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56 results for “genotype-phenotype”
Patients data for meta-analysis of genotype-phenotype associations in Bardet-Biedl Syndrome
<p>Data used for metaanalysis of the genotype-phenotype relationship in Bardet Biedl Syndrome.</p> <p>File "EV table 1 literature.xlsx" describes studies that were included in the metaanalysis. File "EV table 2 dataset.xlsx" contains individual patient data. Each row corresponds to a patient. If the same patient was reported in more than 1 study, their data were merged into one row. The columns are as follows:</p> <p>* source - a citation to the study the patient originated in</p> <p>* FamilyID - randomly generated ID of a family (unique over the dataset), two persons with the same FamilyID are related.</p> <p>* source case n. - A unique identifier of the patient within the study</p> <p>* gene - A gene carrying the principal BBSome related mutation</p> <p>* nucleotide change (allele 1,2) - description of the mutations in DNA individual alleles of the gene, in HGVS nomenclature</p> <p>* protein change (allele 1,2) - description of how the mutations in DNA change the resulting protein, in HGVS nomenclature</p> <p>* type of mut allele 1,2 - whether the given mutation is considered missense (MS) or large truncation (trunc)</p> <p>* mut/mut - combination of mutations for both alleles</p> <p>* additional mutations - mutations in other BBSome-related genes. Format is "gene: DNA mutation, protein mutation"</p> <p>* sex - "F" or "M" (where reported)</p> <p>* age group - age group (where reported)</p> <p>* age - age in years. Contains fractions, decimal values and "5 month"</p> <p>* RD, OBE, PD, CI, REP, REN, HEART, LIV, DD - presense or absence of phenotypes, if reported. RD – retinal dystrophy, OBE – obesity, PD – polydactyly, CI – cognitive impairment , REP – reproductive system anomalies, REN – renal anomalies, HRT – heart disease, LIV – liver anomalies, DD - Developmental delay. Values are "" (not reported), "0" (no phenotype), "1" (phenotype present), "1!" conflicting reports of phenotype in multiple studies (some patients were involved in multiple studies)</p> <p>* ethnicity - ethnicity of the patient, if reported</p> <p>* ethinc group - grouping of the ethnicities into 8 larger groups (see paper for details)</p> <p>* note - miscellanous text, in particular contains notes on patients merged from multiple studies</p> <p>====</p> <p>The protocol for this meta-analysis was pre-registered with PROSPERO (CRD42018096099).</p> <p>PubMed and Google Scholar databases were searched in May 2018 for the following keywords: [bardet-biedl syndrome AND (genotype phenotype OR cohort)]. Other suitable records were identified by snowball searching, in particular, by retrieving relevant articles from the references of the studied full-texts. In addition, all the references included in the publicly available Euro-Wabb database (<a href="https://lovd.euro-wabb.org/home.php">https://lovd.euro-wabb.org/home.php</a>) were covered. Our search was limited to the literature published in English language and covered the period from the inception of each database to the 21st of May 2018.</p>
Datset related to article "G507D MUTATION IN FUS GENE CAUSES FAMILIAL AMYOTROPHIC LATERAL SCLEROSIS WITH A SPECIFIC GENOTYPE-PHENOTYPE CORRELATION"
<p><strong>NGS_analysis performed at Fondazione Besta carried out as part of the study reported at title</strong></p>
Dataset related to article"G507D MUTATION IN FUS GENE CAUSES FAMILIAL AMYOTROPHIC LATERAL SCLEROSIS WITH A SPECIFIC GENOTYPE-PHENOTYPE CORRELATION"
<p>NGS Analyses of patients included in the study reported at title followed at Fondazione Besta</p>
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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data set related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study
<p>This record contains raw data related to article Broad phenotypic spectrum and genotype-phenotype correlations in GMPPB-related dystroglycanopathies: an Italian cross-sectional study</p>
Supplemental material for the paper "Genotype-phenotype correlation, clinical presentation, and feminizing surgery in children with congenital adrenal hyperplasia - a nationwide study"
