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20 results for “genetic baseline”
Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
<p>In mixed-stock fishery analyses, genetic stock identification (GSI) estimates the contribution of each population to a mixture and is typically conducted at a regional scale using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines due to non-overlapping populations and genetic markers. In cases where the mixture contains stocks spanning across a wide area, a broad-scale baseline is created, but often at the cost of resolution. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analysis of mixtures containing individuals from a broad geographic range. This method employs a multistage framework that allows disparate baselines to be used in a single integrated process that produces estimates along with the propagated errors from each stage. All individuals in the mixture sample are required to be genotyped for all genetic markers in the baselines used by this model, but the baselines do not require overlap in genetic markers or populations representing the broad-scale or regional baselines.</p> <p>We demonstrate our integrated multistage GSI model using a synthesized data set made up of Chinook salmon, <em>Oncorhynchus tshawytscha</em>, from the North Bering Sea of Alaska. The data set is designed to be run using R package, Ms.GSI, and it does not represent the composition of the real fishery. The results show an improved accuracy for estimates using an integrated multistage framework, compared to the conventional framework of using separate hierarchical steps. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high-resolution genetic baseline or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all regionally important markers.</p>
A baseline for the genetic stock identification of Atlantic herring, Clupea harengus, in ICES Divisions 6.a, 7.b-c
<p>Atlantic herring in ICES Divisions 6.a, 7.b-c comprises at least three populations, distinguished by temporal and spatial differences in spawning, which have until recently been managed as two stocks defined by geographic delineators. Outside of spawning the populations form mixed aggregations, which are the subject of acoustic surveys. The inability to distinguish the populations has prevented the development of separate survey indices and separate stock assessments. A panel of 45 SNPs, derived from whole genome sequencing, were used to genotype 3,480 baseline spawning samples (2014-2021). A temporally stable baseline comprising 2,316 herring from populations known to inhabit Division 6.a was used to develop a genetic assignment method, with a self-assignment accuracy >90%. The long-term temporal stability of the assignment model was validated by assigning archive (2003-2004) baseline samples (270 individuals) with a high level of accuracy. Assignment of non-baseline samples (1,514 individuals) from Division 6.a, 7.b-c indicated previously unrecognised levels of mixing of populations outside of the spawning season. The genetic markers and assignment models presented constitute a 'toolbox' that can be used for the assignment of herring caught in mixed survey and commercial catches in Division 6.a into their population of origin with a high level of accuracy.</p>
Fig. 1 in Baseline study of the morphological and genetic characteristics of Haemoproteus parasites in wild pigeons (Columba livia) from paddy fields in Thailand
Fig. 1. Haemoproteus columbae from the blood of wild pigeons (Columba livia); cytochrome b lineage HAECOL1 (a–d), COLIV03 (e–f) and COQUI05 (i–l); macrogametocytes (a–b; e–f; i–j) and microgametocytes (c–d; g–h; k–l); simple arrows: nuclei of erythrocytes; short triangle-head arrows: volutin granules; long trianglehead arrows: pigment granules; scale bar = 10 μm.
Fig. 2 in Baseline study of the morphological and genetic characteristics of Haemoproteus parasites in wild pigeons (Columba livia) from paddy fields in Thailand
Fig. 2. Haplotype network of partial cyt b sequence (479 bp) of Haemoproteus columbae from wild pigeons (Columba livia); (A) the three common haplotypes (HAECOL1, COLIV03 and COQUI05) found in this study and proportions between study sites; (B) proportions of each haplotype reported worldwide and in the two study sites; Nakhon Sawan (purple), Phitsanulok (pink), and previous reports (gray); number of sequences shown in a pie chart of the network, without number inside indicates one sequence. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
A baseline for the genetic stock identification of Atlantic herring, Clupea harengus, in ICES Divisions 6.a, 7.b-c
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Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
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Data from: Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem
Chinook Salmon is an economically and ecologically important species, and populations from the west coast of North America are a major component of fisheries in the North Pacific Ocean. The anadromous life history strategy of this species generates populations (or stocks) that typically are differentiated from neighboring populations. In many cases, it is desirable to discern the stock of origin of an individual fish or the stock composition of a mixed sample to monitor the stock-specific effects of anthropogenic impacts and alter management strategies accordingly. Genetic stock identification (GSI) provides such discrimination, and we describe here a novel GSI baseline composed of genotypes from more than 8000 individual fish from 69 distinct populations at 96 single nucleotide polymorphism (SNP) loci. The populations included in this baseline represent the likely sources for more than 99% of the salmon encountered in ocean fisheries of California and Oregon. This new genetic baseline permits GSI with the use of rapid and cost-effective SNP genotyping, and power analyses indicate that it provides very accurate identification of important stocks of Chinook Salmon. In an ocean fishery sample, GSI assignments of more than 1000 fish, with our baseline, were highly concordant (98.95%) at the reporting unit level with information from the physical tags recovered from the same fish. This SNP baseline represents an important advance in the technologies available to managers and researchers of this species.
