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927 results for “genetic study”
Data from: Genetic distance as an alternative to physical distance for definition of gene units in association studies
Background: Some association studies, as the implemented in VEGAS, ALIGATOR, i-GSEA4GWAS, GSA-SNP and other software tools, use genes as the unit of analysis. These genes include the coding sequence plus flanking sequences. Polymorphisms in the flanking sequences are of interest because they involve cis-regulatory elements or they inform on untyped genetic variants trough linkage disequilibrium. Gene extensions have customarily been defined as ± 50 Kb. This approach is not fully satisfactory because genetic relationships between neighbouring sequences are a function of genetic distances, which are only poorly replaced by physical distances. Results: Standardized recombination rates (SRR) from the deCODE recombination map were used as units of genetic distances. We searched for a SRR producing flanking sequences near the ± 50 Kb offset that has been common in previous studies. A SRR ≥ 2 was selected because it led to gene extensions with median length = 45.3 Kb and the simplicity of an integer value. As expected, boundaries of the genes defined with the ± 50 Kb and with the SRR ≥2 rules were rarely concordant. The impact of these differences was illustrated with the interpretation of top association signals from two large studies including many hits and their detailed analysis based in different criteria. The definition based in genetic distance was more concordant with the results of these studies than the based in physical distance. In the analysis of 18 top disease associated loci form the first study, the SRR ≥2 genes led to a fully concordant interpretation in 17 loci; the ± 50 Kb genes only in 6. Interpretation of the 43 putative functional genes of the second study based in the SRR ≥2 definition only missed 4 of the genes, whereas the based in the ± 50 Kb definition missed 10 genes. Conclusions: A gene definition based on genetic distance led to results more concordant with expert detailed analyses than the commonly used based in physical distance. The genome coordinates for each gene are provided to maintain a simple use of the new definitions.
Data from: Influence of introduction history on genetic variation in introduced populations: a case study of Oregon Chub
Population introductions and reintroductions have become a common tool for conserving threatened species, but oftentimes introduced populations have reduced the genetic diversity compared with the source population they were founded from. Population introductions played an important role in the recovery of the Oregon Chub Oregonichthys crameri, a small floodplain minnow found in western Oregon. Unlike many introduction efforts, introduced populations of Oregon Chub were founded using large numbers of individuals (hundreds in many cases) and each population had a unique introduction history (e.g., number of founders, source populations selected, duration of the introduction effort). We used microsatellite loci to examine 13 introduced populations and their respective sources to evaluate how well the introduction program captured genetic diversity present in the wild populations. Genetic variation was reduced by roughly 25% in one introduced population, and three introduced populations showed evidence of a genetic bottleneck due to heterozygote excess. Populations introduced from multiple sources had greater genetic diversity than populations from a single source. When multiple source populations were used, all source populations contributed genetic material to the introduced population, though the proportional contribution from each source population varied. Using correlation analyses and general linear models, we explored the relationship between introduction history variables and genetic diversity. Our top-ranked models included genetic diversity in the source population, and this variable had the highest variable importance weight (0.999), but the number of founders and the number of source populations were also important. Overall, the Oregon Chub introduction program was highly successful at capturing the genetic variation observed in natural populations. Results of this study will be useful for planning future population introductions for Oregon Chub and other species of conservation concern.
Data from: The red coral (Corallium rubrum) transcriptome: a new resource for population genetics and local adaptation studies
The question of species survival and evolution in heterogeneous environments has long been a subject for study. Indeed, it is often difficult to identify the molecular basis of adaptation to contrasted environments, and nongenetic effects increase the difficulty to disentangle fixed effects, such as genetic adaptation, from variable effects, such as individual phenotypic plasticity, in adaptation. Nevertheless, this question is also of great importance for understanding the evolution of species in a context of climate change. The red coral (Corallium rubrum) lives in the Mediterranean Sea, where at depths ranging from 5 to 600 m, it meets very contrasted thermal conditions. The shallowest populations of this species suffered from mortality events linked with thermal anomalies that have highlighted thermotolerance differences between individuals. We provide here a new transcriptomic resource, as well as candidate markers for the study of local adaptation. We sequenced the transcriptome of six individuals from 5 m and six individuals from 40 m depth at the same site of the Marseilles bay, after a period of common garden acclimatization. We found differential expression maintained between the two depths even after common garden acclimatization, and we analysed the polymorphism pattern of these samples. We highlighted contigs potentially implicated in the response to thermal stress, which could be good candidates for the study of thermal adaptation for the red coral. Some of these genes are also involved in the response to thermal stress in other corals. Our method enables the identification of candidate loci of local adaptation useful for other nonmodel organisms.
