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opencc-by-4.0Mar 2022View details →

Genome of the isolates: Enhanced cultured diversity of the mouse gut microbiota enables custom-made synthetic communities

The draft genome of the isolates in Mouse Intestinal Bacteria Collection (miBC)   Microbiome research is hampered by the fact that many bacteria are still unknown and by the lack of publicly available isolates. Fundamental and clinical research is in need of comprehensive and well-curated repositories of cultured bacteria from the intestine of mammalian hosts. Due to host-specific features of the gut microbiota, it is sound to establish collections of isolates from single host species. Hence, this project established a collection of bacterial strains isolated from the intestine of mice. The original version of the collection published in 2016 (Lagkouvardos, et. al. 2016. Nat. Microbiol.). was doubled by the addition of 112 strains, representing a total of 141 species across 6 phyla and 35 families for the entire collection. As we aimed to create a well-curated resource, all bacterial species within miBC have been taxonomically described and are publicly available.

opencc-zeroMay 2024View details →

Data from: Genome-wide association mapping within a local Arabidopsis thaliana population more fully reveals the genetic architecture for defensive metabolite diversity

A paradoxical finding from genome-wide association studies (GWAS) in plants is that variation in metabolite profiles typically maps to a small number of loci, despite the complexity of underlying biosynthetic pathways. This discrepancy may partially arise from limitations presented by geographically diverse mapping panels. Properties of metabolic pathways that impede GWAS by diluting the additive effect of a causal variant, such as allelic and genic heterogeneity and epistasis, would be expected to increase in severity with the geographic range of the mapping panel. We hypothesized that a population from a single locality would reveal an expanded set of associated loci. We tested this in a French Arabidopsis thaliana population (< 1 km transect) by profiling and conducting GWAS for glucosinolates, a suite of defensive metabolites that have been studied in depth through functional and genetic mapping approaches. For two distinct classes of glucosinolates, we discovered more associations at biosynthetic loci than previous GWAS with continental-scale mapping panels. Candidate genes underlying novel associations were supported by concordance between their observed effects in the TOU-A population and previous functional genetic and biochemical characterization. Local populations complement geographically diverse mapping panels to reveal a more complete genetic architecture for metabolic traits.

opencc-by-4.0Sep 2024View details →

Datasets: Population genomics and mitochondrial DNA reveal cryptic diversity in North American Spring Cavefishes (Amblyopsidae, Forbesichthys)

Forbesichthys_allsites.vcf: Dataset in VCF format used to perform Effective Population Size estimation. Forbesichthys_SNPs_NoLD.vcf: Dataset in VCF format used to perform PCA, fastStrucuture, and phylogenetic analyses. Files with extension .sfs contain site spectrum frequencies generated with the program easySFS.py.

opencc-by-4.0Oct 2024View details →

Low-input breeding in stone pine, a multipurpose forest tree with low genome diversity

Stone pine (Pinus pinea L.) is an emblematic tree species within the Mediterranean basin, with high ecological and economic relevance due to the production of edible nuts. Breeding programmes to improve pine nut production started decades ago in Southern Europe but have been hindered by the near absence of polymorphisms in the species genome and the lack of suitable genomic tools. In this study, we assessed new stone pine’s genomic resources and their utilisation in breeding and sustainable use, by using a commercial SNP-array (5,671 SNPs). Firstly, we confirmed the accurate clonal identification and identity check of 99 clones from the Spanish breeding programme. Secondly, we successfully estimated genomic relationships in clonal collections, an information needed for low-input breeding and genomic prediction. Thirdly, we applied this information to genomic prediction for total number of cones unspoiled by pests and their weight measured in three Spanish clonal tests. Genomic prediction accuracy depends on the trait under consideration and possibly on the number of genotypes included in the test. Predictive ability (ry) was significant for the mean cone weight measured in the three clonal tests, while solely significant for the number of cones in one clonal test. The combination of a new SNP-array together with the phenotyping of relevant commercial traits into genomic prediction models, proved to be very promising to identify superior clones for cone weight. This approach opens new perspectives for early selection.

