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Population genetics of caribou in the Alaska-Yukon border region: implications for designation of conservation units and small herd persistence

Better knowledge of genetic relationships between the Fortymile caribou herd and its neighbors is needed for conservation decision-making in Canada. Here, we contribute the first fine-scale analysis of genetic population structure in nine contiguous caribou herds at the geographic boundaries between Barren-ground and Northern Mountain caribou, and at the Alaska-Yukon border. Using pairwise differentiation metrics, STRUCTURE, and discriminant analysis of principal components (DAPC) to analyze 15 microsatellite loci in 379 caribou, we found complex patterns of genetic differentiation. The Fortymile was the only herd assigned to more than one genetic cluster, indicative of its history as a larger herd whose range expansions and gene flow to other herds were likely important to maintaining diversity across a functioning genetic metapopulation. Some small herds (Chisana, Klaza, and White Mountains) were genetically distinct, while others (Hart River, Clear Creek, Mentasta) exhibited little differentiation from herds they occasionally overlap, including herds assigned to different conservation units (DUs). This genetic connectivity does not result from demographic connectivity, as episodic contact during rut, rather than herd switching, is the likely mechanism. Unusually, one small herd (White Mountains) maintained genetic differentiation despite rut overlap with Fortymile. Our data reveal that some herds with different ecological and behavioral attributes are demographically independent but nonetheless genetically connected. Thus, we suggest that managing caribou for an appropriate level of genetic connectivity, while also supporting herd persistence, will be essential to conserve caribou genetic diversity in the region.

Data from: Range-wide genetic analysis of an endangered bumble bee (Bombus affinis) reveals population structure, isolation by distance, and low colony abundance

Declines in bumblebee species ranges and abundances are documented across multiple continents and have prompted the need for research to aid species recovery and conservation. The rusty patched bumblebee (Bombus affinis) is the first federally-listed bumblebee species in North America. We conducted a range-wide population genetics study of B. affinis from across all extant conservation units to inform conservation efforts. To understand the species' vulnerability and help establish recovery targets, we examined population structure, patterns of genetic diversity, and population differentiation. Additionally, we conducted site-level analysis of colony abundance to inform prioritizing areas for conservation, translocation, and other recovery actions. We find substantial evidence of population structuring along an east-to-west gradient. Putative populations show evidence of isolation by distance, high inbreeding coefficients, and a range wide male diploidy rate of ~15%. Our results suggest the Appalachians represents a genetically distinct cluster with high levels of private alleles and substantial differentiation from the rest of the extant range. Site-level analyses suggest low colony abundance estimates for B. affinis compared to similar datasets of stable, co-occurring species. These results lend genetic support to trends from observational studies suggesting B. affinis has undergone a recent decline and exhibits substantial spatial structure. The low colony abundances observed here suggest caution in overinterpreting the stability of populations even where B. affinis is reliably detected interannually. These results help delineate informed management units, provide context for the potential risks of translocation programs, and can help set clear recovery targets for this and other threatened bumblebee species.

Demography and environment modulate the effects of genetic diversity on extinction risk in a butterfly metapopulation

Linking genetic diversity to extinction is a common goal in genomic studies. Recently, a debate has arisen regarding the importance of genetic variation in conservation as some studies have failed to find associations between genome-wide genetic diversity and extinction risk. However, only rarely are genetic diversity and fitness measured together in the wild, and typically variation in demographic history or environment are ignored. It is therefore difficult to infer whether a lack of an association is real or obscured by confounding factors. To address these shortcomings, we analysed genetic data from 7,501 individuals with extinction data from 279 meadows and mortality of 1,742 larval nests in a butterfly metapopulation. We found strong negative associations between genetic diversity and extinction when heterozygosity was included alone in models. However, this association was no longer present when ecological covariates were included. Interactions between heterozygosity and demographic variables revealed that associations are context-dependent or only detectable when confounding factors are controlled. For example, extinction declined with increasing heterozygosity in large but not currently small populations, although negative associations between heterozygosity, extinction, and mortality were detected in populations with a recent history of decline. We conclude that low genetic diversity is an important predictor of extinction, predicting >25% increase in extinction beyond ecological factors in certain contexts. This highlights that inferences about the importance of genetic diversity should not rely on genomic data alone but requires investments in obtaining demographic and environmental data from natural populations to jointly assess their impact on population extinction risk.