Data from: A practical introduction to random forest for genetic association studies in ecology and evolution
Large genomic studies are becoming increasingly common with advances in sequencing technology, and our ability to understand how genomic variation influences phenotypic variation between individuals has never been greater. The exploration of such relationships first requires the identification of associations between molecular markers and phenotypes. Here we explore the use of Random Forest (RF), a powerful machine learning algorithm, in genomic studies to discern loci underlying both discrete and quantitative traits, particularly when studying wild or non-model organisms. RF is becoming increasingly used in ecological and population genetics because, unlike traditional methods, it can efficiently analyze thousands of loci simultaneously and account for non-additive interactions. However, understanding both the power and limitations of Random Forest is important for its proper implementation and the interpretation of results. We therefore provide a practical introduction to the algorithm and its use for identifying associations between molecular markers and phenotypes, discussing such topics as data limitations, algorithm initiation and optimization, as well as interpretation. We also provide short R tutorials as examples, with the aim of providing a guide to the implementation of the algorithm. Topics discussed here are intended to serve as an entry point for molecular ecologists interested in employing Random Forest to identify trait associations in genomic data sets.
Data from: Inconsistent use of multiple comparison corrections in studies of population genetic structure: are some type I errors more tolerable than others?
Studies of genetic population structure often involve numerous tests of Hardy-Weinberg equilibrium (HWE), linkage disequilibrium (LD), and genetic differentiation. Tests of HWE or LD are important precursors to population structure assessments. When conducting multiple related statistical tests, type 1 error increases, e.g., familywise error rate (FWER) inflation. FWER inflation can alter the results of statistical tests and thus the conclusions. Authors are aware of the need to control for FWER inflation, but there has been low consistency of use. Furthermore, there is a potential for the choice of correction methods to be exploited to selectively use FWER corrections to avoid data exclusion or to result in increased the rejection of null hypotheses. We surveyed literature from 2011-2013 to determine if studies of population structure assess LD and HWE and if FWER corrections were applied consistently across different types of genetic differentiation, linkage disequilibrium, and Hardy-Weinberg equilibrium tests. We found a lack of documentation of FWER corrections in studies, and we advocate for authors to be more cognizant in reporting their corrections. We also found significantly inconsistent FWER corrections, with a bias towards less restrictive correction on genetic differentiation and more restrictive corrections with LD and HWE. While varied adjustments of FWER for different types of analyses might be justified, papers with inconsistent usage across tests of HWE, LD and genetic differentiation did not present rationale for their FWER corrections. We also found a lack of documentation of HWE, LD and FWER corrections in studies. We encourage authors to report statistical tests and related FWER corrections, use FWER corrections consistently or justify their different methods in the same study.
Data from: Challenges in analysis and interpretation of microsatellite data for population genetic studies
Advancing technologies have facilitated the ever-widening application of genetic markers such as microsatellites into new systems and research questions in biology. In light of the data and experience accumulated from several years of using microsatellites, we present here a literature review that synthesizes the limitations of microsatellites in population genetic studies. With a focus on population structure, we review the widely used fixation (FST) statistics and Bayesian clustering algorithms and find that the former can be confusing and problematic for microsatellites and that the latter may be confounded by complex population models and lack power in certain cases. Clustering, multivariate analyses, and diversity-based statistics are increasingly being applied to infer population structure, but in some instances these methods lack formalization with microsatellites. Migration-specific methods perform well only under narrow constraints. We also examine the use of microsatellites for inferring effective population size, changes in population size, and deeper demographic history, and find that these methods are untested and/or highly context-dependent. Overall, each method possesses important weaknesses for use with microsatellites, and there are significant constraints on inferences commonly made using microsatellite markers in the areas of population structure, admixture, and effective population size. To ameliorate and better understand these constraints, researchers are encouraged to analyze simulated datasets both prior to and following data collection and analysis, the latter of which is formalized within the approximate Bayesian computation framework. We also examine trends in the literature and show that microsatellites continue to be widely used, especially in non-human subject areas. This review assists with study design and molecular marker selection, facilitates sound interpretation of microsatellite data while fostering respect for their practical limitations, and identifies lessons that could be applied toward emerging markers and high-throughput technologies in population genetics.
FIGURE 1 in Spiophanes norrisi sp. nov. (Polychaeta: Spionidae) — a new species from the NE Pacific coast, separated from the Spiophanes bombyx complex based on both morphological and genetic studies
FIGURE 1. Assignment of specimens examined in the course of this study to species.
Figure 3 from: Al-Musawi ZJMA, Al-Juhaishi AMR (2024) Association study between D2 receptor A-241G, rs1799978 genetic variation and olanzapine efficacy in Iraqi schizophrenic patients. Pharmacia 71: 1-6. https://doi.org/10.3897/pharmacia.71.e111984
Figure 3 The Prevalence of D2 receptor alleles A-241G (rs1799978) among both male and female volunteers.
