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169 results for “selective inference”
Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection
<p>Phylogenetic reconstruction and introgression detection rely on an assumption about the probability distribution of gene tree topologies. Recently, evidence has emerged that population size differences can affect the probability distribution of gene tree topologies in the presence of purifying selection. Here, using the population genetic simulator SLiM, we provide evidence that in the presence of purifying selection, population size differences can lead to biases in phylogenetic inference. We also provide evidence that in the presence of purifying selection, population size differences can cause statistics used for introgression detection to exhibit patterns resembling those caused by introgression. In addition, we present a theoretical analysis showing that the occurrence of population size–dependent gene tree distributions is an inherent consequence of purifying selection. Our work underscores the importance of considering the potential confounding effect of purifying selection on phylogenetic inference and introgression detection.</p>
Text-fig. 6. Stratigraphic and phylogenetic placement inferred for fossil Fraxinus fruits. Only Fraxinus fossil fruits identified on the section level are included. The black color represents selected fossil fruits from published literature (excluding some Eocene North American occurrences not assigned to section), the red color represents the fossil fruits from the Lühe flora, Yunnan, Southwest China. The phylogenetic relationships are based on Hinsinger et al. (2013). in Fraxinus L. (Oleaceae) Fruits From The Early Oligocene Of Southwest China And Their Biogeographic Implications
Text-fig. 6. Stratigraphic and phylogenetic placement inferred for fossil Fraxinus fruits. Only Fraxinus fossil fruits identified on the section level are included. The black color represents selected fossil fruits from published literature (excluding some Eocene North American occurrences not assigned to section), the red color represents the fossil fruits from the Lühe flora, Yunnan, Southwest China. The phylogenetic relationships are based on Hinsinger et al. (2013).
Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
<p>Habitat selection models frequently use data collected from a small geographic area over a short window of time to extrapolate patterns of relative abundance to unobserved areas or periods of time. However, these types of models often poorly predict how animals will use habitat beyond the place and time of data collection because space-use behaviors vary between individuals and are context-dependent. Here, we present a modelling workflow to advance predictive distribution performance by explicitly accounting for individual variability in habitat selection behavior and dependence on environmental context. Using global positioning system (GPS) data collected from 238 individual pronghorn, (<em>Antilocapra americana</em>), across 3 years in Utah, we combine individual-year-season-specific exponential habitat-selection models with weighted mixed-effects regressions to both draw inference about the drivers of habitat selection and predict space-use in areas/times where/when pronghorn were not monitored. We found a tremendous amount of variation in both the magnitude and direction of habitat selection behavior across seasons, but also across individuals, geographic regions, and years. We were able to attribute portions of this variation to season, movement strategy, sex, and regional variability in resources, conditions, and risks. We were also able to partition residual variation into inter- and intra-individual components. We then used the results to predict population-level, spatially and temporally dynamic, habitat-selection coefficients across Utah, resulting in a temporally dynamic map of pronghorn distribution at a 30x30m resolution but an extent of 220,000km2. We believe our transferable workflow can provide managers and researchers alike a way to turn limitations of traditional habitat selection models - variability in habitat selection - into a tool to understand and predict species-habitat associations across space and time.</p>
Pervasive selection biases inferences of the species tree
<p>Supplementary files, scripts, and data of 'Pervasive selection biases inferences of the species tree' by Borges, Boussau, Szöllősi, and Kosiol</p>
Integrative QTL mapping and selection signatures in Groningen White Headed cattle inferred from whole-genome sequences
