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opencc-by-4.0May 2024View details →

Code and Data for manuscript "Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation processes"

Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation -------- This repository contains all the R code used in the manuscript: * Title: "Floral phenotypic divergence and genomic insights in an Ophrys orchid: Unraveling early speciation" * Authors: Anais Gibert, Schatz Bertrand, Buscail Roselyn, Dominique Nguyen, Baguette Michel, Bartes Nicolas and Joris Bertrand * Year of publication: 2024 * doi: https://doi.org/10.1101/2024.03.21.586062   Synopsis of the study -------- <ul> <li> Adaptive radiation in Ophrys orchids leads to complex floral phenotypes that vary in scent, color and shape. </li> <li> Using a novel pipeline to quantify these phenotypes, we investigated trait divergence at early stages of speciation in six populations of Ophrys aveyronensis experiencing recent allopatry. By integrating different genetic/genomic techniques, we investigated: (i) variation and integration of floral components (scent, color and shape), (ii) phenotypes and genomic regions under divergent selection, and (iii) the genomic bases of trait variation. </li> <li> We identified a large genomic island of divergence, associated with phenotypic variation in particular in floral odor. We detected potential divergent selection on macular color, while convergent selection was suspected on floral morphology and for several volatile olfactive compounds. We also identify candidate genes involved in anthocyanin and in steroid biosynthesis pathways associated with standing genetic variation in color and odor. </li> <li> This study sheds light on early differentiation in Ophrys, revealing patterns that often become invisible over time, i.e., the geographic mosaic of traits under selection and the early appearance of strong genomic divergence. It also supports a crucial genomic region for future investigation and highlights the value of a multifaceted approach in unraveling speciation within taxa with large genomes. </li> </ul>   Running the code -------- Here we present the data and code for carrying out the analyses, as well as the figures and tables from the article and the supplementary material. Once you have installed the necessary packages, run the commands in 'analysis_share.R'. This script uses several functions available in the '/R' directory. Figures and tables are produced in a 'manuscript/figures' and 'manuscript/tables' directory.  The `/data' directory contains the data used in the analyses (data/input or data/output), but also the resulting datasets produced by the code (data/RData/).

opencc-by-4.0Dec 2024View details →

GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank (5-fold cross-validation)

This dataset contains GWAS summary statistics for 9 quantitative phenotypes from the UK Biobank. The dataset is designed to enable systematic PRS analyses with 5-fold cross validation. For each phenotype and fold, we provide GWAS summary statistics for the training, validation, and test sets. The validation summary statistics can be used for model selection/tuning. The test summary statistics can be used to evaluate PRS models via pseudo-validation metrics. Association testing for all phenotypes and samples was done with plink2.   The phenotypes included in this dataset are: <ul> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=50">HEIGHT</a>: Standing height (Data-Field: 50)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=21001">BMI</a>: Body mass index (Data-Field: 21001)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=48">WC</a>: Waist circumference (Data-Field: 48)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=49">HC</a>: Hip circumference (Data-Field: 49)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=20022">BW</a>: Birth weight (Data-Field: 20022)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3062">FVC</a>: Forced vital capacity (Data-Field: 3062)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=3063">FEV1</a>: Forced expiratory volume in 1-second (Data-Field: 3063)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30760">HDL</a>: HDL cholesterol (Data-Field: 30760)</li> <li><a href="https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30780">LDL</a>: LDL cholesterol (Data-Field: 30780)</li> </ul>   To allow users to assess PRS performance as a function of sample size, we also provide subsampled training GWAS summary statistics. This is done by taking the training samples and randomly selecting (without replacement) a subset of them for conducting association testing. The training sample sizes are: <ul> <li>N = 5000</li> <li>N = 10000</li> <li>N = 20000</li> <li>N = 40000</li> <li>N = 80000</li> <li>N = 160000</li> <li>Full training set (sample size varies by phenotype).</li> </ul> NOTE: Due to the smaller overall sample size for the Birth weight phenotype, we do not include training data for the `N=160000` setting. The folder structure of the GWAS data for each phenotype is as follows: <ul> <li><code>train</code> <ul> <li><code>N_5000</code> <ul> <li><code> fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>N_10000</code></li> <li><code>N_20000</code></li> <li><code>N_40000</code></li> <li><code>N_80000</code></li> <li><code>N_160000</code></li> <li><code>full</code></li> </ul> </li> <li><code>validation</code> <ul> <li><code>fold_1</code> <ul> <li><code>chr_1.PHENO1.glm.linear</code></li> <li><code>chr_2.PHENO1.glm.linear</code></li> <li><code>...</code></li> </ul> </li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> <li><code>test</code> <ul> <li><code>fold_1</code></li> <li><code>fold_2</code></li> <li><code>fold_3</code></li> <li><code>...</code></li> </ul> </li> </ul> For more details about the GWAS study, Quality Control (QC) criteria, or other information, please consult our publication: Zabad, S., Gravel, S., & Li, Y. (2023). Fast and accurate Bayesian polygenic risk modeling with variational inference. The American Journal of Human Genetics, 110(5), 741–761. <a href="https://doi.org/10.1016/j.ajhg.2023.03.009" rel="nofollow">https://doi.org/10.1016/j.ajhg.2023.03.009</a> If you use this data in your work, please cite the publication above.

