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6,234 results for “phenotypes”
Is phenotypic evolution affected by spiders' construction behaviors?
<p><span>What happens when organisms actively modify their environment? Clarifying the role of construction behavior on a macroevolutionary scale is crucial to interpreting phenotypic evolution. Spiders, an extremely successful group of animals exhibiting a wide variety of functional, morphological, and behavioral diversity, are ideal candidates to test whether animal behaviors are related to their phenotypic evolution. Herein, we reconstructed the phylogenomic relationships of 303 spiders covering 105 families with 99 newly developed molecular markers that universally apply across Araneae, and</span><span> explicitly tested the potential link between construction behavior and somatic evolution based on extensive morphological data from 3,342</span><span> extant species</span><span> and 682 fossil species. </span><span>Our dated molecular phylogeny provides the backbone for analyses, revealing the behavioral and ecological processes behind these spiders' morphological adaptations. </span><span>Evolutionary model analyses showed the artifacts constructed by spiders, especially the suspending webs, expand the spider's ability to inhabit different habitats. These artifacts have more substantial impacts on their </span><span>somatic</span><span> traits than habitats and promote different trajectories of morphological evolution. Specifically, for spiders, silk-lined burrowing produced larger bodies, relatively shorter legs, and longer patellae, while web-building produced smaller bodies, relatively long legs, and shorter patellae, and hunting promoted an intermediate morphological size. Molecular convergence analysis showed that genes related to morphogenesis or </span><span>response to stimulus</span><span> and stress are enriched in spiders with construction behavior. Our study demonstrated that the construction behavior of an animal plays a crucial role in determining the direction and intensity of the selection pressure imposed on it and provides strong evidence that construction behaviors are associated with phenotypic evolution on macroevolutionary timescales.</span></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>
Drug Response Phenotype Terms
<p>Data and code for "Leveraging a Pharmacogenomics Knowledge-base to Formulate a Drug Response Phenotype Terminology for Genomic Medicine" published in <em>Bioinformatics</em></p>
FIGURE 31 in Anatolian endemic genus Bolua (Orthoptera: Tettigoniidae: Tettigoniinae): genetic and phenotypic data indicate inconsistent diversity and evolutionary patterns
FIGURE 31. The ML tree showing unique base changes per phylogroups of Bolua, obtained by maximum parsimony analysis (the base position indicates by the position number in the concatenated matrix)
FIGURES 28–30 in Anatolian endemic genus Bolua (Orthoptera: Tettigoniidae: Tettigoniinae): genetic and phenotypic data indicate inconsistent diversity and evolutionary patterns
FIGURES 28–30. Song in Bolua (28—B. balikesirensis from Balıkesir; 29—and 30—B. bursaensis from Bursa and Kütahya, respectively; A, B and C show song at three different time scale)
FIGURES 18–26. 18–20 in Anatolian endemic genus Bolua (Orthoptera: Tettigoniidae: Tettigoniinae): genetic and phenotypic data indicate inconsistent diversity and evolutionary patterns
FIGURES 18–26. 18–20. Female head+pronotum+tegmina in Bolua (18—B. turkiyae, 19—B. balıkesirensis, 20—B. bursaensis; D—dorsal view, L—lateral view). 21–23. Female subgenital plate in Bolua (21—B. turkiyae, 22—B. balıkesirensis, 23—B. bursaensis; V—ventral view, L-lateral view). 24–26. Ovipositor in Bolua (24—B. turkiyae, 25—B. balıkesirensis, 26— B. bursaensis)
FIGURE 2 in Anatolian endemic genus Bolua (Orthoptera: Tettigoniidae: Tettigoniinae): genetic and phenotypic data indicate inconsistent diversity and evolutionary patterns
FIGURE 2. Phylogenetic tree, chronogram and taxonomic pattern obtained from the matrix constituted by concatenation of COI+NAD2+VAL+ITS sequences. The bootstrap (ML)/ posterior probability (BI) supports to the node are given above the node and the time to the most recent common ancestor (TMRCA) and the 95% HPD as million years below the node. The results from the species delimitation test are indicated by a bar line.