<p>Here, we aimed to identify and characterize genomic regions that differ between Groningen White Headed (GWH) breed and other cattle, and in particular to identify candidate genes associated with coat color and/or eye-protective phenotypes. Firstly, whole genome sequences of 170 animals from eight breeds were used to evaluate the genetic structure of the GWH in relation to other cattle breeds by carrying out principal components and model-based clustering analyses. Secondly, the candidate genomic regions were identified by integrating the findings from: a) a genome-wide association study using GWH, other white headed breeds (Hereford and Simmental), and breeds with a non-white headed phenotype (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel, Dutch Belted, and Holstein Friesian); b) scans for specific signatures of selection in GWH cattle by comparison with four other Dutch traditional breeds (Dutch Friesian, Deep Red, Meuse-Rhine-Yssel and Dutch Belted) and the commercial Holstein Friesian; and c) detection of candidate genes identified via these approaches. The alignment of the filtered reads to the reference genome (ARS-UCD1.2) resulted in a mean depth of coverage of 8.7X. After variant calling, the lowest number of breed-specific variants was detected in Holstein Friesian (148,213), and the largest in Deep Red (558,909). By integrating the results, we identified five genomic regions under selection on BTA4 (70.2–71.3 Mb), BTA5 (10.0–19.7 Mb), BTA20 (10.0–19.9 and 20.0–22.7 Mb), and BTA25 (0.5–9.2 Mb). These regions contain positional and functional candidate genes associated with retinal degeneration (e.g., <em>CWC27</em> and <em>CLUAP1</em>), ultraviole<em>t</em> protection (e.g., <em>ERCC8</em>), and pigmentation (e.g. <em>PDE4D</em>) which are probably associated with the GWH specific pigmentation and/or eye-protective phenotypes, e.g. Ambilateral Circumocular Pigmentation (ACOP). Our results will assist in characterizing the molecular basis of GWH phenotypes and the biological implications of its adaptation.</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
<p>Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal underlying abiotic and biotic drivers of community dynamics. Here we present a model-based predictive framework to quantify multidimensional trait distributions of communities (community trait niches), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait niches and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.</p>
Data from: Inferring ecological selection from multidimensional community trait distributions along environmental gradients
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Population size differences can lead to biases in phylogenetic inference and introgression detection in the presence of purifying selection
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Data from: Forecasting animal distribution through individual habitat selection: Insights for population inference and transferable predictions
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Data from: Inference of selective force on house mice genomes during secondary contact in East Asia
<p>The house mouse (<em>Mus musculus</em>), commensal to humans, has spread globally via human activities, leading to secondary contact between genetically divergent subspecies. This pattern of genetic admixture can provide insights into the selective forces at play in this well-studied model organism. Our analysis of 163 house mouse genomes, mainly from East Asia, revealed substantial admixture between the subspecies<em> castaneus</em> and <em>musculus</em>, particularly in Japan and southern China. We revealed, despite the admixture, that all Y chromosomes in the East Asian samples belonged to the <em>musculus</em>-type haplogroup, potentially explained by genomic conflict under sex ratio distortion due to varying copy numbers of ampliconic genes on sex chromosomes. We also investigated the influence of natural selection on the post-hybridization of the subspecies <em>castaneus</em> and <em>musculus</em> in Japan. Even though the genetic background of most Japanese samples closely resembles the subspecies<em> musculus</em>, certain genomic regions overrepresented the <em>castaneus</em>-like genetic components, particularly in immune-related genes. Furthermore, a large genomic block containing a vomeronasal/olfactory receptor gene cluster predominantly harbored <em>castaneus</em>-type haplotypes in the Japanese samples, highlighting the possible role of olfaction-based recognition in shaping hybrid genomes.</p>
Table of hsp65 OTUs (cutoff 99%), their inferred taxonomic allocations according to the hsp65 database and, for selected OTUs, closest species obtained from GenBank (BLAST) with percent identity.