opencc-zeroNov 2021View details →

Phenological data for the article: Phenotypic plasticity drives phenological changes in a Mediterranean blue tit population

Earlier phenology induced by climate change, such as the passerines' breeding time, is observed in many natural populations. Understanding the nature of such changes is key to predict the responses of wild populations to climate change. Genetic changes have been rarely investigated for laying date, though it has been shown to be heritable and under directional selection, suggesting that the trait could evolve. In a Corsican blue tit population, the birds' laying date has significantly advanced over 40 years, and we here determine whether this response is of plastic or evolutionary origin, by comparing the predictions of the breeder's and the Robertson-Price (STS) equations, to the observed genetic changes. We compare the results obtained for two fitness proxies (fledgling and recruitment success), using models accounting for their zero-inflation. Because the trait appears heritable and under directional selection, the breeder's equation predicts that genetic changes could drive a significant part of the phenological change observed. We however found that fitness proxies and laying date are not genetically correlated. The STS therefore predicts no evolution of the breeding time, predicting correctly the absence of trend in breeding values. Our results also emphasize that when investigating selection on a plastic trait under fluctuating selection, part of the fitness-trait phenotypic covariance can be due to within individual covariance. In the case of repeated measurements, splitting within and between individual covariance can shift our perspective on the actual intensity of selection over multiple selection episodes, shedding light on the potential for the trait to evolve.

opencc-zeroNov 2021View details →

Association of gray matter atrophy patterns with clinical phenotype and progression in multiple sclerosis

Objectives. Grey matter (GM) involvement is clinically relevant in multiple sclerosis (MS). Using source-based morphometry (SBM), we characterized GM atrophy and its 1-year evolution across different MS phenotypes. Methods. Clinical and MRI data were obtained at 8 European sites from 170 healthy controls (HCs) and 398 MS patients (34 clinically isolated syndromes [CIS], 226 relapsing-remitting [RR], 95 secondary progressive [SP] and 43 primary progressive [PP] MS). Fifty-seven HC and 144 MS underwent 1-year follow-up. Baseline GM loss, atrophy progression and correlations with disability and 1-year clinical worsening were assessed. Results. SBM identified 26 cerebellar, subcortical, sensory, motor and cognitive GM components. GM atrophy was found in MS vs HC in almost all components (p=range<0.001-0.04). Compared to HCs, CIS patients showed circumscribed subcortical, cerebellar, temporal and salience GM atrophy, while RRMS patients exhibited widespread GM atrophy. Cerebellar, subcortical, sensorimotor, salience and fronto-parietal GM atrophy was found in PPMS patients vs HCs, and SPMS vs RRMS. At 1-year, 21 (15%) patients had clinically worsened. GM atrophy progressed in MS in subcortical, cerebellar, sensorimotor, and fronto-temporo-parietal components. Baseline higher disability was associated (R2=0.65) with baseline lower normalized brain volume (beta=-0.13, p=0.001), greater sensorimotor GM atrophy (beta=-0.12, p=0.002) and longer disease duration (beta=0.09, p=0.04). Baseline normalized GM volume (odds ratio=0.98, p=0.008) and cerebellar GM atrophy (odds ratio=0.40, p=0.01) independently predicted clinical worsening (area-under-the-curve=0.83). Conclusion. GM atrophy differed across disease phenotypes and progressed at 1-year in MS. In addition to global atrophy measures, sensorimotor and cerebellar GM atrophy explained baseline disability and clinical worsening.

