenic expression. While phylogenetically distant wmk homologs induce no sex-ratio bias, closely-related homologs exhibit complex phenotypes spanning no death, male death, or death of all hosts. We demonstrate that alternative start codons, synonymous codons, and notably a single synonymous nucleotide in wmk can ablate killing. These findings reveal previously unrecognized features of transgenic wmk-induced killing and establish new hypotheses for the impacts of post-transcriptional processes in male killing variation. We conclude that synonymous sequence changes are not necessarily silent in nested endosymbiotic interactions with life-or-death consequences.

Mutation bias interacts with composition bias to influence adaptive evolution.

Mutation is a biased stochastic process, with some types of mutations occurring more frequently than others. Previous work has used synthetic genotype-phenotype landscapes to study how such mutation bias affects adaptive evolution. Here, we consider 746 empirical genotype-phenotype landscapes, each of which describes the binding affinity of target DNA sequences to a transcription factor, to study the influence of mutation bias on adaptive evolution of increased binding affinity. By using empirical genotype-phenotype landscapes, we need to make only few assumptions about landscape topography and about the DNA sequences that each landscape contains. The latter is particularly important because the set of sequences that a landscape contains determines the types of mutations that can occur along a mutational path to an adaptive peak. That is, landscapes can exhibit a composition bias-a statistical enrichment of a particular type of mutation relative to a null expectation, throughout an entire landscape or along particular mutational paths-that is independent of any bias in the mutation process. Our results reveal the way in which composition bias interacts with biases in the mutation process under different population genetic conditions, and how such interaction impacts fundamental properties of adaptive evolution, such as its predictability, as well as the evolution of genetic diversity and mutational robustness.

Mutation spectrum of Charcot‐Marie‐Tooth disease among the Han Chinese in Taiwan

ObjectiveCharcot‐Marie‐Tooth disease (CMT) is a clinically and genetically heterogeneous group of inherited neuropathies. Mutations in more than 90 genes have been implicated in CMT; however, the mutational spectrum of CMT in Chinese population remains obscure. This study aims to provide a comprehensive overview of the frequency of mutations in Taiwanese patients with CMT and look for genotype‐phenotype correlations.MethodsMutational analyses were performed on 427 unrelated Taiwanese patients with CMT by polymorphic microsatellite markers analysis or real‐time fluorescent PCR for PMP22 duplication, Sanger sequencing for GJB1 mutations, and targeted sequencing covering 124 genes causing or relevant to inherited neuropathies. We also correlated the genotypes with the phenotypic features, such as age at disease onset and ulnar motor nerve conduction velocity.ResultsPathogenic mutations were identified in 312 patients (73.1%; 312/427), including 208 patients with a PMP22 duplication, 40 patients with a GJB1 mutation, and 64 patients with a mutation in one of other 18 CMT genes. A confirmed molecular diagnosis was achieved in 84.4% (266/315) of the patients with demyelinating CMT and 41.1% (46/112) of the patients with axonal CMT. Mutations in MPZ, MFN2, or NEFL are the most frequent disease causes in patients with infantile‐onset CMT (≤2 years), while PMP22 duplications and mutations in GJB1, MFN2, or MPZ are the frequent causes among patients with childhood‐ or adolescence‐onset CMT (3–9 years).InterpretationThis study provides a genotype‐phenotype landscape of CMT in Taiwan and highlights the unique spectrum of CMT genes frequencies among patients of Chinese origin.

Systematic analysis of complex genetic interactions.

To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.

Trigenic interactions in yeast link bioprocesses

Yeast Genomics To dissect the genotype-phenotype landscape of a cell, it is necessary to understand interactions between genes. Building on the digenic protein-protein interaction network, Kuzmin et al. created a trigenic landscape of yeast by using a synthetic genetic array (see the Perspective by Walhout). Triple-mutant analyses indicated that the majority of genes with trigenic associations functioned within the same biological processes. These converged on networks identified in the digenic interaction landscape. Although the overall effects were weaker for trigenic than for digenic interactions, trigenic interactions were more likely to bridge biological processes in the cell. Science , this issue p. [eaao1729][1]; see also p. [269][2] [1]: /lookup/doi/10.1126/science.aao1729 [2]: /lookup/doi/10.1126/science.aat4667

Constraints and consequences of the emergence of amino acid repeats in eukaryotic proteins.

Proteins with amino acid homorepeats have the potential to be detrimental to cells and are often associated with human diseases. Why, then, are homorepeats prevalent in eukaryotic proteomes? In yeast, homorepeats are enriched in proteins that are essential and pleiotropic and that buffer environmental insults. The presence of homorepeats increases the functional versatility of proteins by mediating protein interactions and facilitating spatial organization in a repeat-dependent manner. During evolution, homorepeats are preferentially retained in proteins with stringent proteostasis, which might minimize repeat-associated detrimental effects such as unregulated phase separation and protein aggregation. Their presence facilitates rapid protein divergence through accumulation of amino acid substitutions, which often affect linear motifs and post-translational-modification sites. These substitutions may result in rewiring protein interaction and signaling networks. Thus, homorepeats are distinct modules that are often retained in stringently regulated proteins. Their presence facilitates rapid exploration of the genotype-phenotype landscape of a population, thereby contributing to adaptation and fitness.

