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opencc-by-4.0Nov 2023View details →

Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Data accompanying the manuscript "Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation". Note: For ATAC fragment files, e,caQTL full cis scan summary files, clustering objects, please see the CMDGA portal (https://cmdga.org/search/?searchTerm=stephen-parker%3AVarshney2024) For raw data including fastq files, please see dbGaP repo phs001048.v3.p1 Data in this repository includes: Filename: Description 1. list of 8,666 genes for which exon-only counts were considered. See methods section "Adjusting RNA counts for overlapping gene annotations" in the manuscript. 2. nucleus_sample_cluster_map.tsv: nucleus-sample-cluster map with other QC info.  # index: nucleus identified syntax <modality>.<batch>.NM.<10X channel>.<barcode>  # UMAP_1, UMAP_2: UMAP coordinates for visualization # modality: rna or atac # batch: processing batch identifier # hqaa_umi: high quality autosomal alignments (HQAA) for atac nuclei, unique molecular identifier (UMI) for tna  # fraction_mitochondrial: fraction of reads mapping to the mitochondrial genome # cohort: sample cohort # tss_enrichment: TSS enrichment for atac nuclei # coarse_cluster_name: cluster name 3. peaks.tar.gz: snATAC peak features including: # consensus-summits.bed: consensus summits along with the cell type that the summits was highest in. # narrow peaks in clusters # consensus summit feature (summit +- 150bp) identified in each cluster - these were used in GWAS enrichments. 4. snrna-cell-type-specific-genes.tsv: Normalized expression scores for genes in each cell-type cluster 5. eqtl_permute.tar.gz: Permutation scan eQTL in each cell-type cluster. Columns:  # variant: syntax <chrom>:<hg38 pos>:<ref>:<alt> # effect_allele: effect allele (was the alt allele) # other_allele: non-effect allele # feature: gene name # featureCoordinates_tss: gene TSS # p-value: nominal p value # beta: slope/beta of the linear regression. Keyed on the alt allele # se: standard error of the slope # snp: SNP ID # strand: gene strand # n_variants_tested: number of variants tested for the gene # distance_var_pheno: distance of the variant with the gene TSS # n_effective_tests: number of effective tests # p_beta: beta distribution adjusted p value # qvalue: qvalue (Storey) 6. caqtl_permute.tar.gz: # Permutation scan caQTL in each cell-type cluster. Columns:  # variant: syntax <chrom>:<hg38 pos>:<ref>:<alt> # effect_allele: effect allele (was the alt allele) # other_allele: non-effect allele # feature: peak feature coordinates # p-value: nominal p value # beta: slope/beta of the linear regression. Keyed on the alt allele # se: standard error of the slope # snp: SNP ID # n_variants_tested: number of variants tested for the gene # distance_var_pheno: distance of the variant with the gene TSS # n_effective_tests: number of effective tests # p_beta: beta distribution adjusted p value # qvalue: qvalue (Storey) 7. eqtl_credible_sets.tar.gz: # eQTL credible set. The file name denotes the egene and the signal hit id. Bed file columns:  # 1: snp chromosome # 2: snp start # 3: snp end # 4: snp chrom_pos_ref_alt # 5: Bayes Factor  # 6: PIP # 7: SNP rsid 8. caqtl_credible_sets.tar.gz: # caqtl credible set. The file name denotes the capeak and the signal hit id. Bed file columns:  # 1: snp chromosome # 2: snp start # 3: snp end # 4: snp chrom_pos_ref_alt # 5: Bayes Factor  # 6: PIP # 7: SNP rsid 9. cicero_all.tar.gz # Cicero coaccessibility results. Columns # Peak 1: Macs2 narrowpeak coordinate for peak 1 # Peak 2: Macs2 narrowpeak coordinate for peak 2 # coaccess: Cicero coaccessibility score 10. cicero_gene_tss.tar.gz: Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name. Columns # Cicero coaccessibility results between peak and genes. Macs2 narrow peaks in the TSS+1kb upstream region are assigned that gene name.Columns # Peak 1: Macs2 narrowpeak coordinate for peak 1 # gene_name: Assigned gene # Peak 2: Macs2 narrowpeak coordinate for peak 2 # coaccess: Cicero coaccessibility score ##  Peak1 is the narrowpeak in the TSS region, peak2 is the distal peak 11. mash.tar.gz Mashr results for e/caQTL - lfsr, posterior means and posterior SD for each tested eSNP-eGene, caSNP-caPeak pair.  12. cellregmap.tar.gz: Cellregmap results for endothelial nucleus-level eQTL scans. ## Persistent genetic effect beta_g was calculated in a simple association model.  ## An interaction model was fit to test for GxC effect. columns: # rho1, g2, e1, and eps2 are variance component measures outputs from CellRegMap corresponding to interaction, genetic, environment and residual variance components.  # p_nominal: nominal p from cellRegMap # kind: model kind in CellRegMap - simple association or interaction # beta_g:  Persistent genetic effect # gene_name: gene name for eQTL or peak feature name for caQTL # context: context used either factors (continuous) or subclusters (discrete) # snp: index snp for which model is fit. This is the most significant identified snp from our standard e,caQTL scans. chrom-hg38pos-rsid 13. coloc-eqtl-caqtl.tsv: # Summary of eQTL-caQTL coloc in each cluster. Columns: # nsnps: Number of SNPs in the region # eqtl_hit: SNP with the highest Bayes factor in the SuSiE eQTL credible set # caqtl_hit: SNP with the highest Bayes factor in the SuSiE caQTL credible set # PP.H0.abf: Coloc posterior probability for no signal # PP.H1.abf: Coloc posterior probability for signal in dataset 1 # PP.H2.abf: Coloc posterior probability for signal in dataset 2 # PP.H3.abf: Coloc posterior probability for different signals in datasets 1 and 2 # PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 # idx1: Index of the SuSiE credible set for dataset 1 # idx2: Index of the SuSiE credible set for dataset 2 # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates 14. cit-mrs-summary.tsv:  Summary from CIT and MR Steiger directionality tests. Columns: # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates # eqhit: SNP with the highest Bayes factor in the SuSiE eQTL credible set # cahit: SNP with the highest Bayes factor in the SuSiE caQTL credible set # p.cit_c_c-e: P value for CIT causal cahit-ca-to-e model # q.cit_c_c-e: q value for CIT causal cahit-ca-to-e model # p.cit_rc_c-e: P value for CIT reverse-causal eqhit-ca-to-e model  # q.cit_rc_c-e: value for CIT reverse-causal eqhit-ca-to-e model  # p.cit_c_e-c: P value for CIT causal eqhit-e-to-ca model # q.cit_c_e-c: q value for CIT causal eqhit-e-to-ca model # p.cit_rc_e-c: P value for CIT reverse-causal cahit-e-to-ca model  # q.cit_rc_e-c: q value for CIT reverse-causal cahit-e-to-ca model  # cit_direction: Direction inferred from CIT   # correct_causal_direction--ca-to-e: MR Steiger directionality test - is ca-to-e direction correct? # correct_causal_direction--e-to-ca: MR Steiger directionality test - is e-to-ca direction correct? # sensitivity_ratio--ca-to-e: MR Steiger Sensitivity ratio for ca-to-e model  # sensitivity_ratio--e-to-ca:  MR Steiger Sensitivity ratio for e-to-ca model # steiger_test--ca-to-e: MR Steiger directionality test P value for ca-to-e model # steiger_test--e-to-ca: MR Steiger directionality test P value for e-to-ca model # steiger_q--ca-to-e: MR Steiger directionality test q value for ca-to-e model # steiger_q--e-to-ca: MR Steiger directionality test q value for e-to-ca model # mrs_direction: Direction inferred from MR Steiger # direction: Direction inferred requiring consistent results between CIT and MR Steiger directionality test 15. coloc-gwas-eqtl.tsv and 16. coloc-gwas-caqtl.tsv # Summary of e/caQTL coloc with GWAS in each cluster. Columns: # nsnps: Number of SNPs in the region # gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set # eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set # caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set # PP.H0.abf: Coloc posterior probability for no signal # PP.H1.abf: Coloc posterior probability for signal in dataset 1 # PP.H2.abf: Coloc posterior probability for signal in dataset 2 # PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2 # PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 # idx1: Index of the SuSiE credible set for dataset 1 # idx2: Index of the SuSiE credible set for dataset 2 # cluster: cluster name # egene: eGene name # capeak: caPeak coordinates # p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization # trait: GWAS trait name # gwas_locus: GWAS locus name for the coloc test - a 250kb left and right flanking genomic window on this SNP was considered for testing coloc between all pairs of GWAS/QTL signals identified in this region   # traitname: Expanded GWAS trait name # variable_type: GWAS type  # source: Source of GWAS - either UKBB or other study 17. supplementary_tables.xlsx: Supplementary tables from the manuscript. Information included in sheets: 1. "marker_genes": Marker genes known from literature used to annotate clusters 2. "n_nuclei": n pass-QC nuclei per modality-sample-cluster 2. "snrna_GO_enrichment": GO term enrichment: matrix of cluster vs top 2 GO terms 3. "qtl_scan_info":  e/caQTL scan info cluster: cluster ntested_eqtl: N genes tested for eQTL nsig_eqtl: N significant (5% FDR) eGenes n_pheno_pcs_eqtl: N phenotype PCs considered for eQTL ratio_eqtl: Ratio of N eGenes/N genes tested nsig_caqtl:  N peaks tested for caQTL ntested_caqtl: N significant (5% FDR) caPeaks n_pheno_pcs_caqtl: N phenotype PCs considered for caQTL ratio_caqtl: Ratio of N caPeaks/N peaks tested nsamples_eqtl: N samples for eQTL nsamples_caqtl: N samples for caQTL 4. "gwas_trait_list": GWAS trait info trait: GWAS trait ID traitname: GWAS trait description variable_type: GWAS type. case/control (cc), continuous_irnt=continuous inverse-normal transformed source: GWAS source doi: GWAS study DOI 5. "traits_in_ldsc_baseline" - list of annotations included in the baseline model for LDSC 6. "gwas_enrichment_in_peaks" GWAS enrichment in cluster peaks (S-LDSC) 7. "gwas_enrichment_in_qtl_peaks" GWAS enrichment in QTL peaks (fGWAS) # fGWAS results comparing GWAS enrichment in type 1 annotations CI_lower_ln, estimate_ln, CI_upper_ln: natural log of lower confidence interval, estimate, and upper confidence interval trait: trait id traitname: trait name annotation: annotation sig: 1 if CIs don't overlap 0, otherwise 0 8. t2d_gwas_caqtl_coloc and 9. t2d_gwas_eqtl_coloc: Summary of e,caQTL coloc with T2D GWAS in each cluster, along with target gene nominations. Columns: nsnps: Number of SNPs in the region gwas_hit: SNP with the highest bayes factor in the SuSiE GWAS credible set eqtl_hit: SNP with the highest bayes factor in the SuSiE eQTL credible set caqtl_hit: SNP with the highest bayes factor in the SuSiE caQTL credible set PP.H0.abf: Coloc posterior probability for no signal PP.H1.abf: Coloc posterior probability for signal in dataset 1 PP.H2.abf: Coloc posterior probability for signal in dataset 2 PP.H3.abf: Coloc posterior probability for different signal in datasets 1 and 2 PP.H4.abf: Coloc posterior probability for shared signal in datasets 1 and 2 idx1: Index of the SuSiE credible set for dataset 1 idx2: Index of the SuSiE credible set for dataset 2 cluster: cluster name egene: eGene name capeak: caPeak coordinates p12min: Min prior p12 where the PP H4 > 0.5. Lower this value, more robust is the colocalization trait: GWAS trait id diamante_gwas_locus: GWAS signal from the DIAMANTE 2018 study. Some signals that our SuSiE runs identified were not present in the original study in which case this column is NA traitname: Expanded GWAS trait name capeak_in_tss: caPeak in TSS + 1kb upstream region of a gene gene_target_standard_cicero: caPeak coaccessible with TSS peak of a gene considering nuclei from all samples for co-accessibility gene_target_allelic_cicero:  caPeak coaccessible with TSS peak of a gene considering nuclei from samples homozygous for the caSNP allele associated with increased accessibility gwashit_nominal_egene: gwas_hit nominally associated with these genes nominated in the columns capeak_in_tss, gene_target_standard_cicero, and  gene_target_allelic_cicero 10. MPRA results for the C2CD4A locus