opencc-by-4.0Jun 2021View details →

Data release: Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration

Source data used in the publication: Berger et al. (2021) - Provisional title: 'Whole-genome sequencing of Schistosoma mansoni reveals extensive diversity with limited selection despite mass drug administration'. These data were used to generate all figures used in the publication and all files are organised and labelled specifically to run with the custom code that uses these data can be found at: http://doi.org/10.5281/zenodo.4975908.  File descriptions: SOURCE DATA.zip - All source data for all figures.  Figure 1b: <ul> <li>supplementary_data_9.txt - Metadata</li> </ul> Figure 2a&b: <ul> <li>207_PCA.eigenvec - PCA eigenvectors</li> <li>207_PCA.eigenval - PCA eigenvalues</li> </ul> Figure 2c: <ul> <li>autosomes.mdist - PLINK distance matrix used to build the neighbour joining phylogeny</li> </ul> Figure 2d: <ul> <li>all.pi.pixy.schools.txt - Nucleotide diversity results for each school subpopulation.</li> </ul> Figure 2e: <ul> <li>autosomes.dxy.5kb.schools.txt - Autosomal DXY results between school subpopulations. </li> <li>autosomes.fst.5kb.schools.txt - Autosomal FST results between school subpopulations.</li> </ul> Figure 2f: <ul> <li>admixture_all.txt - ADMIXTURE results for each sample and population sizes, column 1 represents number of populations (K), columns 3-8 represent admixture values for each population. </li> </ul> Figure 3a, Supplementary figure 10a: <ul> <li>sfs.csv - Site frequency spectra (allelic proportions at each frequency bin) for each school. </li> </ul> Figure 3b: <ul> <li>TD.all.txt - Tajima's D values calculated in 5 kb windows for each school subpopulation. </li> </ul> Figure 4a, Supplementary figures 13-18:  <ul> <li>ALL.MAYUGE.IHS.ihs.out.100bins.norm.txt.zip  - Normalised iHS scores for the Mayuge district parasite populations (Selscan output).</li> </ul> Figure 4b, Supplementary figures 13-18:  <ul> <li>ALL.TORORO.IHS.ihs.out.100bins.norm.txt.zip - - Normalised iHS scores for the Tororo district parasite populations (Selscan output).</li> </ul> Figure 4c, Supplementary figures 13-18:  <ul> <li>ALL.MAYUGEvsTORORO.xpehh.xpehh.out.norm.txt.zip - - Normalised XP-EHH scores between Mayuge and Tororo parasite populations.</li> </ul> Figure 4d, Supplementary figures 13-18: <ul> <li>MAYUGE_TORORO_2000.windowed.weir.txt.zip - FST values calculated between Mayuge and Tororo populations in 2kb windows.  </li> </ul> Figure 4e, Supplementary figures 12a&c: <ul> <li>MAYUGE_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Mayuge populations. </li> <li>TORORO_PI.windowed.pi.zip - Nucleotide diversity values calculated in 2 kb windows for Kocoge populations (Tororo district).</li> </ul> Figure 5a: <ul> <li>all.pi.treat.fix.txt.zip - Nucleotide diversity results for each treatment subpopulation</li> </ul> Figure 5b <ul> <li>autosomes.dxy.5kb.treatment.txt -  - Autosomal DXY results between clearance phenotype subpopulations. </li> <li>autosomes.fst.5kb.treatment.txt - Autosomal FST results between clearance phenotype subpopulations. </li> </ul> Figure 5c: <ul> <li>fst.windows.2kb.treatment.txt.zip - FST values for comparisons between different treatment groups (Pre-treatment, post-treatment (good clearers), post-treatment (poor clearers))</li> </ul> Figure 5d:  <ul> <li>assoc_err_binary.txt.zip - Results of binary trait association between miracidia sampled from hosts with good clearance phenotypes (where treatment appeared to be highly effective) and miracidia isolated post-treatment from hosts with poor clearance phenotypes (where miracidia are potentially derived from parasites that survived treatment.</li> </ul> Figure 5e: <ul> <li>assoc_err_linear.txt.zip - - Results of linear regression genome-wide association study with the ERR estimates for all 198 samples, using the mean of the posterior ERR estimates from Crellen et al. (2016) as a quantitative trait.</li> </ul> Supplementary figure 1: <ul> <li>median.coverage.txt - Normalised depth of read coverage (column 4) calculated in 25 kb windows (columns 2&3) across all samples for all chromosomes (column 1).</li> </ul> Supplementary figure 2a-f:  <ul> <li>cohort.genotyped.txt.zip -  - Variant quality site values (used to inform variant site retention or removal). </li> </ul> Supplementary figure 2g: <ul> <li>hard_filtered.imiss.txt -  Per sample variant missingness (used to inform quality control).</li> </ul> Supplementary figure 2h: <ul> <li>hard_filtered_filtindv.lmiss.txt.zip - Per site missingness (used to inform quality control).</li> </ul> Supplementary figure 3a, 4a, 4b: <ul> <li>prunedData.eigenvec - PCA eigenvectors</li> <li>prunedData.eigenval - PCA eigenvalues</li> </ul> Supplementary figure 3b: <ul> <li>pruned_data.mdist.csv - Distance matrix used as the basis for the neighbour joining phylogeny.</li> </ul> Supplementary figure 5: <ul> <li>cv_scores.txt - ADMIXTURE coefficient of variation scores (column 2) for each population size (1).</li> </ul> Supplementary figure 6: <ul> <li>*_SMC_SE.csv - SMC++ results (from 25 subsampled replicates) for each school subpopulation and outgroup samples. </li> </ul> Supplementary Figure 7: <ul> <li>smcpp.csv - SMC++ results for each school subpopulation and outgroup samples. </li> </ul> Supplementary Figure 8a-d <ul> <li>pi.per_host.txt.zip - Nucleotide diversity values for each host infrapopulation. </li> </ul> Supplementary Figure 9: <ul> <li>sexing.csv - inferred sex (based on differential read coverage over pseudoautosomal and Z-specific regions of the Z chromosome). </li> </ul> Supplementary Figure 10b: <ul> <li>sfs_res.csv - residuals for the SFS analysis in 3a/10a.</li> </ul> Supplementary Figure 11: <ul> <li>MAYUGE_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Mayuge population in 2kb windows. </li> <li>Tororo_TAJIMA_D.Tajima.D.2kb.txt.zip - Tajima's D values calculated for the Tororo population in 2kb windows. </li> </ul> Supplementary Figures 13-18: <ul> <li>genes.bed - Coordinates of gene models (S. mansoni v7 annotation).</li> <li>KOCOGE_SITE_PI.sites.pi.txt.zip - Per site nucleotide diversity values</li> <li>MAYUGE_TORORO_sites.weir.fst.txt.zip - Per site FST values between Mayuge and Tororo populations. </li> <li>coverage_5kb.windows.txt.zip - Per sample depth of read coverage in 5 kb windows. Columns 4,5,6 represent the median, mean and sstev of coverage for each 5kb window (columns 2&3) along each chromosome (column 1). </li> <li>median.sample.coverage.txt -  Median chromosomal depth of read coverage for each sample. </li> </ul> Supplementary Figure 19: <ul> <li>kocoge_median.ld.txt.zip -  - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Kocoge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> <li>mayuge_median.ld.txt.zip - The decay of linkage disequilibrium with genomic distance between all sites within 50 kb for the Mayuge parasite samples. Chromosomes are shown in column 1, distance in column 2, median values in column 3. </li> </ul> Misc files: schools.list - List of samples and schools where they were sampled.