Data from: Evaluation of a single nucleotide polymorphism baseline for genetic stock identification of Chinook Salmon (Oncorhynchus tshawytscha) in the California Current Large Marine Ecosystem
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Pacific Rim Chinook salmon genetic stock identification baseline of SNP genotypes
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Atlantic herring population baseline (genotypes) for genetic stock identification
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Data from: Baseline and stress-induced corticosterone levels are heritable and genetically correlated in a barn owl population
The hypothalamic-pituitary-adrenal (HPA) axis is responsible for the regulation of corticosterone, a hormone that is essential in the mediation of energy allocation and physiological stress. As a continuous source of challenge and stress for organisms, the environment has promoted the evolution of physiological adaptations and led to a great variation in corticosterone profiles within or among individuals, populations and species. In order to evolve via natural selection, corticosterone levels do not only depend on the strength of selection exerted on them but also on the extent to which the regulation of corticosterone is heritable. Nevertheless, heritability of corticosterone profiles in wild populations is still poorly understood. In this study, we estimated the heritability of baseline and stress-induced corticosterone levels in barn owl (Tyto alba) nestlings from 8 years of data, using a multivariate animal model based on a behavioural pedigree. We found that baseline and stress-induced corticosterone levels are strongly genetically correlated (r = 0.68 – 0.80) and that the heritability of stress-induced corticosterone levels (h2 = 0.24 – 0.33) was moderate and similar to the heritability of baseline corticosterone levels (h2 = 0.19 – 0.30). These findings suggest that the regulation of stress-induced corticosterone and baseline levels evolve at a similar pace when selection acts with the same intensity on both traits, and that contrary to previous studies, the evolution of baseline and stress-induced level is interdependent in barn owls, as they may be strongly genetically correlated.
Oncorhynchus kisutch genetic stock ID SNP baseline
<p>Genetic stock identification (GSI) <span>using genotypes produced from genotyping-by-sequencing of SNP loci</span> has become the gold standard for stock identification in Pacific salmon, which are found in mixed stocks in the ocean. Sequencing platforms currently applied require large batch sizes and multi-day processing in specialized facilities to perform genotyping by the thousands. However, recent advances in third-generation single-molecule sequencing platforms, like the Oxford Nanopore minION, provide base calling on portable, pocket-sized sequencers and promise real-time, in-field stock identification on variable batch sizes. Here we evaluate utility and comparability to established GSI platforms of at-sea stock identification of coho salmon <i>Oncorhynchus kisutch</i> based on targeted SNP amplicon sequencing on the minION platform during a high-sea winter expedition to the Gulf of Alaska. As long read sequencers are not optimized for short amplicons, we concatenate amplicons to increase coverage and throughput. Nanopore sequencing at-sea yielded stock assignment for 50 of the 80 assessed individuals. Nanopore-based SNP calls agreed with Ion Torrent based genotypes in 83.25%, but assignment of individuals to stock of origin only agreed in 61.5% of individuals highlighting inherent challenges of Nanopore sequencing, such as resolution of homopolymer tracts and indels. However, poor representation of assayed coho salmon in the queried baseline dataset contributed to poor assignment confidence on both platforms. Future improvements will focus on lowering turnaround time, accuracy, throughput, and cost, as well as augmentation of the existing baselines. If successfully implemented, Nanopore sequencing will provide an alternative method to the large-scale laboratory approach by providing mobile small batch genotyping to diverse stakeholders.</p>
Supplementary material 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
Table S1 : Explanation note: Amphipod and outgroup accession numbers in BOLD, GenBank and station data.
Data from: Structure and genetic diversity of Ixora margaretae an endangered species: a baseline study for conservation and restoration of natural dry forest of New Caledonia
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Oncorhynchus kisutch genetic stock ID SNP baseline
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Data from: Baseline and stress-induced corticosterone levels are heritable and genetically correlated in a barn owl population
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Data from: Assessing current genetic status of the Hainan gibbon using historical and demographic baselines: implications for conservation management of species of extreme rarity
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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.
Genetic Variability in the Response to Warfarin at Baseline and During Induction of CYP2C9
ClinicalTrials.gov study NCT01388075. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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