The complete local genotype-phenotype landscape for the alternative splicing of a human exon.

The properties of genotype–phenotype landscapes are crucial for understanding evolution but are not characterized for most traits. Here, we present a >95% complete local landscape for a defined molecular function—the alternative splicing of a human exon (FAS/CD95 exon 6, involved in the control of apoptosis). The landscape provides important mechanistic insights, revealing that regulatory information is dispersed throughout nearly every nucleotide in an exon, that the exon is more robust to the effects of mutations than its immediate neighbours in genotype space, and that high mutation sensitivity (evolvability) will drive the rapid divergence of alternative splicing between species unless it is constrained by selection. Moreover, the extensive epistasis in the landscape predicts that exonic regulatory sequences may diverge between species even when exon inclusion levels are functionally important and conserved by selection.

Human disease phenotypes associated with mutations in TREX1.

Considering that it is a single exon gene encoding a 314 amino acid protein, the genotype-phenotype landscape of TREX1 is remarkably complex. Here we briefly describe the human diseases so-far associated with mutations in TREX1, which include Aicardi-Goutières syndrome, familial chilblain lupus, systemic lupus erythematosus and retinal vasculopathy with cerebral leukodystrophy.

SHAPE MATTERS: EFFECT OF POINT MUTATIONS ON RNA SECONDARY STRUCTURE

A suitable model to dive into the properties of genotype-phenotype landscapes is the relationship between RNA sequences and their corresponding minimum free energy secondary structures. Relevant issues related to molecular evolvability and robustness to mutations have been studied in this framework. Here, we analyze the one-mutant neighborhood of the predicted secondary structure of 46 different RNAs, including tRNAs, viroids, larger molecules such as Hepatitis-δ virus, and several random sequences. The probability distribution of the effect of point mutations in linear structural motifs of the secondary structure is well fit by Pareto or Lognormal probability distributions functions, independent of the origin of the RNA molecule. This extends previous results to the case of natural sequences of diverse origins. We introduce a new indicator of robustness, the average weighted length of linear motifs (AwL) and demonstrate that it correlates with the average effect of point mutations in RNA secondary structures. Structures with a high AwL value display the highest structural robustness and cluster in particular regions of sequence space.

Evolutionary Origins of Transcription Factor Binding Site Clusters

Empirical studies have revealed that regulatory DNA sequences such as enhancers or promoters often harbor multiple binding sites for the same transcription factor. Such "homotypic site clustering" has been hypothesized as arising out of functional requirements of the sequences. Here, we propose an alternative explanation of this phenomenon that multisite enhancers are common because they are favored by evolutionary sampling of the genotype-phenotype landscape. To test this hypothesis, we developed a new computational framework specialized for population genetic simulations of enhancer evolution. It uses a thermodynamics-based model of enhancer function, integrating information from strong as well as weak binding sites, to determine the strength of selection. Using this framework, we found that even when simpler genotypes exist for a desired strength of regulation, relatively complex genotypes (enhancers with more sites) are more readily reached by the simulated evolutionary process. We show that there are more ways to "build" a fit genotype with many weak sites than with a few strong sites, and this is why evolution finds complex genotypes more often. Our claims are consistent with an empirical analysis of binding site content in enhancers characterized in Drosophila melanogaster and their orthologs in other Drosophila species. We also characterized a subtle but significant difference between genotypes likely to be sampled by evolution and equally fit genotypes one would obtain by uniform sampling of the fitness landscape, that is, an "evolutionary signature" in enhancer sequences. Finally, we investigated potential effects of other factors, such as rugged fitness landscapes, short local duplications, and noise characteristics of enhancers, on the emergence of homotypic site clustering. Homotypic site clustering is an important contributor to the complexity and function of cis-regulatory sequences. This work provides a simple null hypothesis for its origin, against which alternative adaptationist explanations may be evaluated, and cautions against "evolutionary mirages" present in common features of genomic sequence. The quantitative framework we develop here can be used more generally to understand how mechanisms of enhancer action influence their composition and evolution.

Multiple peaks and reciprocal sign epistasis in an empirically determined genotype-phenotype landscape

Insight into the ruggedness of adaptive landscapes is central to understanding the mechanisms and constraints that shape the course of evolution. While empirical data on adaptive landscapes remain scarce, a handful of recent investigations have revealed genotype-phenotype and genotype-fitness landscapes that appeared smooth and single peaked. Here, we used existing in vivo measurements on lac repressor and operator mutants in Escherichia coli to reconstruct the genotype-phenotype map that details the repression value of this regulatory system as a function of two key repressor residues and four key operator base pairs. We found that this landscape is multipeaked, harboring in total 19 distinct optima. Analysis showed that all direct evolutionary pathways between peaks involve significant dips in the repression value. Consistent with earlier predictions, we found reciprocal sign epistatic interactions at the repression minimum of the most favorable paths between two peaks. These results suggest that the occurrence of multiple peaks and reciprocal epistatic interactions may be a general feature in coevolving systems like the repressor-operator pair studied here.