opencc-by-4.0Jul 2024View details →

Snapgene files of the genetic constructions for Morella thermoacetica used in Task 2.2 (Advanced GMO preparation for gas (CO2/CO/H2) fermentation process)

Part of WP2 these snapgene files of the genetic constructions  for Morella thermoacetica were used in Task 2.2 (Advanced GMO preparation for gas (CO2/CO/H2) fermentation process). The strains we were able to construct were: 1.- M. thermoacetica DSM 521 pK18ACSACassette complete 2.- M. thermoacetica DSM 521 pK18ACSACassette AatA 3.- M. thermoacetica DSM 2955 pK18ACSACassette PudL-AckA 4.- M. thermoacetica DSM 2955 pK18ACSACassette AatA To construct these strains CSIC used a modular synthetic construction carrying the regions up and down of the acsA gene to delete it. Within these regions, a gene cluster composed by the GmR, pduL, ackA and aatA genes, all of them controlled by the strong constitutional promoter of the glyceraldehyde 3-phosphate dehydrogenase gene from Moorella were inserted. The modular design of our recombinant cassette allows us to construct different cassettes with different gene combinations. In all cases, the acsA gene will be deleted and the acetate cannot be transformed back into acetyl-CoA: Cassette 1.- ACSA-GmR-pduL-ackA-aatA-ACSA: this is a complete cassette which carries the genes encoding the phosphotransacetylase, the acetate kinase and the acetate exporter. This strain should accumulate a higher concentration of acetate inside the cell that should be exported to the medium. Cassette 2.- ACSA-GmR-pduL-ackA-ACSA: this cassette lacks the transporter, so this strain should accumulate a higher concentration inside the cell. Then, the only way to prevent acetate toxicity is by increasing its own mechanisms to export this compound or by growing slower in order to manage acetate accumulation. Cassette 3.- ACSA-GmR-aatA-ACSA: this cassette only carries the acetate exporter, so virtually all the acetate produced inside the cell should be exported. This is another strategy to increase the metabolic flux inside the cell towards acetate without overexpressing any other gene of the acetate pathway, and at the same time avoiding toxicity problems. Cassette 4.- ACSA-GmR-ACSA: this cassette would generate an insertion mutant in which acsA is only replaced by the GmR encoding gene. This mutant should also generate a higher amount of acetate since it cannot be transformed back to acetyl-CoA.

opencc-by-4.0Oct 2023View details →

Georeferenced data for the study Environmental suitability throughout the late Quaternary explains population genetic diversity

Data filtered from GBIF (datasetKey: 50c9509d-22c7-4a22-a47d-8c48425ef4a7)  Contains 150 records of the Sciurus aberti squirrel filtered in latitudinal windows of 5 degrees from 20 to 45 degrees N.

opencc-zeroOct 2023View details →

Data from: The genetic basis of traits associated with the evolution of serpentine endemism in monkeyflowers