<p>This dataset provides documentation for the translation between historical nomenclature for variants in the CYP21A2 gene to the Human genome variation society nomenclature. </p> <p>Furthermore, it provides data on all Danish CYP21A2 variants included in the study and the translation into genotype groups: Null, A, B, C, and D. </p>
Harnessing genotype-phenotype nonlinearity to accelerate biological prediction
<p><strong>Description:</strong></p> <p>We are sharing phenotypic data sets used in the pub <a href="http://doi.org/10.57844/arcadia-5953-995f">“Harnessing genotype-phenotype nonlinearity to accelerate biological prediction”</a>. Contained within are a set of publicly available empirical phenotype data sets, synthetic phenotypes we generated, and the output of an autoencoder model trained on them.</p> <p>Walkthroughs for all analyses using these data are available on <a href="http://doi.org/10.5281/zenodo.8371249">GitHub</a>.</p> <p><strong>Files: </strong></p> <ul> <li><strong>“ail_cleaned_phenos.RDS”:</strong> .RDS file of mouse phenotypes from Bogue et al. 2015</li> <li><strong>“all_autoencoder_phenotype_predictions.csv“:</strong> .csv file containing accuracy statistics for all phenotype-phenotype autoencoder models analyzed in the pub</li> <li><strong>“arapheno_cleaned_phenos.RDS“:</strong> .RDS file of Arabidopsis phenotypes from Exposito-Alonso et al. 2019</li> <li><strong>“autoencoder_phenos.zip“:</strong> directory of synthetic phenotype sets used to train the autoencoder models</li> <li><strong>“dgrp_cleaned_phenos.RDS”:</strong> .RDS file of DGRP fruit fly phenotypes compiled from multiple sources, originally reported in Mackay et al. 2012</li> <li><strong>“jax_cleaned_phenos.RDS”:</strong> .RDS file of mouse phenotypes from Gonzales et al. 2018</li> <li><strong>“nematode_cleaned_phenos.RDS”:</strong> .RDS file of nematode phenotypes from Snoek et al. 2019</li> <li><strong>“phen_pleio_int_01_0_1.pk”:</strong> pickle of all synthetic phenotypes analyzed in the pub</li> <li><strong>“yeast_cleaned_phenos.RDS”:</strong> .RDS file of yeast phenotypes from Bloom et al. 2019</li> </ul> <p>A full description of how the synthetic phenotypes and autoencoder predictions were generated is available in the associated <a href="http://doi.org/10.57844/arcadia-5953-995f">pub</a>.</p>
Incremental integration for tracking genotype-phenotype associations - dataset snapshot
<p>This dataset is an archive of data and code files used to generate figures and tables for a manuscript entitled "Incremental integration for tracking genotype-phenotype associations".</p> <p>Data files include the exact versions of raw materials as well as databases produced during the course of the analysis described in the manuscript. </p> <p>Code files include python and R components.</p> <p>Further details are available in the archive README file.</p>
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>
Data from: Detecting macroevolutionary genotype-phenotype associations using error-corrected rates of protein convergence
<p><span>On macroevolutionary timescales, extensive mutations and phylogenetic uncertainty mask the signals of genotype-phenotype associations underlying convergent evolution. To overcome this problem, we extended the widely used framework of nonsynonymous-to-synonymous substitution rate ratios and developed the novel metric <em>ω<sub>C</sub></em>, which measures the error-corrected convergence rate of protein evolution. While </span><span><em>ω<sub>C</sub></em></span><span> distinguishes natural selection from genetic noise and phylogenetic errors in simulation and real examples, its accuracy allows an exploratory genome-wide search of adaptive molecular convergence without phenotypic hypothesis or candidate genes. Using gene expression data, we explored over 20 million branch combinations in vertebrate genes and identified the joint convergence of expression patterns and protein sequences with amino acid substitutions in functionally important sites, providing hypotheses on undiscovered phenotypes. We further extended our method with a heuristic algorithm to detect highly repetitive convergence among computationally nontrivial higher-order phylogenetic combinations. Our approach allows bidirectional searches for genotype-phenotype associations, even in lineages that diverged for hundreds of millions of years.</span></p>