<p>This table is part of the paper intitled "Comparison of Actinobacteria communities from human-impacted and pristine karst caves"</p>
How to select predictive models for decision making or causal inference? Experiments data
<p>This is the full result data for the experiments of the paper : Doutreligne, M., & Varoquaux, G. (2023). How to select predictive models for decision making or causal inference?, https://hal.science/hal-03946902. <br><br>The code repository is : https://github.com/soda-inria/caussim/tree/main</p> <p>The files in this dataset are the one for the most computationnally costly experiments. There is one folder for each of the four datasets used in the paper. Then, one folder for each of the experimental setup. The files required for the main figure (Fig.3) of the paper are the one labelled #fig3 in the following descriptions.</p> <p>Details on the files : </p> <p>.<br>├── acic_2016_save<br>│ ├── acic_2016__nuisance_non_linear__candidates_hist_gradient_boosting__dgp_1-77__rs_1-5<br>│ │ └── run_logs.csv: results for the experiment with non linear models for both the nuisances and the candidates<br>│ ├── acic_2016__nuisance_non_linear__candidates_ridge__dgp_1-77__rs_1-10<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates<br>│ └── acic_2016__stacked_regressor__dgp_1-77__seed_1-10<br>│ └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3<br>├── acic_2018_save<br>│ └── acic_2018__nuisance_non_linear__candidates_hist_gradient_boosting__first_uid_432<br>│ └── run_logs.csv results for the experiment with stacked models (linear and non linear) for the nuisances models and non linear models for the candidates #fig3<br>├── caussim_save<br>│ ├── caussim__linear_regressor__test_size_5000__n_datasets_1000<br>│ │ ├── run_logs.csv: results for the experiment with stacked models for the nuisances models and linear models for the candidates <br>│ │ └── simu.yaml: configuration file of the experiment<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_join_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, joined sets for the nuisances and the candidates<br>│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_separated_nuisance_train_set<br>│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, separated sets for the nuisances and the candidates<br>│ └── caussim__stacked_regressor__test_size_5000__n_datasets_1000<br>│ ├── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and linear models for the candidates #fig3<br>│ └── simu.yaml: configuration file of the experiment<br>└── twins_save<br> └── twins__stacked_regressor__rs_1-10__overlap_0.1-3<br> └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3</p>
Repositiry for the article: "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain Mbebi & Zoran Nikoloski
<p>This is the repository for the manuscript "Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection" by Alain J. Mbebi & Zoran Nikoloski.</p> <p><strong>Organisation</strong></p> <ol> <li>The folder Codes contains the following R scripts with the K-folds cross-validation option to learn the hyperparameters:</li> </ol> <ul> <li>Mixed_L1L21_GRN.R which computes L1L21-solution</li> <li>Mixed_L1L21G_GRN.R which computes L1L21G-solution</li> <li>Mixed_L2L21_GRN.R which computes L2L21-solution</li> <li>Mixed_L2L21G_GRN.R which computes L2L21G-solution</li> <li>L1L21_Dream5_Scerevisiae_example_run.R is an example run using the L1L21-solution with S. cerevisiae data (Network 4 in DREAM5 challenge) All files needed to successfully run "L1L21_Dream5_Scerevisiae_example_run" are locaded in the folder Codes.</li> </ul> <p>2. The folder Figures contains all figures in the manuscript.</p> <p>3. The folder Inferred-networks contains all network objects for each dataset and each inference methods in the comparative analysis.</p> <p><strong>Dependencies and required packages</strong></p> <p>The following packages are required for the contending approaches in the comparative analysis: "devtools", "foreach", "plyr", "glmnet" and "randomForest".</p> <p><strong>GENIE3</strong></p> <p>The GENIE3 package can be installed from: <a href="http://bioconductor.org/packages/release/bioc/html/GENIE3.html">http://bioconductor.org/packages/release/bioc/html/GENIE3.html</a></p> <p><strong>TIGRESS</strong></p> <p>The TIGRESS repository can be obtained from: <a href="https://github.com/jpvert/tigress">https://github.com/jpvert/tigress</a></p> <p><strong>ENNET</strong></p> <p>The ENNET repository can be obtained from: <a href="https://github.com/slawekj/ennet">https://github.com/slawekj/ennet</a></p> <p><strong>PLSNET</strong></p> <p>The Matlab source code of PLSNET can be obtained from: <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1398-6#Sec17</a></p> <p><strong>PORTIA</strong></p> <p>The PORTIA repository can be obtained from: <a href="https://github.com/AntoinePassemiers/PORTIA">https://github.com/AntoinePassemiers/PORTIA</a></p> <p><strong>D3GRN</strong></p> <p>The Matlab source code of D3GRN can be obtained from: <a href="https://github.com/chenxofhit/D3GRN">https://github.com/chenxofhit/D3GRN</a></p> <p><strong>Fused-LASSO</strong></p> <p>The fused-LASSO repository can be obtained from: <a href="https://github.com/omranian/inference-of-GRN-using-Fused-LASSO">https://github.com/omranian/inference-of-GRN-using-Fused-LASSO</a></p> <p><strong>ANOVerence</strong></p> <p>Because of some technical issues (e.g code's accessibility: <a href="http://www2.bio.ifi.lmu.de/%CB%9Ckueffner/anova.tar.gz">http://www2.bio.ifi.lmu.de/˜kueffner/anova.tar.gz</a>), we were not able to reproduce ANOVerence results and used the inferred network from DREAM5 challenge instead.</p> <p>4. Although the codes here were tested on Fedora 29 (Workstation Edition) using R (version 4.2.2), they can run under any Linux or Windows OS distributions, as long as all the required packages are compatible with the desired R version.</p>