opencc-zeroNov 2021View details →

Genomic basis for skin phenotype and cold adaptation in the extinct Steller's sea cow

Steller's sea cow, an extinct sirenian and one of the largest Quaternary mammals, was described by Georg Steller in 1741 and eradicated by humans within 27 years. Here, we complement Steller's descriptions with paleogenomic data from 12 individuals. We identified convergent evolution between Steller's sea cow and cetaceans but not extant sirenians, suggesting a role of several genes in adaptation to cold environments. Among these are inactivations of lipoxygenase genes, which in humans and mouse models cause ichthyosis – a skin disease characterized by a thick, hyperkeratotic epidermis that recapitulates Steller's sea cows' reportedly bark-like skin. Finally, we found that Steller's sea cows' abundance was continuously declining for tens of thousands of years prior to their description, implying that environmental changes also contributed to their extinction.

opencc-by-4.0Jul 2020View details →

PheneBank: Processed Medline Abstracts and PMC full articles + Phenotype-Disease Associations

The PheneBank project: Free text scientific literature has the potential to be an incredibly valuable source of data for uncovering the often hidden relationships between genes, diseases and phenotypes. Phenotypic descriptions cover abnormalities in anatomical structures, processes and behaviours. For example 'growth delay' and 'body weight loss'. Such descriptions form the basis for determining the existence and treatment of a disease but, because of their inherent complexity, have previously received less attention by the text mining community. In recent years, significant effort has been spent by a small number of expert curators to create coding systems for phenotypes (called "ontologies"), such as the Human Phenotype Ontology (HP) and the Mammalian Phenotype Ontology (MP). The PheneBank project proposes to support and speed up curation using terms discovered directly from the literature and to automatically integrate them with such standard ontologies.  The project seeks to harness texts for extracting statistically significant associations between phenotypes, diseases and genes. Earlier approaches have suffered from not providing deep semantic representations of the phenotypes they tried to target. Our deep learning-based approach is an attempt to overcome this issue by reducing the uncertainty between textual and ontological forms of phenotypes. Specifically, the model treats multitoken named entities as a single token which allows more reliable handling of multiword expressions. The approach builds on ground breaking research at the European Bininformatics Institute by the PI (Nigel Collier) and  the Co-investigator (Damian Smedley, Queen Mary University London), including terminology alignment of phenotypes using pairwise scoring of the conceptual elements that make up the phenotype.  <a href="http://www.phenebank.org">http://www.phenebank.org</a> The dataset: As an output of the PheneBank project, we release the set of 24 million MEDLINE abstracts as well as 3.8M open-access PMC full articles annotated with 9 classes of entity: Phenotype, Disease, Anatomy, Cell, Cell_line, GPR, Gene_variant, Molecule, and Pathway. The entities have been mapped to five major ontologies: SNOMED, HPO, MeSH, PRO, and FMA. In addition, we release the phenotype-disease associations that are automatically extracted based on co-occurrences statistics in Medline abstracts. Among different statistical measures we evaluated, the Fisher test best corresponded to the known tuples available from the curated associations available from the Monarch Initiative (https://monarchinitiative.org). Processing: The NER tagging has been done using a BiLSTM-CRF neural model (<a href="https://github.com/pilehvar/phenebank">https://github.com/pilehvar/phenebank</a>) trained on expert-annotated data (to be released for research). The grounding to ontologies relies on semantic embedding of concepts and entities in a unified semantic space. Data format: PheneBank_Processed_PubMed.part[x].tar.gz contains 24,359,010 .txt files that are classified into 812 directories. Each .txt file is named with a PubMed article ID and contains the corresponding article's abstract and its annotations. The dataset is split into four (unequal) parts based on PubMed's structure: part1: medline16n00* medline16n01* medline16n02* [299 directories,  2.8GB] part2: medline16n03* medline16n04* [200 directories, 4.7GB] part3: medline16n05* medline16n06* [200 directories, 5.3GB] part4: medline16n07* medline16n08* [113 directories, 3.1GB] The PheneBank_Processed_PMC.tar.gz files has 6,180 directories which are named after the journal titles from which the articles have been drawn. There are three files per each article (i.e., 3 .txt files for the 3,751,770 distinct articles), containing text from different parts of the article: .title.txt, .abstract.txt, and .body.txt.  Each line starts with a word; for those words that are identified as entities, entity type and mapping information are followed in the same line (tab separated), with the following format: word <TAB> ::: <TAB> entity_type <TAB> entity_concept_ID_1##confidence_score_1 entity_concept_ID_2##confidence_score_2 ... Note that the concepts are sorted according to their mapping confidence scores. As for the PheneBank_Associations.tsv file, there are ten columns that correspond to the following (left to right): - Disease Name - Disease (MONDO) ID - Phenotype Name - Phenotype (HPO) ID - Co-occurrence Frequency - Disease Frequency - Phenotype Frequency - Fisher (log) - Dice - Normalized PMI

opencc-zeroDec 2021View details →

Data from: Genomic region associated with run-timing has similar haplotypes and phenotypic effects across three lineages of Chinook salmon