Data from: Genetic susceptibility, Mendelian randomization and nomogram model construction of gestational diabetes mellitus

The dataset contains subjects' basic information, including the Identification number of the test sample, fasting plasma glucose (FPG), oral glucose tolerance test 1h plasma glucose (1hPG), oral glucose tolerance test 2h plasma glucose (2hPG), glycated hemoglobin (HbA1c), Systolic blood pressure (SBP), Diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), High-density lipoprotein cholesterol (HDL-c), Low-density lipoprotein cholesterol (LDL-c), and also involves subjects' genetic variant information used for analysis of the association of functional polymorphisms and GDM. The variables including SBP_M, DBP_M, FPG_M, 1hPG_M, 2hPG_M, HbA1c_M, TG_M represent the mean value of SBP, DBP, FPG, 1hPG, 2hPG, HbA1c, TG, which are used for the stratification analysis. This study has obtained the support from the Ethics Committee of Guilin Medical University. All included subjects signed the informed consent.

opencc-by-4.0Mar 2024View details →

Code & data of the paper "Data sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data"

Code & data of the paper "Data sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data"

opencc-by-4.0Mar 2024View details →

Supplementary file for the manuscript entitled: Demographic and genetic impacts of powdery mildew in a young oak cohort

Barres et al 2023 Supplementary material-vf.pdf: supplementary material file for the related article

opencc-by-4.0Sep 2023View details →

GSA-GxE: A Framework of Global Sensitivity Analysis of Maize Coupled with Genetics by Environments (GxE) Model

We present the coupled Global Sensitivity Analysis (GSA) and Genetics by Environment model (GxE) framework (Sarzaeim and Muñoz-Arriola, submitted). GSA-GxE uses the sensitivity analysis method PAWN (Pianosi and Wagener, 2015) coupled with the environmental covariance matrix used in GxE modeling (Jarquin et al., 2014). GSA-GxE estimates the relative sensitivity of maize yield predictability to hydroclimate variables that interact with maize genetics from the environmental covariances and genetic marker structures. We include hydroclimate variables like temperature (T), solar radiation (SR), rainfall (R), and relative humidity (RH). The data, codes, and scripts presented here were used to develop and test the GSA-GxE framework. They were built upon an enhanced version of the multi-dimensional Genomes to Fields (G2F) database consisting of maize genetic, phenotypic, environmental, and metadata in 84 field experiments in 2014-2017 across the U.S. and province of Ontario, Canada (Sarzaeim et al., 2020, 2022, 2023). This digital package contains a multi-dimensional Climate and Omics dataset, the GSA-GxE framework created in Python, and the GxE model developed in R. Acknowledgement This work was supported by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.

opencc-by-4.0Nov 2022View details →

F I G U R E 4 in Genetic characteristics and growth patterns of the hybrid grouper derived from the hybridization of Epinephelus fuscoguttatus (female) Epinephelus polyphekadion (male)

F I G U R E 4 Examples of functional secondary structures of the 5S gene

Data and code for: Does the definition of a novel environment affect the ability to detect cryptic genetic variation?

Anthropogenic change exposes populations to environments that have been rare or entirely absent from their evolutionary past. Such novel environments are hypothesised to release cryptic genetic variation, a hidden store of variance that can fuel evolution. However, support for this hypothesis is mixed. One possible reason is a lack of clarity in what is meant by 'novel environment', an umbrella term encompassing conditions with potentially contrasting effects on the exposure or concealment of cryptic variation. Here, we use a meta-analysis approach to investigate changes in the total genetic variance of multivariate traits in ancestral versus novel environments. To determine whether the definition of a novel environment could explain the mixed support for a release of cryptic genetic variation, we compared absolute novel environments, those not represented in a population's evolutionary past, to extreme novel environments, those involving frequency or magnitude changes to environments present in a population's ancestry. Despite sufficient statistical power, we detected no broadscale pattern of increased genetic variance in novel environments, finding the type of novel environment did not explain any significant variation in effect sizes. When effect sizes were partitioned by experimental design, we found increased genetic variation in studies based on broad-sense measures of variance, and decreased variation in narrow-sense studies, in support of previous research. Therefore, the source of genetic variance, not the definition of a novel environment, was key to understanding environment-dependant genetic variation, highlighting non-additive genetic variance as an important component of cryptic genetic variation and avenue for future research.