FIGURE 27 in Anatolian endemic genus Bolua (Orthoptera: Tettigoniidae: Tettigoniinae): genetic and phenotypic data indicate inconsistent diversity and evolutionary patterns
FIGURE 27. The PCA plots and trait contribution prepared from the metric data sets of male and female individuals belonging to Bolua
FIGURE 1 in Anatolian endemic genus Bolua (Orthoptera: Tettigoniidae: Tettigoniinae): genetic and phenotypic data indicate inconsistent diversity and evolutionary patterns
FIGURE 1. Distribution of Bolua [for detail of the localities see material examined per species; Bolu(1), Kastamonu (2,3,4, 6), Karabük (5), Çankırı (7), Balıkesir (8), Bursa (9, 10), Kütahya (11) and Kocaeli (12, 13)]
FIGURES 3–17. 3–5 in Anatolian endemic genus Bolua (Orthoptera: Tettigoniidae: Tettigoniinae): genetic and phenotypic data indicate inconsistent diversity and evolutionary patterns
FIGURES 3–17. 3–5. Male head+pronotum+tegmina in Bolua (3—B. turkiyae, 4—B. balıkesirensis, 5—B. bursaensis; D— dorsal view, L—lateral view). 6–8. Male anal tergite in Bolua (6—B. turkiyae, 7—B. balıkesirensis, 8—B. bursaensis). 9–11. Male cercus in Bolua (9—B. turkiyae, 10—B. balıkesirensis, 11—B. bursaensis). 12–14. Male subgenital plate in Bolua (12—B. turkiyae, 13—B. balıkesirensis, 14—B. bursaensis). 15–17. Titillators in Bolua (15—B. turkiyae, 16—B. balıkesirensis, 17—B. bursaensis)
Inbreeding measures (FROH) and racing phenotypes in North American thoroughbred horses
<p>We quantified inbreeding based on runs of homozygosity (FROH) using 333K SNP genotypes from 768 Thoroughbred horses born in North America to evaluate the effect of inbreeding on racing traits. Among North American horses, FROH was not associated (P = 0.518) with the probability of ever racing but was significantly associated with the number of race starts (P = 0.002). Among raced horses, those with a 10% higher <em>F</em><sub>ROH </sub>than the mean inbreeding coefficient were predicted to have 3.5 fewer race starts compared to horses with a mean inbreeding coefficient.</p>
Data for the Arcadia Phenotype-o-mat pub
<p>This is the data used in the Arcadia phenotype-o-mat pub which is available <a href="https://doi.org/10.57844/arcadia-112f-5023">here</a>.</p> <p>The repository containing the analysis code for these data is available <a href="https://github.com/Arcadia-Science/2024-phenotypeomat">here</a>.</p> <p>This data repository contains two data sets from two species, <em>Chlamydomonas reinhardtii </em>and <em>Chlamydomonas smithii.</em></p> <p>The data sets are chlorophyll fluorescence from colonies streaked on petri dishes (chlorophyll_fluorescence_data.tar.gz) and multi-wavelength reflectance data from colonies in 96 well format (reflectance_data.tar.gz).</p>
Genome annotations for: Multi-omics approaches define novel aphid effector candidates associated with virulence and avirulence phenotypes
<div> <p><span><span>Peter Thorpe</span></span><span><span>1</span></span><span><span>, Simone Altmann</span></span><span><span>1</span></span><span><span>, Rosa Lopez-Cobollo</span></span><span><span>2</span></span><span><span>, Nadine Douglas</span></span><span><span>3</span></span><span><span>, Javaid Iqbal</span></span><span><span>2</span></span><span><span>, Sadia Kanvil</span></span><span><span>2</span></span><span><span>, </span><span>Jean-Christophe Simon</span></span><span><span>4</span></span><span><span>, </span><span>James </span><span>C. </span><span>Carolan</span></span><span><span>3</span></span><span><span>, Jorunn Bos</span></span><span><span>1</span><span>*</span></span><span><span>, Colin Turnbull</span></span><span><span>2</span><span>*</span></span><span> </span></p> </div> <div> <p><span><span>1</span></span><span><span>School of Life Sciences, University of Dundee, UK;</span> </span><span><span>2</span></span><span><span>Department of Life Sciences, Imperial College London, UK; </span></span><span><span>3</span></span><span><span>Department of Biology, Maynooth University, </span><span>Republic of