Data from: Inference of selective force on house mice genomes during secondary contact in East Asia
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Data from: Major histocompatibility complex class II variation in bottlenose dolphin from Adriatic Sea: inferences about the extent of balancing selection
The bottlenose dolphin (Tursiops truncatus) is the most common cetacean species worldwide and the only marine mammal species resident in the Croatian part of the Adriatic Sea. To gain insight into genetic diversity of bottlenose dolphins at adaptively important loci relevant to conservation, we analysed the polymorphism of major histocompatibility complex (MHC) genes, which play a key role in pathogen confrontation and clearance. Specifically, we examined the diversity of MHC class II DRA, DQA and DQB alleles in 50 bottlenose dolphins from the Adriatic Sea collected between 1997 and 2011 and in 12 animals from other Mediterranean locations. Notable variation in DQA, DQB and three-locus haplotypes was found, with all 10 DQA and 12 DQB alleles encoding unique protein products. Analysis of the ratio of non-synonymous to synonymous substitution rates suggests that positive selection acts at both highly variable loci. Phylogenetic analyses revealed trans-species polymorphism at the DQB locus, strongly indicating the influence of balancing selection in the long term. In fact, the balancing selection observed in bottlenose dolphins is higher than that reported for most other cetaceans and comparable to that seen in terrestrial mammals.
Data from: Major histocompatability complex variation in insular populations of the Egyptian vulture: inferences about the roles of genetic drift and selection
Insular populations have attracted the attention of evolutionary biologists because of their morphological and ecological peculiarities with respect to their mainland counterparts. Founder effects and genetic drift are known to distribute neutral genetic variability in these demes. However, elucidating whether these evolutionary forces have also shaped adaptive variation is crucial to evaluate the real impact of reduced genetic variation in small populations. Genes of the Major Histocompatibility Complex (MHC) are classical examples of evolutionarily relevant loci because of their well-known role in pathogen confrontation and clearance. In this study, we aim to disentangle the partial roles of genetic drift and natural selection in the spatial distribution of MHC variation in insular populations. To this end, we integrate the study of neutral (22 microsatellites and one mtDNA locus) and MHC class II variation in one mainland (Iberia) and two insular populations (Fuerteventura and Menorca) of the endangered Egyptian vulture (Neophron percnopterus). Overall, the distribution of the frequencies of individual MHC alleles (N=17 alleles from two class II B loci) does not significantly depart from neutral expectations, which indicates a prominent role for genetic drift over selection. However, our results point towards an interesting co-evolution of gene duplicates that maintains different pairs of divergent alleles in strong linkage disequilibrium on islands. We hypothesize that the co-evolution of genes may counteract the loss of genetic diversity in insular demes, maximize antigen recognition capabilities when gene diversity is reduced, and promote the co-segregation of the most efficient allele combinations to cope with local pathogen communities.
Data from: Effect of microsatellite selection on individual and population genetic inferences: an empirical study using cross-specific and species-specific amplifications
Although whole-genome sequencing is becoming more accessible and feasible for nonmodel organisms, microsatellites have remained the markers of choice for various population and conservation genetic studies. However, the criteria for choosing microsatellites are still controversial due to ascertainment bias that may be introduced into the genetic inference. An empirical study of red deer (Cervus elaphus) populations, in which cross-specific and species-specific microsatellites developed through pyrosequencing of enriched libraries, was performed for this study. Two different strategies were used to select the species-specific panels: randomly vs. highly polymorphic markers. The results suggest that reliable and accurate estimations of genetic diversity can be obtained using random microsatellites distributed throughout the genome. In addition, the results reinforce previous evidence that selecting the most polymorphic markers leads to an ascertainment bias in estimates of genetic diversity, when compared with randomly selected microsatellites. Analyses of population differentiation and clustering seem less influenced by the approach of microsatellite selection, whereas assigning individuals to populations might be affected by a random selection of a small number of microsatellites. Individual multilocus heterozygosity measures produced various discordant results, which in turn had impacts on the heterozygosity-fitness correlation test. Finally, we argue that picking the appropriate microsatellite set should primarily take into account the ecological and evolutionary questions studied. Selecting the most polymorphic markers will generally overestimate genetic diversity parameters, leading to misinterpretations of the real genetic diversity, which is particularly important in managed and threatened populations.