The effect of sampling density and study area size on landscape genetic inferences for the Mississippi slimy salamander (Plethodon mississippi)
<p>In the field of landscape genetics, it is largely unknown how choices regarding population sampling density and study area size impact inferences about which habitat features impede vs. facilitate gene flow. While it is commonly recommended that sampling locations be spaced no further apart than the average individual dispersal distance, for low mobility species, this could lead to a logistically challenging number of sampling locations, or a small and unrepresentative study area. We assessed the effects of sampling density and study area size on landscape genetics inferences for a dispersal-limited amphibian, the Mississippi slimy salamander (<i>Plethodon mississippi</i>), via comparative analysis of nested datasets. Microsatellite-based genetic distances among individuals were divided into three datasets representing either sparse sampling across a large study area, dense sampling across a small study area, or sparse sampling across the same small study area. These datasets were each used as a response variable in maximum likelihood population effects models that assessed the nature and strength of the relationship, if any, between each of five land use classes (i.e., potential predictor variables) and the response variable. Comparative analyses were based on the rank order of effect (i.e., strongest to weakest), sign of effect (i.e., gene flow resistance vs. facilitation), spatial scale of effect, and functional relationship with gene flow. Outcomes were interpreted within the context of five possible combinations of congruence among datasets. We found that each best-fit model associated with the three datasets had the same sign of effect for hardwood forests, manmade structures, and pine forests. However, different sampling densities led to a different inferred functional relationship between agricultural areas and gene flow. Furthermore, study area size appeared to influence the inferred scale of effect of manmade structures and sign of effect of pine forests. Taken together, our findings provided evidence for an influence of sampling density, study area size, and sampling effort upon inferences. Accordingly, in the absence of strong <i>a priori</i> information about spatial-genetic structure and species' life history traits, we recommend iterative subsampling and reanalysis of empirical datasets, coupled with continued investigation into the sensitivities of landscape genetics analyses using simulations or other controlled experimental designs. </p>
FIGURE 1 in Comparative study and genetic diversity of Salicornia persica (Chenopodiaceae) using SCOT molecular markers
FIGURE 1. Distribution map of the studied populations.
Museomics contributes to the spatiotemporal assessment of genetic diversity and structure in wild and ex situ conservation organisms: a case study of three endangered coastal plants in Japan
<p><span>Understanding </span><span>the extent to which </span><span>genetic diversity of wild populations in ex</span> <span>situ</span><span> conservation can be retained is </span><span>crucial</span><span> for the management of </span><span>such</span><span> populations. Wild individuals collected in the target area in the past </span><span>and</span><span> present can be used to estimate the number of alleles lost over time in wild populations and</span><span> thereby</span><span> the number of alleles whose loss could be </span><span>prevented</span><span> by ex</span> <span>situ</span><span> conservation. </span><span>Here</span><span>, we assessed the genetic diversity of wild and ex</span> <span>situ</span><span> conservation populations of three endangered coastal herb species</span><span>,</span><span> <em>Cirsium maritimum</em> Makino (Asteraceae), <em>Linaria japonica</em> Miq. (Plantaginaceae</span><span>) and</span><span> <em>Suaeda glauca</em></span><span><em> </em>(Bunge) Bunge (Amaranthaceae), which are endangered </span><span>species on</span><span> Awaji Island, Hyogo Prefecture, Japan, via multiplexed inter-simple sequence repeat genotyping by sequencing (MIG-seq). We </span><span>incorporated</span><span> the museomics approach, which </span><span>involves conducting</span><span> genetic analyses of museum specimens collected from the targeted wild populations in the past to estimate the temporal transition of genetic diversity in wild populations and the number of alleles maintai</span><span>ned in <em>ex situ</em></span><span> conservation. </span><span>Our </span><span>results </span><span>reveal</span><span> a declining trend in genetic diversity in the wild populations of all </span><span>investigated</span><span> species, although </span><span>this trend is </span><span>not significant. In all the species, </span><span>numerous</span><span> alleles were already lost in current wild populations, </span><span>whereas they</span><span> were </span><span>present</span><span> in the past wild and ex</span> <span>situ</span><span> conservation populations. Our study </span><span>indicates</span><span> that extinct alleles in current wild populations have been maintained in ex</span> <span>situ</span><span> conservation</span><span> by museomics approach. These </span><span>appro</span><span>aches </span><span>were effective in verifying</span><span> the genetic diversity retention effects of <em>ex</em></span><em> <span>situ</span></em><span> conservation populations.</span></p>
Figure 3 in Variations in heterochromatin content reveal important polymorphisms for studies of genetic improvement in garlic (Allium sativum L.)