Conserving life history variation is a stated goal of many management programs, but the most effective means by which to accomplish this are often far from clear. Early, premature migrating and late, mature migrating forms of Chinook salmon face unequal pressure from natural and anthropogenic forces. These forces may result in the diminishment of migration variation in some stocks because migration timing is known to be highly heritable. Genomic regions of chromosome 28 are known to be strongly associated with migration variation in adult Chinook salmon, but it remains unclear whether there is consistent association among the diverse populations of Chinook salmon. Therefore, application of this association for management may be premature. We examined the association of genetic variation in 28 markers on chromosome 28 surveyed with high-throughout genotyping (GT-seq) with individual run timing characteristics gleaned from passive integrated transponder recordings of over 5,000 Chinook salmon from the three phylogeographic lineages that inhabit the Columbia River Basin. Despite the strong genetic differences among them, the three lineages exhibited very similar genetic variants in the chromosome 28 region and moderate to strong association of these variants and run timing phenotypes. This is particularly notable for the interior stream-type lineage, which exhibits an earlier and more constrained migration of fish than the other lineages and which are exclusively premature when they enter freshwater. In both interior stream-type and interior-ocean type Chinook salmon, heterozygotes of the most strongly associated linkage groups are largely intermediate to homozygotes in migration timing, and while we make no robust conclusions about dominance, results indicate codominance or marginal partial dominance of the early migrating allele. Our results lend support to the cautious utilization of chromosome 28 variation in tracking and predicting run timing in these Chinook salmon.

FIGURE 5 in True branching and phenotypic plasticity in the planktonic cyanobacterium Dolichospermum brachiatum sp. nov. (Nostocales, Aphanizomenonaceae), from south-eastern Australia

FIGURE 5. Phylogenetic tree based on partial 16S ribosomal RNA gene sequence (1518 base pairs) of 128 OTUs showing the phylogenetic position of Dolichospermum brachiatum. Numbers indicate bootstrap values (> 50%) from 1000 replicates of ML and NJ analyses respectively. GenBank accession numbers are shown in parentheses. Scale bar = 0.01 nucleotide substitutions per site.

FIGURE 2 in True branching and phenotypic plasticity in the planktonic cyanobacterium Dolichospermum brachiatum sp. nov. (Nostocales, Aphanizomenonaceae), from south-eastern Australia

FIGURE 2. Sequence of development of T-type true branching of Dolichospermum brachiatum from Waranga Basin. Scale bars = 20 μm.

FIGURE 7 in True branching and phenotypic plasticity in the planktonic cyanobacterium Dolichospermum brachiatum sp. nov. (Nostocales, Aphanizomenonaceae), from south-eastern Australia

FIGURE 7. Secondary structure of the ITS sequence in D. brachiatum and allied species. Circles and oblongs highlight the differences in the structures; (A–D) D1-D1′ helix (A) D. brachiatum strains WB20619.B1, WB20619.B3, WB20619.C1 and WB20619.C2, (B) D. planctonicum strains 1-3; 19-1; 23-10; NRERC-101; D. ucrainicum CHAB623, (C) D. affinis CHAB28, (D) D. lemmermanni BC Ana 0032. (E–H) Box B helix (E) D. brachiatum strains WB20619.B1, WB20619.B3, WB20619.C1 and WB20619.C2, (F) D. planctonicum strains 1-3; 19-1; 23-10; NRERC-101; D. ucrainicum CHAB623, (G) D. affinis CHAB28 and CHAB964; D. flos-aquae CHAB1652 and NIES 1669, (H) D. lemmermannii BC Ana 0032. (I–M) V3 helix (I) D. brachiatum strains WB20619.B1, WB20619.B3, WB20619.C1 and WB20619.C2, (J) D. planctonicum strain 1-3; 1-9; 19-1; 23-10; NRERC-101, (K) D. affinis CHAB28; D. flos-aquae CHAB1652 and NIES 1669, (L) D. ucrainicum CHAB623, (M) D. lemmermannii BC Ana 0032.

