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14 results for “genotype to phenotype map”
Data from: Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve
<div class="section abstract"> <p>Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organised as bricklayer's graphs, so called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype (GP) maps for RNA secondary structure and the HP model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer's graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.</p> </div>
Data from: The structure of an ancient genotype-phenotype map shaped the functional evolution of a protein family
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Data from: Maximum mutational robustness in genotype-phenotype maps follows a self-similar blancmange-like curve
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E. coli-ΦX174 genotype to phenotype map reveals flexibility and diversity in LPS structures
<p>Here are the raw sequencing data of all <em>E. coli</em> C and ΦX174 strains obtained during this study. A short description of each file's content can be found below.</p> <p> </p> <p><em>Bacteria</em></p> <ul> <li>"E.coliC_res.zip": Whole genome sequencing results of <em>E. coli</em> C WT and ΦX174-resistant strains (generated by fluctuation experiments). All<em> E. coli</em> C samples were prepared for whole-genome sequencing from one millilitre of stationary-phase cultures. Genomic DNA was extracted using the Wizard® Genomic DNA purification Kit (Promega, Germany). Bacterial samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany) using an Illumina Nextera DNA Flex Library Prep Kit to produce 150 bp paired-end reads.</li> </ul> <p> </p> <ul> <li>"E.coliC_res_excluded.zip": Whole genome sequencing results of the four ΦX174-resistant <em>E. coli</em> C strains (generated by fluctuation experiments) that have been excluded from the analyses<em>: E. coli </em>C R1, R3, R15, and R19. We found that both<em> E. coli</em> C R3 and R15 were not isogenic. While whole-genome sequencing from their respective glycerol stocks showed only a single mutation in <em>galE</em>, whole-genome sequencing carried on colony re-streaks showed that additional mutations were systematically associated with the single mutation in <em>galE</em> (<strong>see file "E.coliC_res_excluded_10_clones</strong>). <em>E. coli</em> C R1 displayed an unstable resistant phenotype. Finally, <em>E. coli</em> C R19 was partially resistant to ΦX174 WT. It carries a single mutation in the <em>yajC</em> gene, which encodes for a periplasmic protein. No apparent link to the LPS biosynthesis or assembly has been discovered yet, but <em>yajC</em> might play a role in phage DNA injection into the bacterium’s cytoplasmic membrane.</li> </ul> <p> </p> <ul> <li>"E.coliC_res_excluded_10_clones.zip": Whole genome sequencing results of 10 randomly chosen isolates of<em> E. coli </em>C R1, R3, and R15.</li> </ul> <p> </p> <ul> <li>"EcoliC.gb": <em>E. coli</em> C WT strain used as reference.</li> </ul> <p> </p> <p><em>Bacteriophages</em></p> <p> </p> <ul> <li>“PhiX174_PCR_399r_400f.zip”: ΦX174 samples were prepared for whole genome re-sequencing from 1 mL of phage lysate. Genomic DNA was extracted using the QIAprep Spin Miniprep Kit (QIAGEN), then amplified by performing 20 cycles of PCR using Q5® High-Fidelity 2X Master Mix (NEB). The sets of primers used for the amplification of ΦX174 whole genome are:</li> </ul> <p> </p> <p>PhiX174_399_r: CTTGACTCATGATTTCTTACC</p> <p>PhiX174_400_f: TTACTGAACAATCCGTACGTTTC</p> <p> </p> <p>DNA samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany). Sequencing was performed using an Illumina MiSeq DNA Flex Library Prep Kit to produce 150 bp paired-end reads.</p> <p> </p> <ul> <li>“PhiX174_PCR_2361r_2362f.zip”: ΦX174 samples were prepared for whole genome re-sequencing from 1 mL of phage lysate. Genomic DNA was extracted using the QIAprep Spin Miniprep Kit (QIAGEN), then amplified by performing 20 cycles of PCR using Q5® High-Fidelity 2X Master Mix (NEB). The sets of primers used for the amplification of ΦX174 whole genome are:</li> </ul> <p> </p> <p>PhiX174_2361_r: TCGCTTGGTCAACCCCTCAG</p> <p>PhiX174_2362_f: AGCGCGGTAGGTTTTCTGCT</p> <p> </p> <p>DNA samples were tested for quality, pooled, and sequenced by the Max-Planck Institute for Evolutionary Biology (Plön, Germany). Sequencing was performed using an Illumina MiSeq DNA Flex Library Prep Kit to produce 150 bp paired-end reads.</p> <ul> <li>“PhiX174_excluded.zip”: Since we removed R19 from the final analysis, we also removed its corresponding evolved phage obtained during the first evolution experiment (ΦX174 R19 T1). We also removed the phage infecting R5 (ΦX174 R5 T2) because it was not isogenic (confirmed by Sanger Sequencing).</li> </ul> <ul> <li>“PhiX174_Sequencing_Sanger.zip”: Both <em>F</em> and <em>H</em> genes were amplified by performing 35 cycles of PCR using Phusion® High-Fidelity PCR Master Mix with HF Buffer. The sequencing primers are listed in the<strong> Information_Sanger_Samples_ID.xlsx</strong> file.</li> </ul> <p> </p> <ul> <li>“PhiX174_ref.gb”: PhiX174 WT strain used as reference.</li> </ul> <p> </p> <p>We also include the raw data and pictures from our spotting tests and plaque assays used to complete the final matrix of infection.</p> <ul> <li>Spotting_tests_reanalyzed.xlsx”: Raw data from the pictures (see <strong>Photos_matrices</strong><strong>.zip</strong>) used to generate the Hierarchical agglomerative clustering analysis of the host ranges of evolved phage and Infection matrix of evolved ΦX174 phages on the 31 resistant E. coli C strains.</li> </ul> <p>The <strong>Photos_matrices</strong><strong>.zip</strong> folder contains</p> <p> </p> <ul> <li>“SSA_matrix_Bact_Lawn”: pictures of all evolution experiments obtained from the spotting test method where we spotted phages on bacterial lawns.</li> </ul> <p> </p> <ul> <li>“SSA_matrix_Phi_Lawn”: pictures of all evolution experiments obtained from the spotting test method where we spotted bacteria on phage lawns.