Fig. 4 in Surprising genomic diversity in the Neotropical fish Synbranchus marmoratus (Teleostei: Synbranchidae): how many species?

Fig. 4. Cluster analysis based on karyotypes and genome sizes. The vertical bars on the right-hand side of the figure illustrate the closeness of samples found in different rivers and appearing in the same branch of the cluster (cf. Fig. 3). Letters A-E indicate cytotypes described in Fig. 2.

Fig. 3 in Surprising genomic diversity in the Neotropical fish Synbranchus marmoratus (Teleostei: Synbranchidae): how many species?

Fig. 3. Nuclear DNA content per individual (pg, + 95% confidence interval) among the sampled fishes. Rectangles include individuals with the same karyotype and dotted lines within rectangles subdivide samples into groupings of individuals with similar nuclear DNA contents.

Fig. 2 in Surprising genomic diversity in the Neotropical fish Synbranchus marmoratus (Teleostei: Synbranchidae): how many species?

Fig. 2. Five different cytotypes found among the samples of Synbranchus marmoratus analyzed. A – from the samples coded as PR, PR, and MS; B – from the samples coded as 2 3 2 SP and SP; C – from the sample coded as PR; D – karyotype 2 3 1

Fig. 1 in Surprising genomic diversity in the Neotropical fish Synbranchus marmoratus (Teleostei: Synbranchidae): how many species?

Fig. 1. South America map showing the major river drainages. The detail show the collecting locations. The bold dashed line in the detail indicates the limit of the last great marine incursion into South America (from the south) at approximately five million years ago (modified from Frailey, 2002). The lighter dashed lines indicate the state-specific boundaries hosting the collecting locations. MS 1,2 = rio Miranda (state of Mato Grosso do Sul; 2n=46 and 2n=42); SP 1 = rio Mogi-Guaçu (state of São Paulo; 2n=44); SP 2 = rio Tietê (state of São Paulo; 2n=42); SP 3 = rio Paraná (state of São Paulo; 2n=42); PR 1 = ribeirão Água do Caixão (state of Paraná; 2n=46); PR = rio Tibagi (state of Paraná; 2n=42); PR = rio Paraná (state of Paraná; 2n=42).

opencc-by-4.0Sep 2021View details →

A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples

Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture. Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38. The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. Tree sequences can be decompressed as follows: <pre><code>$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz</code></pre> Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>.  <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> Accessing variant sites in the tree sequence provides the position and id of variants: <pre><code>import json site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use: <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example, <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.