Creating a 7,000 strains genotype-phenotype dataset of E. coli and antimicrobial resistance phenotypes
<h1>Description</h1> <p>This Zenodo repository contains the data (except for the input fastq files available on SRA and intermediary files generated during the variant calling process) and code to recapitulate the study from <a href="https://doi.org/10.57844/arcadia-d2cf-ebe5">https://doi.org/10.57844/arcadia-d2cf-ebe5</a> and the associated GitHub repository, where the code, pipelines, and analysis are described in more detail.</p> <p> </p> <h1>Work summary</h1> <p>In this work, we established a framework for compiling large genotype-phenotype datasets and produced a large-scale dataset of more than 7,000 <em>E. coli</em> strains and antimicrobial resistance phenotypes.</p> <p>We leveraged the genetic information and antimicrobial resistance (AMR) phenotype data available for the bacterium <em>Escherichia coli</em> to construct our dataset and took advantage of the existing knowledge about genetic variations and AMR phenotypes to validate our approach and dataset. We performed variant calling and compiled a genotype-phenotype dataset for more than 7,000 <em>E. coli</em> strains. Briefly, variant calling consists of identifying all genetic variations and their associated genotypes in a population compared to a reference genome. This is performed by aligning sequencing reads for each strain of the population against a reference genome, then identifying polymorphic regions in the population, and finally characterizing variants and their genotypes at each of these polymorphic regions.</p> <p>We have generated a dataset that successfully revealed significant genetic diversity and identified 2.4 million variants. By focusing on non-silent variants within genes associated with AMR, we confirmed the dataset's accuracy. </p> <p>We hope this study is a foundational resource for conducting large-scale genotype-phenotype studies that will offer valuable insights for genetics investigations, informing the development of treatments and prevention strategies for AMR. This resource is invaluable for microbiologists and epidemiologists seeking to understand AMR mechanisms and improve genotype-phenotype predictions in pathogenic <em>E. coli</em> outbreaks. Additionally, it's of particular interest to geneticists and evolutionary biologists, providing a dataset to develop strategies for studying genetic interactions and broader applications in phenotype-phenotype predictions and phylogenetic research.</p> <h1>Data organization</h1> <p>Data are organized in the compressed folder. Briefly, they’re divided into two main folders.</p> <p>The first folder, <strong>dataset_generation</strong>, includes the code and information necessary to build the genotype dataset and perform the variant calling. It covers major steps like the generation of the reference pangenome used for variant calling, the variant calling pipeline applied to each of the 7,000 strains, the filtering of false positive variants, and the annotation of the variants. </p> <p>The second section, <strong>dataset_analysis</strong>, includes the code and information used to process and analyze the dataset and generate figures for the Pub (https://doi.org/10.57844/arcadia-d2cf-ebe5). It includes the preliminary analysis of AMR phenotypes within the population and the analysis of variants regarding known AMR phenotypes.</p> <p> </p> <h1>Files description</h1> <p>The following table provides a list and description of the different files and their locations.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Location</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>variant_calling_pipeline</td> <td>dataset_generation/scripts/</td> <td>Snakefile: performs variant calling from raw paired-end sequencing files and generate one vcf.gz file per sample</td> </tr> <tr> <td>snakemake_ECOR72_annotation</td> <td>Snakefile: performs Prokka annotation on inputs whole genome fastq files</td> </tr> <tr> <td>ECOR72_and_DP_threshold_analysis.Rmd</td> <td>R markdown: analyses the coverage of known present and absebt loci in the ECOR population</td> </tr> <tr> <td>average_coverage_41.csv</td> <td>dataset_generation/data/dp_threshold/</td> <td>Pangenome loci read coverage information for 40 ECOR strains</td> </tr> <tr> <td>average_coverage_last32.csv</td> <td>Pangenome loci read coverage information