Data from: Long term impacts of selective logging on two Amazonian tree species with contrasting ecological and reproductive characteristics: inferences from Eco-gene model simulations
The impact of logging and subsequent recovery after logging is predicted to vary depending on specific life history traits of the logged species. The Eco-gene simulation model was used to evaluate the long-term impacts of selective logging over 300 years on two contrasting Brazilian Amazon tree species, Dipteryx odorata and Jacaranda copaia. D. odorata (Leguminosae), a slow growing climax tree, occurs at very low densities, whereas J. copaia (Bignoniaceae) is a fast growing pioneer tree that occurs at high densities. Microsatellite multilocus genotypes of the pre-logging populations were used as data inputs for the Eco-gene model and post-logging genetic data was used to verify the output from the simulations. Overall, under current Brazilian forest management regulations, there were neither short nor long-term impacts on J. copaia. By contrast, D. odorata cannot be sustainably logged under current regulations, a sustainable scenario was achieved by increasing the minimum cutting diameter at breast height from 50 to 100 cm over 30-year logging cycles. Genetic parameters were only slightly affected by selective logging, with reductions in the numbers of alleles and single genotypes. In the short term, the loss of alleles seen in J. copaia simulations was the same as in real data, whereas fewer alleles were lost in D. odorata simulations than in the field. The different impacts and periods of recovery for each species support the idea that ecological and genetic information are essential at species, ecological guild or reproductive group levels to help derive sustainable management scenarios for tropical forests.
Data from: Differential effect of selection against LINE retrotransposons among vertebrates inferred from whole-genome data and demographic modeling
Variation in LINE composition is one of the major determinants for the substantial size and structural differences among vertebrate genomes. In particular, the larger genomes of mammals are characterized by hundreds of thousands of copies from a single LINE clade, L1, whereas nonmammalian vertebrates possess a much greater diversity of LINEs, yet with orders of magnitude less in copy number. It has been proposed that such variation in copy number among vertebrates is due to differential effect of LINE insertions on host fitness. To investigate LINE selection, we deployed a framework of demographic modeling, coalescent simulations, and probabilistic inference against population-level whole-genome data sets for four model species: one population each of threespine stickleback, green anole, and house mouse, as well as three human populations. Specifically, we inferred a null demographic background utilizing SNP data, which was then exploited to simulate a putative null distribution of summary statistics that was compared with LINE data. Subsequently,we applied the inferred null demographic model with an additional exponential size change parameter, coupled with model selection, to test for neutrality as well as estimate the strength of either negative or positive selection. We found a robust signal for purifying selection in anole and mouse, but a lack of clear evidence for selection in stickleback and human. Overall, we demonstrated LINE insertion dynamics that are not in accordance to a mammalian versus nonmammalian dichotomy, and instead the degree of existing LINE activity together with host-specific demographic history may be the main determinants of LINE abundance.
Data from: Reconstructing paternal genotypes to infer patterns of sperm storage and sexual selection in the hawksbill turtle
Postcopulatory sperm storage can serve a range of functions, including ensuring fertility, allowing delayed fertilization and facilitating sexual selection. Sperm storage is likely to be particularly important in wide-ranging animals with low population densities, but its prevalence and importance in such taxa, and its role in promoting sexual selection, are poorly known. Here, we use a powerful microsatellite array and paternal genotype reconstruction to assess the prevalence of sperm storage and test sexual selection hypotheses of genetic biases to paternity in one such species, the critically endangered hawksbill turtle, Eretmochelys imbricata. In the majority of females (90.7%, N = 43), all offspring were sired by a single male. In the few cases of multiple paternity (9.3%), two males fertilized each female. Importantly, the identity and proportional fertilization success of males were consistent across all sequential nests laid by individual females over the breeding season (up to five nests over 75 days). No males were identified as having fertilized more than one female, suggesting that a large number of males are available to females. No evidence for biases to paternity based on heterozygosity or relatedness was found. These results indicate that female hawksbill turtles are predominantly monogamous within a season, store sperm for the duration of the nesting season and do not re-mate between nests. Furthermore, females do not appear to be using sperm storage to facilitate sexual selection. Consequently, the primary value of storing sperm in marine turtles may be to uncouple mating and fertilization in time and avoid costly re-mating.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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