Collecting baleen whale blow samples by drone: a minimally intrusive tool for conservation genetics

In coastal British Columbia, Canada, marine megafauna such as humpback whales (Megaptera novaeangliae) and fin whales (Balaenoptera physalus velifera) have been subject to a history of exploitation and near extirpation. While their populations have been in recovery, significant threats are posed by proposed natural resource ventures in this region, in addition to the compounding effects of increasingly severe marine heatwaves. Genetic tools play a vital role in informing conservation efforts, but the associated collection of tissue biopsy samples can be challenging for the investigators and disruptive to the ongoing behaviour of the targeted whales. Here we evaluate a minimally intrusive approach based on collecting exhaled breath condensate, or respiratory 'blow' samples, from baleen whales using an unoccupied aerial system (UAS), within Gitga'at First Nation territory for conservation genetics. Minimal behavioural responses to the sampling technique were observed, with no response detected 87% of the time (of 112 UAS deployments). DNA from whale blow (n = 88 samples) was extracted, and DNA profiles consisting of 10 nuclear microsatellite loci, sex identification, and mitochondrial (mt) DNA haplotypes, were constructed. An average of 7.5 microsatellite loci per individual were successfully genotyped. The success rates for mtDNA and sex assignment were 80% and 89% respectively. Thus, this minimally intrusive sampling method can be used to describe genetic diversity and generate genetic profiles for individual identification. The results of this research show the potential of UAS-collected whale blow for conservation genetics from a remote location.

opencc-by-4.0Apr 2024View details →

DArTseq genetic dataset associated with the article "Hybrids as mirrors of the past: genomic footprints reveal spatio-temporal dynamics and extinction risk of alpine extremophytes in the mountains of Central Asia"

Description: This file stores genetic information on the single nucleotide polymorphism markers (SNPs) in the examined alkali grasses (Poaceae: Puccinellia). The dataset was generetad by Genome-Wide Restriction Fragment Analysis via the DArTseq platform (Diversity Arrays Technology Pty Ltd, Canberra, Australia), which combines complexity reduction methods, fragment size selection, and high-throughput sequencing, optimised for a target organism. The file contains raw data.   Usage notes: We used R (version 4.2.2, 2022-10-31; https://www.R-project.org/) and RStudio (version 2022.07.2+576 "Spotted Wakerobin" Release (e7373ef832b49b2a9b88162cfe7eac5f22c40b34, 2022-09-06; http://www.rstudio.com/) on Windows 8.1 to handle this file. We used the dartR R-package (version 2.7.2) with necessary dependencies to import, proccess and analyse this data file as an object of a class genlight (dartR) in the R environment. You may also handle the file as an object of a class genlight using the adegenet and ade4 R-packages. To learn more about installation procedure and how to use of the R-packages visit: https://cran.r-project.org/web/packages/available_packages_by_name.html.

Data from: Genetic analysis of red deer (Cervus elaphus) administrative management units in a human-dominated landscape - patterns of genetic diversity, population structure and gene flow

Red deer (Cervus elaphus) throughout central Europe are impacted by different anthropogenic activities including habitat fragmentation, selective hunting, and translocations. This has substantial influences on genetic diversity and the long-term conservation of local populations of this species. Here we use genetic samples from 480 red deer individuals to assess the genetic diversity and differentiation of the 12 administrative management units located in Schleswig Holstein, the northernmost federal state in Germany. We applied multiple analytical approaches and show that the history of local populations (i.e., translocations, culling of individuals outside of designated red deer zones, and anthropogenic infrastructures) has led to comparably low levels of genetic diversity. The mean expected heterozygosity was below 0.6 and we observed on average 4.2 alleles across 12 microsatellite loci. Effective population sizes below the recommended level of 50 were estimated for multiple local populations. Our estimates of genetic structure and gene flow show that red deer in northern Germany are best described as a complex network of asymmetrically connected subpopulations, with high genetic exchange among some local populations and reduced connectivity of others. Genetic diversity was also correlated with population densities of neighboring management units. Based on these findings, we suggest that connectivity among existing management units needs to be considered in the practical management of the species, which means that some administrative management units should be managed together, while the effective isolation of other units needs to be mitigated.

