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Record W2961123939 · doi:10.1371/journal.pone.0213818

Novel organ-specific genetic factors for quantitative resistance to late blight in potato

2019· article· en· W2961123939 on OpenAlexfundno aff
Deissy Katherine Juyo Rojas, Johana Carolina Soto Sedano, Agim Ballvora, Jens Léon, Teresa Mosquera

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Pathogens and Resistance
Canadian institutionsnot available
FundersGlobal Affairs CanadaRheinische Friedrich-Wilhelms-Universität BonnInternational Development Research CentreLeibniz-GemeinschaftDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
KeywordsBiologyQuantitative trait locusSolanum tuberosumBlightPhytophthora infestansGenotypingGeneticsSingle-nucleotide polymorphismCandidate geneGenomePlant disease resistanceGeneGenotypeBotany

Abstract

fetched live from OpenAlex

Potato, Solanum tuberosum, is one of the major consumed food in the world, being the basis of the diet of millions of people. The main limiting and destructive disease of potato is late blight, caused by Phytophtora infestans. Here, we present a multi-environmental analysis of the response to P. infestans using an association panel of 150 accessions of S. tuberosum Group Phureja, evaluated in two localities in Colombia. Disease resistance data were merged with a genotyping matrix of 83,862 SNPs obtained by 2b-restriction site-associated DNA and Genotyping by sequencing approaches into a Genome-wide association study. We are reporting 16 organ-specific QTL conferring resistance to late blight. These QTL explain between 13.7% and 50.9% of the phenotypic variance. Six and ten QTL were detected for resistance response in leaves and stem, respectively. In silico analysis revealed 15 candidate genes for resistance to late blight. Four of them have no functional genome annotation, while eleven candidate genes code for diverse proteins, including a leucine-rich repeat kinase.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.059
GPT teacher head0.215
Teacher spread0.155 · 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 designBench or experimental
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

Citations27
Published2019
Admission routes1
Has abstractyes

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