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Record W2966361747 · doi:10.3389/fgene.2019.00665

Comparative Genomic Analysis of Three Salmonid Species Identifies Functional Candidate Genes Involved in Resistance to the Intracellular Bacterium Piscirickettsia salmonis

2019· article· en· W2966361747 on OpenAlexafffundabout
José M. Yáñez, Grazyella Yoshida, A Parra, Katharina Correa, Agustin Barría, Liane N. Bassini, Kris A. Christensen, María E. López, Roberto Carvalheiro, Jean P. Lhorente, Rodrigo Pulgar

Bibliographic record

VenueFrontiers in Genetics · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAquaculture disease management and microbiota
Canadian institutionsFisheries and Oceans Canada
FundersFondo de Fomento al Desarrollo Científico y TecnológicoComisión Nacional de Investigación Científica y TecnológicaFundação de Amparo à Pesquisa do Estado de São PauloConselho Nacional de Desenvolvimento Científico e TecnológicoGenome British ColumbiaGovernment of CanadaMinisterio de Economía, Fomento y Turismo, ChileUniversidad de ChileGenome Canada
KeywordsBiologySalmoGeneticsSingle-nucleotide polymorphismSNPCandidate geneGeneZoologyGenotypeFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

Piscirickettsia salmonis is the etiological agent of Salmon Rickettsial Syndrome (SRS), and is responsible for considerable economic losses in salmon aquaculture. The bacteria affect coho salmon (CS) (Oncorhynchus kisutch), Atlantic salmon (AS) (Salmo salar) and rainbow trout (RT) (Oncorhynchus mykiss) in several countries, including: Norway, Canada, Scotland, Ireland and Chile. We used Bayesian genome-wide association (GWAS) analyses to investigate the genetic architecture of resistance to P. salmonis in farmed populations of these species. Resistance to SRS was defined as the number of days to death (DD) and as binary survival (BS). A total of 828 CS, 2,130 RT and 2,601 AS individuals were phenotyped and then genotyped using ddRAD sequencing, 57K SNP Affymetrix® Axiom® and 50K Affymetrix® Axiom® SNP panels, respectively. Both trait of SRS resistance in CS and RT, appeared to be under oligogenic control. In AS there was evidence of polygenic control of SRS resistance. To identify candidate genes associated with resistance, we applied a comparative genomics approach in which we systematically explored the complete set of genes adjacent to SNPs which explained more than 1% of the genetic variance of resistance in each salmonid species (533 genes in total). Thus, genes were classified based on the following criteria: i) shared function of their protein domains among species, ii) shared orthology among species, iii) proximity to the SNP explaining the highest proportion of the genetic variance and, iv) presence in more than one genomic region explaining more than 1% of the genetic variance within species. Our results allowed us to identify 120 candidate genes belonging to at least one of the four criteria described above. Of these, 21 of them were part of at least two of the criteria defined above and are suggested to be strong functional candidates influencing P. salmonis resistance. These genes are related to diverse biological processes, such as: kinase activity, GTP hydrolysis, helicase activity, lipid metabolism, cytoskeletal dynamics, inflammation and innate immune response, which seem essential in the host response against P. salmonis infection. These results provide fundamental knowledge on the potential functional genes underpinning resistance against P. salmonis in three salmonid species.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.216
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2019
Admission routes3
Has abstractyes

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