Data from: genetic evidence of differential dispersal pattern in Asiatic wild dog: comparing populations with different pack size and tiger densities

Dispersal is a multi-causal, crucial life-history event in shaping the genetic and behavioral structure of mammals. We assessed the dispersal pattern of dholes aka Asiatic wild dog (Cuon alpinus), a social monogamous mammal at two tiger reserves of Maharashtra with different degrees of pack size and competition with tigers i.e. Tadoba-Andhari (TATR, smaller pack size, higher tiger density) and Nawegaon-Nagzira (NNTR, larger pack size, lower tiger density). We used the microsatellite data of 174 individual genotypes (98 males and 67 females) to assess the dispersal pattern of dholes from two populations with varying pack size, tiger density, and landscape connectivity using gene flow as a proxy. We compared the population structure, pairwise F statistics, assignment index, and relatedness across a spatial scale. Overall, the results suggested a difference in sex-bias dispersal pattern for the two sub-populations, exhibiting significant results for female-biased dispersal in the TATR population with a smaller pack size and higher tiger density. Our study highlights the variability in sex-biased dispersal patterns in two different populations which could be the consequence of different variables such as pack size, tiger density, and geographical scale. The study warrants further quantitative investigation including several factors including individual behavior, pack composition, pack size, tiger density, etc. In the present Anthropocene era, determining the sex bias in dispersal patterns for a short-range, pack-living carnivore will help in devising an effective conservation management plan for their long-term survival.

opencc-zeroOct 2023View details →

First insights into population structure and genetic diversity versus host specificity in trypanorhynch tapeworms using multiplexed shotgun genotyping

Theory predicts relaxed host specificity and high host vagility should contribute to reduced genetic structure in parasites while strict host specificity and low host vagility should increase genetic structure. Though these predictions are intuitive, they have never been explicitly tested in a population genomic framework. Trypanorhynch tapeworms, which parasitize sharks and rays (elasmobranchs) as definitive hosts, are the only order of elasmobranch tapeworms that exhibit considerable variability in their definitive host specificity. This allows for unique combinations of host use and geographic range, making trypanorhynchs ideal candidates for studying how these traits influence population-level structure and genetic diversity. Multiplexed shotgun genotyping (MSG) datasets were generated to characterize component population structure and infrapopulation diversity for a representative of each trypanorhynch suborder: the ray-hosted Rhinoptericola megacantha (Trypanobatoida) and the shark-hosted Callitetrarhynchus gracilis (Trypanoselachoida). Adults of R. megacantha are more host-specific and less broadly distributed than adults of C. gracilis, allowing correlation between these factors and genetic structure. Replicate tapeworm specimens were sequenced from the same host individual, from multiple conspecific hosts within and across geographic regions, and from multiple definitive host species. For R. megacantha, population structure coincided with geography rather than host species. For C. gracilis, limited population structure was found, suggesting a potential link between degree of host specificity and structure. Conspecific trypanorhynchs from the same host individual were found to be as, or more, genetically divergent from one another as from conspecifics from different host individuals. For both species, high levels of homozygosity and positive FIS values were documented.

opencc-by-4.0Dec 2018View details →

Figure 1 in Genetic characterization of field populations of Culex pipiens Linnaeus, 1758 (Diptera: Culicidae) sampled from the Aegean region of Turkey

Figure 1. Map of collection localities for mosquitoes.

opencc-by-4.0Dec 2018View details →

Figure 2. A in Genetic characterization of field populations of Culex pipiens Linnaeus, 1758 (Diptera: Culicidae) sampled from the Aegean region of Turkey

Figure 2. A UPGMA dendrogram based on Nei's (1972) genetic distances among C. pipiens populations.