The floras on chemically and physically challenging soils, such as gypsum, shale, and serpentine, are characterized by narrowly endemic species. The evolution of edaphic endemics may be facilitated or constrained by genetic correlations among traits contributing to adaptation and reproductive isolation across soil boundaries. The yellow monkeyflowers in the Mimulus guttatus species complex are an ideal system in which to examine these evolutionary patterns. To determine the genetic basis of adaptive and prezygotic isolating traits, we performed genetic mapping experiments with F2 hybrids derived from a cross between a serpentine endemic, M. nudatus, and its close relative M. guttatus. Few large effect and many small effect QTL contribute to interspecific divergence in life history, floral and leaf traits, and a history of directional selection contributed to trait divergence. Loci contributing to adaptive traits and prezygotic reproductive isolation overlap, and their allelic effects are largely in the direction of species divergence. These loci contain promising candidate genes regulating flowering time and plant organ size. Together our results suggest that genetic correlations among traits can facilitate the evolution of adaptation and speciation and may be a common feature of the genetic architecture of divergence between edaphic endemics and their widespread relatives.

opencc-zeroOct 2023View details →

Genetic variation in Loudetia simplex supports the presence of ancient grasslands in Madagascar

<div>Summary</div> <div> </div> <div> Research Aims - The extent of Madagascar's grasslands prior to human colonization is unresolved. We used population genetic analyses of a broadly dominant C4 fire-adapted grass, Loudetia simplex, as a proxy for estimating grassland change through time. We carefully examined the utility of target-enrichment data for population genetics to make recommendations for conservation genetics. We explored the potential of estimating individual ploidy levels from target-enrichment data and how assumptions about ploidy could affect analyses.</div> <div> </div> <div> Methods - We developed a novel bioinformatic pipeline based to estimate ploidy and genotypes from target-enrichment data. We estimated standard population genetic summary statistics in addition to species trees and population structure. Extended Bayesian skyline plots provided estimates of population size through time for empirical and simulated data.</div> <div> </div> <div> Key Result - All Malagasy Loudetia simplex individuals sampled in this study formed a clade and possibly indicated an ancestral high-altitude distribution. Demographic models suggested grassland expansions occurred prior to the Last Interglacial Period and supported extensive grasslands prior to human colonization. Though there are limitations to target-enrichment data for population genetic studies, we find that analyses of population structure are reliable.</div> <div> </div> <div> Key Point - Genetic variation in Loudetia simplex supports widespread grasslands in Madagascar prior to the more recent periods of notable paleoclimatic change. However, the methods explored here could not differentiate between paleoclimatic change near the Last Glacial Maximum and anthropogenic effects. Target-enrichment data can be a valuable tool for analyses of population structure in the absence a reference genome.</div> <div> </div> <div>Societal Impact Statement</div> <div> </div> <div>Recognizing Loudetia dominated grasslands were widespread prior to human colonization highlights that open ecosystems were and continue to be an important component to Madagascar's biodiversity. Urgently required are biodiversity inventories and risk assessments for unique grassland flora and fauna under present day environmental conditions to recognize and quantify modern human impacts within ecosystems historically regarded as wastelands. Substantial financial and logistic barriers exist to implementing conservation studies using contemporary genomic tools that we seek to ameliorate by developing computational resources to leverage a cost-effective data generation strategy that requires no prior genetic knowledge of the target species.</div>

opencc-zeroOct 2023View details →

Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue

Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.

opencc-by-4.0Jun 2022View details →

Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics (Dataset)

Complete dataset of Clock and Adcyap1 alleles, distance matrices, and migration data used in the review and meta-analysis "Time trees and Clock genes: a Systematic Review and Comparative Analysis of Contemporary Avian Migration Genetics".

Figure 8 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 8. Daphnia tanakai sp. nov., male from Lake Midori-ga-ike, Japan. A, lateral view. B, caudal spine. C, head. D, E, armature of antero-ventral and posterior portion of valve. F, G, postabdomen and postabdominal claw. H, male antenna I. I, tip of male seta ('flagellum') on antenna I. J, K, limb I and its distal portion. L–O, distal-most endite of limb II.

Figure 7 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 7. Daphnia tanakai sp. nov., thoracic limbs of parthenogenetic female from Lake Midori-ga-ike, Japan. A, B, limb I. C, D, anterior seta on its endite 3 and 2. E, limb II. F, G, stiff seta on its inner-distal end. H, gnathobase II. I, J, limb III and its inner-distal portion. K, L, limb IV and its inner-distal portion. M, N, limb V and distal portion of its exopodite.