for 32 ECOR strains</td> </tr> <tr> <td>whole_pan_ecor_presence_absence.csv</td> <td>Reformated pangenome loci presence-absence in ECOR strains</td> </tr> <tr> <td>pangenome_genomes_SRA_GCA.csv</td> <td>Correspondance table between ECOR72 strains genome names and raw sequencing files SRA accession number</td> </tr> <tr> <td>index_loci_pangenome_good.txt</td> <td>List of indexed positions in the pangenome</td> </tr> <tr> <td>list_ecor_txtfiles.txt</td> <td>List of txt files (containing the DP information per nucleotide) to use - This corresponds to the files for each 72 ECOR strains</td> </tr> <tr> <td>ECOR72_SRA_and_assembly_accessions.csv</td> <td>dataset_generation/data/</td> <td>List of Genome accession number and the SRA accession number of the associated sequencing files for the 72 ECOR strains</td> </tr> <tr> <td>sample_list_SRA.csv</td> <td>List of SRA accession numbers of the E. coli strains used for variant calling</td> </tr> <tr> <td>gene_presence_absence.csv</td> <td>dataset_generation/results/pangenome_cds/</td> <td>Roary output of presence-absence of the pangenome cds loci in the ECOR72 strains</td> </tr> <tr> <td>genes.gff</td> <td>Annotation file of the pangenome cds sequences (Prokka output)</td> </tr> <tr> <td>pangenome_cds.fa</td> <td>Roary output cds_pangenome sequencing file</td> </tr> <tr> <td>summary_statistics.txt</td> <td>Roary statistics output of creations of the cds pangenome</td> </tr> <tr> <td>roary_output</td> <td>Roary output folder</td> </tr> <tr> <td>IGR_presence_absence.csv</td> <td>dataset_generation/results/pangenome_igr/</td> <td>Piggy output of presence-absence of the pangenome igr loci in the ECOR72 strains</td> </tr> <tr> <td>pangenome_igr.fasta</td> <td>Piggy output igr_pangenome sequencing file</td> </tr> <tr> <td>piggy_output</td> <td>Piggy output folder</td> </tr> <tr> <td>whole_pangenome.fasta</td> <td>dataset_generation/results/pangenome_whole/</td> <td>whole pangenome sequences</td> </tr> <tr> <td>annot_summary_filtered.html</td> <td>dataset_generation/results/vcf/</td> <td>Summary of snpEff annotations</td> </tr> <tr> <td>annotated_output.vcf.gz</td> <td>snpEff annotated vcf file</td> </tr> <tr> <td>annotated_output.vcf.gz.csi</td> <td>indexed annotated vcf file</td> </tr> <tr> <td>filtered_output.vcf.gz</td> <td>filtered vcf file (removed low coveraged and low quality variants)</td> </tr> <tr> <td>filtered_output.vcf.gz.csi</td> <td>indexed filtered vcf files</td> </tr> <tr> <td>output.non_silent.vcf.gz</td> <td>vcf file containing only the nonsilent variants in the pangenome cds loci</td> </tr> <tr> <td>merged_output_listN.vcg.gz</td> <td>intermediary vcf files of 1000 merged strains vcf - these intermediary merged files are numbered from 1 to 7</td> </tr> <tr> <td>merged_output_all.vcf.gz</td> <td>final vcf.gz files of all merged vcf files in this study</td> </tr> <tr> <td>List_N_merging.txt</td> <td>dataset_generation/data/vcf_merging/</td> <td>List of the 1000 vcf.gz files to be merged together. There are 7 lists, numbered from 1 to 7</td> </tr> <tr> <td>ecor72_array.txt</td> <td>dataset_generation/results/ecor72_DP/</td> <td>Consolidate DP information per nucleotide for each ECOR strain</td> </tr> <tr> <td>variants_pos.tsv</td> <td>dataset_analysis/data/variant_analysis/</td> <td>List of all the variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_freqs.txt</td> <td>Variant frequency informations</td> </tr> <tr> <td>variants_non_silent_pos.tsv</td> <td>List of all the non-silent variants found in the population and identified by their locus and position within the locus</td> </tr> <tr> <td>allele_non_silent_freqs.txt</td> <td>Non-silent variant frequency informations</td> </tr> <tr> <td>cds_eggNog.tsv</td> <td>eggNog output file of the pangenome annotation</td> </tr> <tr> <td>COG_functional_categories.csv</td> <td>Correspondance between COG functional categories and higher-order annotation</td> </tr> <tr> <td>BVBRC_genome_May31.csv</td> <td>dataset_analysis/data/dataset_analysis</td> <td>List of E. coli with available genomes as reported in BCBRV database</td> </tr> <tr> <td>BVBRC_genome_amr_May31.csv</td> <td>E. coli antimicrobial resistance information available in BCBRV database</td> </tr> <tr> <td>antibiotic_class.csv</td> <td>Antibiotic name and Antibiotic class