opencc-by-4.0Jun 2017View details →

Figure 1 in Guidelines and quantitative standards to improve consistency in cetacean subspecies and species delimitation relying on molecular genetic data

Figure 1. Guidelines for studies of cetacean taxonomy based on genetic data.

opencc-by-4.0Nov 2024View details →

The genetic mechanism of B chromosome drive in rye illuminated by chromosomescale assembly

Figures, tables and supplementary data from the paper: The genetic mechanism of B chromosome drive in rye illuminated by chromosomescale assembly Abstract: The genomes of many plants, animals, and fungi frequently comprise dispensable B chromosomes that rely upon various chromosomal drive mechanisms to counteract the tendency of non-essential genetic elements to be purged over time. The B chromosome of rye – a model system for nearly a century – undergoes targeted nondisjunction during first pollen mitosis, favouring segregation into the generative nucleus, thus increasing their numbers over generations. However, the genetic mechanisms underlying this process are poorly understood. Here, using a newly-assembled, ~430Mb-long rye B chromosome pseudomolecule, we identify five candidate genes whose role as trans-acting moderators of the chromosomal drive is supported by karyotyping, chromosome drive analysis and comparative RNA-seq. Among them, we identify DCR28, coding a microtubule-associated protein related to cell division, and detect this gene also in the B chromosome of Aegilops speltoides. The DCR28 gene family is neo-functionalised and serially-duplicated with 15 B chromosome-located copies that are uniquely highly expressed in the first pollen mitosis of rye.

opencc-by-4.0Sep 2024View details →

Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score

<h2>ML-GPS: Machine Learning-Assisted Genetic Priority Score</h2> This Zenodo repository contains data and code associated with the publication: Chen R, Duffy Á, Petrazzini BO, Vy HM, Stein D, Mort M, Park JK, Schlessinger A, Itan Y, Cooper DN, Jordan DM, Rocheleau G, Do R. Expanding drug targets for 112 chronic diseases using a machine learning-assisted genetic priority score. Nat Commun. 2024 Oct 15;15(1):8891. doi: <a href="https://doi.org/10.1038/s41467-024-53333-y">10.1038/s41467-024-53333-y</a>. <h3>Important notes</h3> <ul> <li>You can interactively view the top 10% of ML-GPS predictions without download at <a href="https://rstudio-connect.hpc.mssm.edu/mlgps/">https://rstudio-connect.hpc.mssm.edu/mlgps/</a>.</li> <li>For running Jupyter notebooks, please follow the instructions in the README of the GitHub repository at <a href="https://github.com/robchiral/ML-GPS">https://github.com/robchiral/ML-GPS</a>.</li> </ul> <h3>Repository contents</h3> Files needed to train ML-GPS and ML-GPS DOE: <ul> <li>Files needed for Jupyter notebooks.zip: Data files required for preprocessing and training.</li> <li>Jupyter notebooks.zip: Notebooks for cleaning data, training models, and generating predictions.</li> </ul> <h3>Other files:</h3> <ul> <li>Predictions for all gene-phecode pairs.zip: ML-GPS and ML-GPS DOE scores for all analyzed gene-phecode pairs.</li> <li>Summary statistics.zip: Genetic association summary statistics for all tested gene-phecode pairs.</li> </ul> <h3>Updated performance metrics</h3> <table> <tbody> <tr> <td>Model</td> <td>Open Targets AUPRC</td> <td>SIDER AUPRC</td> </tr> <tr> <td>ML-GPS (non-DOE)</td> <td>0.074</td> <td>0.080</td> </tr> <tr> <td>ML-GPS DOE (activator predictions)</td> <td>0.029</td> <td>0.042</td> </tr> <tr> <td>ML-GPS DOE (inhibitor predictions)</td> <td>0.067</td> <td>0.064</td> </tr> </tbody> </table> <h3>Zenodo versions</h3> <ul> <li>Version 4: Updated notebooks and external data to use Open Targets 2024.9; summary statistics are unchanged</li> <li>Version 3: Corrected error where DOE for rare and ultrarare variants was incorrectly incorporated</li> <li>Version 2: Original release accompanying the publication</li> </ul>