opencc-by-4.0Nov 2022View details →

Balancing selection at a wing pattern locus is associated with major shifts in genome-wide patterns of diversity and gene flow

Selection shapes genetic diversity around target mutations, yet little is known about how selection on specific loci affects the genetic trajectories of populations, including their genome-wide patterns of diversity and demographic responses. Here we study the patterns of genetic variation and geographic structure in a neotropical butterfly, Heliconius numata, and its closely related allies in the so-called melpomene-silvaniform clade. H. numata is known to have evolved an inversion supergene which controls variation in wing patterns involved in mimicry associations with distinct groups of co-mimics. Butterflies show disassortative mate preferences and heterozygote advantage at this locus. We contrasted patterns of genetic diversity and structure 1) among extant polymorphic and monomorphic populations of H. numata, 2) between H. numata and its close relatives, and 3) between ancestral lineages. We show that H. numata populations which carry the inversions as a balanced polymorphism show markedly distinct patterns of diversity compared to all other taxa. They show the highest genetic diversity and effective population size estimates in the entire clade, as well as a low level of geographic structure and isolation by distance across the entire Amazon basin. By contrast, monomorphic populations of H. numata as well as its sister species and their ancestral lineages all show lower effective population sizes and genetic diversity, and higher levels of geographical structure across the continent. One hypothesis is that the large effective population size of polymorphic populations could be caused by the shift to a regime of balancing selection due to the genetic load and disassortative preferences associated with inversions. Testing this hypothesis with forward simulations supported the observation of increased diversity in populations with the supergene. Our results are consistent with the hypothesis that the formation of a supergene triggered a change in gene flow, causing a general increase in genetic diversity and the homogenisation of genomes at the continental scale.

opencc-zeroJan 2023View details →

Whole genome demographic models indicate divergent effective population size histories shape contemporary genetic diversity gradients in a montane bumble bee

Understanding historical range shifts and population size variation provides important context for interpreting contemporary genetic diversity. Methods to predict changes in species distributions and model changes in effective population size (Ne) using whole genomes make it feasible to examine how temporal dynamics influence diversity across populations. We investigate Ne variation and climate-associated range shifts to examine the origins of a previously observed latitudinal heterozygosity gradient in the bumble bee Bombus vancouverensis Cresson (Hymenoptera: Apidae: Bombus Latreille) in western North America. We analyze whole genomes from a latitude-elevation cline using sequentially Markovian coalescent models of Ne through time to test whether relatively low diversity in southern high-elevation populations is a result of long-term differences in Ne. We use Maxent models of the species range over the last 130,000 years to evaluate range shifts and stability. Ne fluctuates with climate across populations, but more genetically diverse northern populations have maintained greater Ne over the late Pleistocene and experienced larger expansions with climatically favorable time periods. Northern populations also experienced larger bottlenecks during the last glacial period which matched the loss of range area near these sites, however, bottlenecks were not sufficient to erode diversity maintained during periods of large Ne. A genome sampled from an island population indicated a severe postglacial bottleneck, indicating that large recent post-glacial declines are detectable if they have occurred. Genetic diversity was not related to niche stability or glacial-period bottleneck size. Instead, spatial expansions and increased connectivity during favorable climates likely maintain diversity in the north while restriction to high elevations maintains relatively low diversity despite greater stability in southern regions. Results suggest genetic diversity gradients reflect long-term differences in Ne dynamics and also emphasize the unique effects of isolation on insular habitats for bumble bees. Patterns are discussed in the context of conservation under climate change.

opencc-zeroAug 2023View details →

Population genomic evidence that stream networks structure genetic diversity in the narrowly endemic patch-nosed salamander (Urspelerpes brucei)

Described in 2009, the Patch-nosed Salamander (Urspelerpes brucei) is a miniature species of lungless salamander with a geographic range of only ~45 km2. This species is endemic to the foothills of the Appalachian Mountains in extreme northeastern Georgia and northwestern South Carolina. The Tugaloo River—a waterway of some 50 m in width that forms the political boundary between the two states—bisects the tiny range of U. brucei and likely acts as a barrier to gene flow. Using RADcap data and a suite of complementary population genomic analyses, we evaluated the role that this river and its tributaries may play in enabling and/or interrupting gene flow among populations of U. brucei, and we investigated patterns of within-population and between-population genetic variation. Our results revealed a general pattern of isolation-by-stream distance and indicated that a population separated by the Tugaloo River is moderately more differentiated than what is explainable by stream distance alone. Unique in both its physiography and geologic history, this region in which U. brucei lives also harbors more than a dozen other species of lungless salamanders. Therefore, the genetic patterns that we have elucidated may have larger implications for differentiation among populations of other species with similar dispersal abilities.