</li> </ul> <p> </p> <ul> <li>“Mismatches”: We performed plaque assays when combinations of phages and bacteria showed discrepancies between the two spotting test methods</li> </ul> <p> </p> <ul> <li>“Plaque_assays_R12_R14”: pictures of plaque assays where we tested the sensitivity of <em>E. coli</em> C R12 and R14 toward a subset of evolved phages</li> </ul> <p> </p> <ul> <li>“Plaque_assays_of_PhiX174R22T1c1/R28T1c1_vs_WT”: pictures of plaque assays where we tested the sensitivity of <em>E. coli</em> C WT toward phages infecting R22 and R28.</li> </ul> <p> </p> <ul> <li>“displayImage.R”. Script made to look for the desired combination of phage and bacterium.</li> </ul> <p> </p> <p>Finally, we include the raw OD measurments:</p> <p> </p> <ul> <li>“All_EcoliC_growth_raw_data.xlsx”: raw OD measurements of each <em>E. coli</em> C resistant strains used in this study.</li> </ul>
Exploring the genotype-to-phenotype map using quantifiable patterns in metazoan genomic and morphological data
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Data from: QTL mapping genotype and phenotype data, Vanilla x MCM5001
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Data from: Is evolution predictable? quantitative genetics under complex genotype-phenotype maps
<p>A fundamental aim of post-genomic 21st century biology is to understand the genotype-phenotype map (GPM) or how specific genetic variation relates to specific phenotypic variation. Quantitative genetics approximates such maps using linear models, and has developed methods to predict the response to selection in a population. The other major field of research concerned with the GPM, developmental evolutionary biology or evo-devo, has found the GPM to be highly nonlinear and complex. Here we quantify how the predictions of quantitative genetics are affected by the complex, nonlinear maps found in developmental biology. We found that the disagreements between predicted and observed responses to selection are common, roughly in a third of generations, systematic and due to nonlinear nature of the genotype-phenotype map. They occur at all time scales, even from one generation to the next. Our results are a step towards integrating the fields studying the GPM.</p>
Mapping the Genotype, Phenotype, and Natural History of Phelan-McDermid Syndrome
ClinicalTrials.gov study NCT02461420. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Is evolution predictable? quantitative genetics under complex genotype-phenotype maps
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Data from: Ecology shapes epistasis in a genotype-phenotype-fitness map for stick insect colour
<p>Genetic interactions such as epistasis are widespread in nature and can shape evolutionary dynamics. Epistasis occurs due to non-linearity in biological systems, which can arise via cellular processes that convert genotype to phenotype and via selective processes that connect phenotype to fitness. Few studies in nature have connected genotype to phenotype to fitness for multiple potentially interacting genetic variants. Thus, the causes of epistasis in the wild remain poorly understood. Here, we show that epistasis for fitness is an emergent and predictable property of non-linear selective processes. We do so by measuring the genetic basis of cryptic colouration and survival in a field experiment with stick insects. We find that colouration exhibits a largely additive genetic basis, but with some effects of epistasis that enhance differentiation between colour morphs. In terms of fitness, different combinations of loci affecting colouration confer high survival in one host-plant treatment. Specifically, non-linear correlational selection for specific combinations of colour traits in this treatment drives the emergence of pairwise and higher-order epistasis for fitness at loci underlying colour. In turn, this results in a rugged fitness landscape for genotypes. In contrast, fitness epistasis was dampened in another treatment, where selection was weaker. Patterns of epistasis that are shaped by ecologically based selection could be common, and central to understanding fitness landscapes, the dynamics of evolution, and potentially other complex systems.</p>
Data from: The architecture of an empirical genotype-phenotype map
Recent advances in high-throughput technologies are bringing the study of empirical genotype-phenotype (GP) maps to the fore. Here, we use data from protein binding microarrays to study an empirical GP map of transcription factor (TF) binding preferences. In this map, each genotype is a DNA sequence. The phenotype of this DNA sequence is its ability to bind one or more TFs. We study this GP map using genotype networks, in which nodes represent genotypes with the same phenotype, and edges connect nodes if their genotypes differ by a single small mutation. We describe the structure and arrangement of genotype networks within the space of all possible binding sites for 525 TFs from three eukaryotic species encompassing three kingdoms of life (animal, plant, and fungi). We thus provide a high-resolution depiction of the architecture of an empirical GP map. Among a number of findings, we show that these genotype networks are "small-world" and assortative, and that they ubiquitously overlap and interface with one another. We also use polymorphism data from Arabidopsis thaliana to show how genotype network structure influences the evolution of TF binding sites in vivo. We discuss our findings in the context of regulatory evolution.
Data from: Ecology shapes epistasis in a genotype-phenotype-fitness map for stick insect colour
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Data from: The architecture of an empirical genotype-phenotype map
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Data from: Pleiotropy can be effectively estimated without counting phenotypes through the rank of genotype-phenotype map
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