Figure 3. Double staining with CMA/DAPI in accessions "Sussuapara - PI" (A), "Santo Antônio de Lisboa" (B), "Catetinho do Paraná 1254" (C), "Branco Mineiro - PI" (D). Inserts highlight small CMA+/DAPI- bands. Yellow arrows indicate the CMA+/DAPI- bands more evident in chromosomes and interphase nuclei. Red arrows indicate CMA+ bands in the overlaid chromosome (B). White arrows indicate gaps formed by condensation (D). Dots indicate distended nucleolar organiser regions (NORs) (f). Bar = 10 µm. Figure 2. Double staining with CMA/DAPI in accessions "Cateto Roxo 99" (E), "Roxo de Minas" (F) and "Sergipe" (G). Inserts highlight small CMA+/DAPI- bands. Yellow arrows indicate the CMA+/DAPI- bands more evident in chromosomes and interphase nuclei. Dots indicate distended nucleolar organiser regions (NORs) (F). Bar = 10 µm.
Searching for the relationship between the presence, extent, location and 12-month dynamics of cardiac fibrosis and the genetic profile in patients with dilated cardiomyopathy - a pilot study
Open the record for dataset details and reuse information.
Fig. 4 in Preliminary Studies On The Genetic Diversity Of An Endemic And Endangered Species Saussurea Esthonica Baer Ex Rupr. In Latvia
Fig. 4 Phylogenetic tree based on AFLP data using the Neighbour-Joining algorithm with Nei's genetic distance.
Figure 2 from: Jażdżewska AM, Corbari L, Driskell A, Frutos I, Havermans C, Hendrycks E, Hughes L, Lörz A-N, Stransky B, Tandberg AHS, Vader W, Brix S (2018) A genetic fingerprint of Amphipoda from Icelandic waters – the baseline for further biodiversity and biogeography studies. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 55–73. https://doi.org/10.3897/zookeys.731.19931
Figure 2 Neighbour-joining (NJ) tree of COI sequences (Suppl. material 1) based on Kimura 2-parameter. Triangles indicate the relative number of individuals studied (height) and sequence divergence (width). The asterisk (*) symbolizes taxa having already published sequences in BOLD/GenBank identified to species level. The numbers in front of the nodes indicate bootstrap support (1000 replicates, only values higher than 50% are presented). The vertical bars represent species delimitations taxonomies obtained from morphology and different species delimitation methods. The same colour indicates the same nominal species. Only the cases where incongruence between different delimitation methods were observed are shown. Note that this tree is not the reconstruction of evolutionary history of presented taxa.
Figure 1 from: Jażdżewska AM, Corbari L, Driskell A, Frutos I, Havermans C, Hendrycks E, Hughes L, Lörz A-N, Stransky B, Tandberg AHS, Vader W, Brix S (2018) A genetic fingerprint of Amphipoda from Icelandic waters – the baseline for further biodiversity and biogeography studies. In: Brix S, Lörz A-N, Stransky B, Svavarsson J (Eds) Amphipoda from the IceAGE-project (Icelandic marine Animals: Genetics and Ecology). ZooKeys 731: 55–73. https://doi.org/10.3897/zookeys.731.19931
Figure 1 Sampling stations. Depth contours are the following: 500 m, 1000 m, 1500 m, 2000 m, 2500 m, 3000 m. Station details are in Suppl. material 1.
Supplementary material 4 from: Davoli F, Cozzo M, Angeli F, Groff C, Randi E (2018) Infanticide in brown bear: a case-study in the Italian Alps – Genetic identification of perpetrator and implications in small populations. Nature Conservation 25: 55-75. https://doi.org/10.3897/natureconservation.25.23776
Table S1. Detailed results of the biological model (consensus and composite) :
Supplementary material 1 from: Davoli F, Cozzo M, Angeli F, Groff C, Randi E (2018) Infanticide in brown bear: a case-study in the Italian Alps – Genetic identification of perpetrator and implications in small populations. Nature Conservation 25: 55-75. https://doi.org/10.3897/natureconservation.25.23776
Text S1. Parameters used for parentage analysis :
Supplementary material 3 from: Davoli F, Cozzo M, Angeli F, Groff C, Randi E (2018) Infanticide in brown bear: a case-study in the Italian Alps – Genetic identification of perpetrator and implications in small populations. Nature Conservation 25: 55-75. https://doi.org/10.3897/natureconservation.25.23776
Text S3. Detailed results of LRmix STUDIO for each suspected male: Global Composite (ADO 0.55) :
Supplementary material 2 from: Davoli F, Cozzo M, Angeli F, Groff C, Randi E (2018) Infanticide in brown bear: a case-study in the Italian Alps – Genetic identification of perpetrator and implications in small populations. Nature Conservation 25: 55-75. https://doi.org/10.3897/natureconservation.25.23776
Text S2. Detailed results of LRmix STUDIO for each suspected male: Global Consensus (ADO 0.65) :
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.