Ireland</span><span>; </span></span><span><span>4</span></span> <span><span>INRAE , France</span><span>. *Authors for correspondenc</span><span>e: </span></span><a href="mailto:j.bos@dundee.ac.uk" target="_blank" rel="noreferrer noopener"><span><span>j.bos@dundee.ac.uk</span></span></a><span><span>, </span></span><a href="mailto:c.turnbull@imperial.ac.uk" target="_blank" rel="noreferrer noopener"><span><span>c.turnbull@imperial.ac.uk</span></span></a><span><span>. </span></span><span> </span></p> <p> </p> <p><span>This is a repository for the version3 gene predictions and annotation for the pea aphid used for the publication:</span></p> <p> </p> <p><strong><span><span><span>Multi-omics approaches define novel aphid effector candidates associated with </span><span>virulence and </span><span>avirulence</span> <span>phenotypes</span></span><span> </span></span></strong></p> <p> </p> <div> <p><span><span>ABSTRACT</span></span></p> </div> <div> <p><span><span>Background</span></span><span><span>. Compatibility between aphids and plant hosts is genetically </span><span>determined</span><span> by both interacting organisms. For example, plants may carry resistance (R) genes or deploy chemical defences. Aphid saliva </span><span>contains</span><span> many proteins that are secreted into host tissues. </span><span>S</span><span>ubset</span><span>s</span><span> of these proteins are predicted to act as effectors, either subverting or triggering host immunity. However, associating </span><span>particular effectors</span><span> with virulence or </span><span>avirulence</span><span> outcomes presents challenges due to the combinatorial complexity. Here we use defined aphid and host genetics to test for co-segregation of expressed aphid transcripts and proteins with virulent or avirulent phenotypes.</span></span><span> </span></p> </div> <div> <p><span><span>Results</span><span>. </span></span><span><span>We compared virulent and avirulent pea aphid parental genotypes, and their bulk segregant F</span></span><span><span>1</span></span><span><span> progeny on </span></span><span><span>Medicago </span><span>truncatula</span> </span><span><span>genotypes</span></span> <span><span>carrying or lacking the </span></span><span><span>RAP1 </span></span><span><span>resistance </span><span>quantitative trait locus</span><span>. </span><span>D</span><span>ifferential expression </span><span>analysis based on </span><span>RNA sequencing </span><span>of whole bod</span><span>y and head samples, </span><span>in combination with proteomics of saliva and salivary glands</span><span>,</span> <span>enabled us </span><span>to pinpoint proteins </span><span>associated</span><span> with virulence/</span><span>avirulence</span><span> phenotypes. </span></span><span><span><span>There was relatively </span><span>little impact</span><span> of </span></span></span><span><span><span>host genotype, </span></span></span><span><span><span>whereas</span><span> l</span></span></span><span><span>arge numbers of transcripts and proteins were differentially expressed between parental aphids, </span><span>likely a</span><span> reflection of their classification as divergent biotypes within the pea aphid species complex. Many fewer </span><span>transcripts</span> <span>intersected with the equivalent differential expression patterns in the bulked F</span></span><span><span>1</span></span><span><span> progeny, providing an effective filter for removing </span><span>genomic </span><span>background effects</span></span><span><span>. </span><span>Overall, t</span><span>here were more upregulated genes detected in the </span><span>F</span></span><span><span>1</span></span><span> <span>avirulent </span><span>dataset </span><span>compared with the virulent one. </span><span>Some of the</span><span> differentially expressed transcripts </span><span>were also found in the differentially expressed proteomes</span><span>, with a</span><span>minopeptidase N prot</span><span>eins </span><span>being </span><span>t</span><span>he most frequent</span><span> differentially expressed</span><span> family</span></span><span><span>. </span><span>In addition</span><span>, a </span><span>substantial</span> <span>proportion </span><span>(26%) </span><span>of salivary proteins lack annotations, suggesting that </span><span>many </span><span>novel functions </span><span>remain</span><span> to be discovered. </span></span><span> </span></p> </div> <div> <p><span><span>Conclusions.