Figure 6 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 6. Daphnia tanakai sp. nov. from Lake Midori-ga-ike, collected on August 30, 2004 by S. Tanaka (A–F, K–O) and Lake Kagami-ike, collected on September 01, 2004 by S. Tanaka (G–J, P–R); both lakes are in Hida Mountain Range, Honshu Island, Japan. A, parthenogenetic female, lateral view. B, head of parthenogenetic female. C, D, armature of postero-ventral and posterior region of valve. E, postabdomen. F–I, postabdominal claws of adults. J, postabdominal claw of juvenile. K, L, antenna I in lateral and distal view. M, N, distal portion of basal segment in posterior and anterior view. O, swimming seta. P, Q, ephippial female and postero-dorsal portion of its carapace. R, ephippium.

Figure 3 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 3. Daphnia curvirostris, large parthenogenetic female from Lake Glubokoe, Moscow area, European Russia, collected on August 9, 2004 by AAK. A, lateral view. B, caudal spine. C–E, head. F, G, armature of postero-ventral and posterior region of valve. H, postabdomen. I, J, postabdominal claw. K, L, antenna I in lateral and posterior view.

Figure 5 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 5. Daphnia curvirostris from Lake Glubokoe, Moscow area, European Russia, collected on September 9, 2004 by N. N. Smirnov. A, B, ephippial female and its postero-dorsal region. C, fresh ephippium. D, adult male. E, male head. F, G, armature of ventral margin of valve. H, armature of posterior portion of valve. I, J, postabdomen and abdomen. K, antenna I. L, antenna II. M, N, limb I and its distal portion. O, armature of distal portion of largest seta of outer distal lobe. P, innerdistal portion of limb II.

Figure 2 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 2. Mapping the characters of chromosome number and postabdominal claw morphology onto the Daphnia ND2 consensus tree (Fig. 1). A, the left cladogram shows the evolution of chromosome number. Black line denotes 2n = 22, white line denotes 2n = 20 and dot line denotes 2n = 24. B, the right cladogram shows the evolution of postabdominal claw morphology. Black line denotes variable phenotype between the longispina-claw and pulex-claw types, white line denotes the longispina- claw type, dot line denotes the pulex-claw type and grey line denotes equivocal.

Figure 4 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 4. Daphnia curvirostris, appendages of parthenogenetic female from Lake Glubokoe, European Russia. A, coxal part of antenna II. B, distal portion of basal segment and basal portion of branches. C, distal portion of endopod. D, swimming seta. E, maxilla I. F, limb I: ODL indicates outer distal lobe; IDL indicates inner distal lobe. G–I, limb II, second seta on its inner-distal end, and gnathobase II. J–L, limb III, its inner-distal portion and filtering seta of gnathobase. M, N, limb IV and its inner-distal portion. O, limb V.

Figure 1 in A new divergent lineage of Daphnia (Cladocera: Anomopoda) and its morphological and genetical differentiation from Daphnia curvirostris Eylmann, 1887

Figure 1. ME bootstrap consensus tree of Daphnia ND2 sequences. The numbers on each branch show support values of the branch. Upper numbers indicate ME, and ML bootstrap support values for nucleotide sequences. Middle numbers indicate MP bootstrap support values and Bayesian clade credibility values for nucleotide sequences. Lower numbers indicate MP bootstrap support values and Bayesian clade credibility values for amino acid sequences. Asterisks indicate no support values.

opencc-by-4.0Nov 2023View details →

Germline CpG methylation signatures in the human population inferred from genetic polymorphism

This repository contains data released accompanying the manuscript "Germline CpG methylation signatures in the human population inferred from genetic polymorphism".