information</td> </tr> <tr> <td>resistance_output.non_silent.vcf.gz</td> <td>dataset_analysis/data/antimicrobial_resistance_analysis</td> <td>vcf.gz file of the loci expected to be associated with antimicrobial resistance</td> </tr> <tr> <td>antibiotic_resistance_freq.csv</td> <td>Frequency information for the non-silent variant in the selected antimicrobial genes</td> </tr> <tr> <td>SRA_to_genome_name.csv</td> <td>correspondence between strain SRA accession number and genome name (as reported in BVBRC)</td> </tr> <tr> <td>Dataset_metainfo_AMR_analysis.Rmd</td> <td>dataset_analysis/scripts</td> <td>R markdown: conducts the characterization of the population and analysis of the AMR phenotype distribution</td> </tr> <tr> <td>Variant_population_analysis.Rmd</td> <td>R markdown: conducts the analysis and investigation of identified variants in the population</td> </tr> <tr> <td>Antimicrobial_resistance_investigation.Rmd</td> <td>R markdown: conducts the antimicrobial resistance investigation </td> </tr> </tbody> </table> <p> </p> <p> </p>
Genotype-phenotype associations in CRB1 bi-allelic patients: a novel mutation and a systematic review
<p><span><strong>Purpose</strong>: Searched for novel bi-allelic <em>CRB1</em> mutations, then analyzed the <em>CRB1</em> literature at the genotypic and phenotypic levels from 439 patients worldwide.</span></p> <p><span><strong>Approach</strong>: We screened various variables such as the <em>CRB1</em> mutation types, domains, exons, and genotypes and their relation with specific ocular phenotypes. An emphasis was given for the bi-allelic missense and nonsense mutations because of their high prevalence compared to other mutation types. Finally, we quantified the effect of various non-modifiable factors over the BCVA OU using multivariate linear regression models and identified genetic interactions.</span></p> <p><span><strong>Results</strong>: We first identified a novel bi-allelic missense in the exon 9 of <em>CRB1</em>; c.2936G>A; p.(Gly979Asp) associated with RCD. <em>CRB1</em> mutation type, exons, domains, and genotype distribution vary significantly according to Fundus characteristics, such as peripheral pigmentation and condition, optic disc, vessels, macular, and pigmentation (P<0.05). Of the 154 articles retrieved from PubMed, 96 studies with 439 bi-allelic <em>CRB1</em> patients were included. </span><span>Missense mutations were significantly associated with an absence of macular pigments, pale optic disc, and periphery pigmentation, resulting in a higher risk of RCD (P<0.05). In contrast, homozygous nonsense mutations were associated with macular pigments, periphery pigments, and a high risk of LCA (P<0.05) and increased BCVA OU (best-corrected visual acuity) levels.</span><span> We found that age, mutation types, and inherited retinal diseases were critical determinants of BCVA OU as they significantly increased it by 33%, 26%, and 38%, respectively (P<0.05). Loss of function alleles additively increased the risk of LCA, with nonsense having a more profound effect than indels. Finally, our analysis showed that p.(Cys948Tyr) and p.(Lys801Ter) and p.(Lys801Ter); p.(Cys896Ter) might interact to modify BCVA OU levels. </span></p> <p><span><strong>Conclusion</strong>: This meta-analysis updated the literature and identified genotype-phenotype associations in bi-allelic <em>CRB1</em> patients.</span></p>
Genotype-phenotype Correlation in Junctional Epidermolysis Bullosa
ClinicalTrials.gov study NCT04727268. IPD Sharing: YES. Countries: 1. Publications: 2.
Genotype-Phenotype Study of Patients With Plaquenil -Induced Retinal Toxicity, With Evaluation of the ABCA4 Gene
ClinicalTrials.gov study NCT01145196. IPD Sharing: NO. Countries: 1. Publications: 3.
Genotype-phenotype Association in Hereditary Hemorrhagic Telangiectasia
ClinicalTrials.gov study NCT05550376. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Genotype-phenotype Correlation Study of Presymptomatic and Symptomatic DFNA9 Patients
ClinicalTrials.gov study NCT03716908. IPD Sharing: NO. Countries: 1. Publications: 2.
Genotype-Phenotype Associations in Pediatric Cardiomyopathy (PCM GENES)
ClinicalTrials.gov study NCT01873963. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Data from: Is evolution predictable? quantitative genetics under complex genotype-phenotype maps
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