</span></span><span><span> Especially when combined with tightly controlled genetics of both insect and host, multi-</span><span>omic</span><span> approaches are powerful tools for revealing and filtering candidate lists down to plausible genes for further functional analysis as putative </span><span>aphid </span><span>effectors.</span></span><span> </span></p> </div> </div>
Data for: Phenotypic variation of hydraulic traits for woody species
<p>Hydraulic traits are major determinants of plant fitness, thus exerting control over vegetation structure, function and distribution. Yet it remains unclear whether and how hydraulic traits respond to environmental stimuli (i.e., phenotypic variation of hydraulic traits; PVHT), and if the coordination between different hydraulic traits and the trait-climate relationship are affected by PVHT.</p> <p>Here, we synthesized data of PVHT (maximum hydraulic conductivity and water potential inducing 50% loss of hydraulic conductivity) as well as potentially related morphological and anatomical traits (e.g. sapwood density, branch Huber value, mean and hydraulic weighted conduit diameter). We analyzed the magnitude, direction and source of variation of the plastic response, as well as the influence of environmental factors on trait coordination. Additionally, we compared the intra- and inter- specific variation between key hydraulic traits and climate metrics (mean annual precipitation and mean annual temperature) at the site of growth, as well as across the population range.</p> <p>PVHT was highly variable in both magnitude and direction, which was contingent on the environmental factor. The variation in PVHT mainly occurred at high taxonomic levels (i.e., family and genus), whereas phenology explained little variation for PVHT. Despite the high variability, trait correlation remained robust in the presence of environmental stimuli. Moreover, trait-climate relationships differed at inter-specific and intra-specific levels. The intra-specific variation of hydraulic traits in most species showed no correlation with climate metrics compared with the high correlation of hydraulic traits with climate metrics across species.</p> <p>Our findings suggest that the high variability of PVHT does not affect the trait correlation which may be valuable in predicting vegetation dynamics under varying environments. The distinct trait-climate relationships highlight the need to unravel the driving force of PVHT, as well as the adaptive strategy across populations.</p>
Data from: Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers.
<p>Genotype data from</p> <p>Li H, Giger ML, Sun C, Ponsukcharoen U, Huo D, Lan L, Olopade OI, Jamieson AR, Brown JB, Di Rienzo A. (2014) Pilot study demonstrating potential association between breast cancer image-based risk phenotypes and genomic biomarkers. Med Phys. 41(3)</p>
Distant metastases of breast cancer resemble primary tumors in tumor cell composition but differ in immune cell phenotypes
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Summary Statistics for Genetic determinants and phenotypic consequences of blood T-cell proportions in 207,000 diverse individuals
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Spatial regulation of substrate adhesion directs fibroblast morphotype and phenotype
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Data from: Phenotypic correlates between clock genes and phenology among populations of Diederik cuckoo, Chrysococcyx caprius
<p>Dataset of Clock genes for Diederik Cuckoos collected as part of a migration genetics study.</p>
AF MIMIC Phenotyping
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