FIGURE 6 in True branching and phenotypic plasticity in the planktonic cyanobacterium Dolichospermum brachiatum sp. nov. (Nostocales, Aphanizomenonaceae), from south-eastern Australia

FIGURE 6. Phylogenetic tree based on the ITS sequence of the 16S–23S rRNA operon of 25 OTUs showing the phylogenetic position of Dolichospermum brachiatum. Numbers indicate bootstrap values> 50% from 1000 replicates of ML and NJ analyses respectively. GenBank accession numbers shown in parentheses. Scale bar = 0.05 nucleotide substitutions per site. Strains isolated and sequenced in this study shown in bold.

Figure 6 in Molecular and phenotypic data reveal a new Amazonian species of pit vipers (Serpentes: Viperidae: Bothrops)

Figure 6. Geographic map of South America showing the distribution of Bothrops oligobalius sp. nov. (yellow) and B. brazili (red). Outlined diamonds and circles represent sites with genetic samples available. Diamonds with an inner dot indicate the type locality of each both species. The Amazonian biome is highlighted in green; the Amazon River is also indicated. Photos: B. oligobalius sp. nov. from French Guiana, by Maël Dewynter; B. brazili from Porto Velho, RO, Brazil, by FDV.

Figure 3 in Molecular and phenotypic data reveal a new Amazonian species of pit vipers (Serpentes: Viperidae: Bothrops)

Figure 3. Holotype of Bothrops oligobalius sp. nov. (MZUSP 23282, field number MTR 13844), from Lourenço Municipality, State of Amapá, Brazil. A: Dorsal view; B: Ventral view. Scale bar = 1 cm.

Figure 8 in Molecular and phenotypic data reveal a new Amazonian species of pit vipers (Serpentes: Viperidae: Bothrops)

Figure 8. Bothrops brazili in dorsal view. A- DHMECN3035 from Ecuador B- QCAZ4827 from Pastaza, Ecuador. Scale bar = 1 cm.

Figure 4 in Molecular and phenotypic data reveal a new Amazonian species of pit vipers (Serpentes: Viperidae: Bothrops)

Figure 4. Head of the holotype (MZUSP 23282) of Bothrops oligobalius sp. nov. in (a) lateral, (b) dorsal and (c) ventral views. Scale bar 1 cm.

Figure 13. Subtree E in Phylogenetics of modern shorebirds (Charadriiformes) based on phenotypic evidence: analysis and discussion

Figure 13. Subtree E. Majority rule consensus (MRC) tree for species of Thinocoridae, Phalaropodidae, and Scolopacidae (part) in the present study. See Figure 9 for definitions of the symbols used.

Figure 15 in Phylogenetics of modern shorebirds (Charadriiformes) based on phenotypic evidence: analysis and discussion

Figure 15. Phylogenetic tree proposed by Pereira & Baker (2005) for species of Tringa and allied genera, with Phalaropus as the most closely related out-group.

Figure 9. Subtree A in Phylogenetics of modern shorebirds (Charadriiformes) based on phenotypic evidence: analysis and discussion

Figure 9. Subtree A. Majority rule consensus (MRC) tree for species of Pedionomidae, Jacanidae, Rostratulidae, and Glareolidae in the present study. Percentages for branches in the MRC are given above the branches (•, 100%), and bootstrap percentages are given below branches (O, <50%; •, 100%).

Figure 11. Subtree C in Phylogenetics of modern shorebirds (Charadriiformes) based on phenotypic evidence: analysis and discussion

Figure 11. Subtree C. Majority rule consensus (MRC) tree for species of Vanellinae in the present study. See Figure 9 for definitions of the symbols used.

Figure 10. Subtree B in Phylogenetics of modern shorebirds (Charadriiformes) based on phenotypic evidence: analysis and discussion

Figure 10. Subtree B. Majority rule consensus (MRC) tree for Pluvianellidae and Chionididae, terminals for Alcidae, Stercorariidae, Larinae, Sterninae, and Rynchopidae, and species of Burhinidae, Ibidorhynchidae, Recurvirostridae, Haematopodidae, and Dromadidae in the present study. See Figure 9 for definitions of the symbols used.