opencc-zeroNov 2023View details →

Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients

Background: Understanding the mechanisms by which genes control and regulate complex quantitative traits during periods of fluctuating resources remains a challenging and uncertain task in photosynthesis studies. Most studies have focused on the structure of photosynthesis, the photosynthetic response under stress, or the genetic mechanisms involved in photosynthetic effects and neglected the interactive genetic mechanism that governs various traits through significant quantitative trait loci (QTLs). Results In this study, we have developed a differential dynamic system that enables the identification of QTLs based on the photosynthetic phenotypic and genotypic data under varying levels of light intensity gradients. The framework not only allows for the assessment of the direct effects of QTLs on phenotypes but also captures how they influence interactions among phenotypes as light intensities change. We have analyzed the genetic effects and genetic variance, visualized the genetic network associated with photosynthesis interactions, and validated the effectiveness and stability of the DDS framework. Pivotal QTLs were identified individually to uncover the process and pattern of interaction. Through functional annotation, we made an intriguing discovery that seemingly unimportant QTLs can still have significant genetic effects on phenotypic changes through their regulation with other QTLs. Conclusions This finding emphasizes the significance of considering the interactive genetic architecture when seeking to understand the genetic interaction mechanism of photosynthesis in natural populations of woody plants. Moreover, our research provides a novel framework that can be extended to explore the interactive genetic architecture among organisms, contributing to a deeper understanding of stress resistance mechanisms in woody plants.

opencc-zeroDec 2023View details →

Data for: Genetic structuring and species boundaries in the Atlantic stony coral Favia (Scleractinia, Faviidae)

Scleractinian corals are the main modern builders of coral reefs, dynamic ecosystems that are hot spots of marine biodiversity. Southern Atlantic reef corals are understudied compared to their Caribbean and Indo-Pacific counterparts and many hypotheses about their population dynamics demand further testing. We employed thousands of single nucleotide polymorphisms (SNPs) recovered via ezRAD to characterize genetic population structuring and species boundaries in the amphi-Atlantic hard coral genus Favia. Coalescent-based species delimitation (BFD* - Bayes factor delimitation) recovered F. fragum and F. gravida as separate species. Although our results agree with depth-related genetic structuring in F. fragum, they did not support incipient speciation of the "tall" and "short" morphotypes. The preferred scenario revealed a split between two main lineages of F. gravida, one from Ascension Island and the other from Brazil. The Brazilian lineage is further divided into a species that occurs throughout the Northeastern coast and another that ranges from the Abrolhos Archipelago to the state of Espírito Santo. BFD* scenarios were supported by analysis of datasets with varying levels of missing data. Our results challenge current notions about Atlantic reef corals because they uncovered surprising genetic diversity in Favia and rejected the long-standing hypothesis that Abrolhos Archipelago may have served as a Pleistocenic refuge during the last glaciations.

opencc-zeroDec 2023View details →

Environmental effects on genetic variance are likely to constrain adaptation in novel environments

Adaptive plasticity allows populations to cope with environmental variation but is expected to fail as conditions become unfamiliar. In novel conditions, populations may instead rely on rapid adaptation to increase fitness and avoid extinction. Adaptation should be fastest when both plasticity and selection occur in directions of the multivariate phenotype that contain abundant genetic variation. However, tests of this prediction from field experiments are rare. Here, we quantify how additive genetic variance in a multivariate phenotype changes across an elevational gradient, and test whether plasticity and selection align with genetic variation. We do so using two closely related, but ecologically distinct, sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and low elevations on Mount Etna. Using a paternal half-sibling breeding design, we generated and then reciprocally planted c.19,000 seeds of both species, across an elevational gradient spanning each species' native elevation, and then quantified mortality and five leaf traits of emergent seedlings. We found that genetic variance in leaf traits changed more across elevations than between species. The high-elevation species at novel lower elevations showed changes in the distribution of genetic variance among the leaf traits, which reduced the amount of genetic variance in the directions of selection and the native phenotype. By contrast, the low-elevation species mainly showed changes in the amount of genetic variance at the novel high elevation, and genetic variance was concentrated in the direction of the native phenotype. For both species, leaf trait plasticity across elevations was in a direction of the multivariate phenotype that contained a moderate amount of genetic variance. Together, these data suggest that where plasticity is adaptive, selection on genetic variance for an initially plastic response could promote adaptation. However, large environmental effects on genetic variance are likely to reduce adaptive potential in novel environments.