Evidence map›Paper›PMID 32155892›Full record

ArticleGenes2020

Genetic Diversity, Population Structure and Linkage Disequilibrium Assessment among International Sunflower Breeding Collections.

Carla V Filippi, Gabriela A Merino, Juan F Montecchia, Natalia C Aguirre, Máximo Rivarola, Guy Naamati, Mónica I Fass, Daniel Álvarez, Julio Di Rienzo, Ruth A Heinz and 3 more

Abstract read
In one paragraph

Article in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

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  15. Mitochondrial Genome ofPlants (Basel, Switzerland) · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Carla V FilippiInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.ORCID 0000-0002-5564-7480
Gabriela A MerinoInstituto de Investigación y Desarrollo en Bioingeniería y Bioinformática-IBB, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional de Entre Ríos, Oro Verde 3100, Argentina.ORCID 0000-0002-0130-6226
Juan F MontecchiaInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.ORCID 0000-0001-9677-1560
Natalia C AguirreInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.ORCID 0000-0002-1668-9530
Máximo RivarolaInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.ORCID 0000-0003-2818-1037
Guy NaamatiEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
Mónica I FassInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.
Daniel ÁlvarezEstación Experimental Agropecuaria INTA Manfredi, Manfredi 5988, Argentina.
Julio Di RienzoFacultad de Ciencias Agropecuarias, Universidad Nacional de Córdoba, Córdoba 5000, Argentina.
Ruth A HeinzInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.
Bruno Contreras MoreiraEuropean Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
Verónica V LiaInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.
Norma B PaniegoInstituto de Agrobiotecnología y Biología Molecular-IABiMo-INTA-CONICET, Instituto de Biotecnología, Centro de Investigaciones en Ciencias Veterinarias y Agronómicas, Instituto Nacional de Tecnología Agropecuaria, Hurlingham 1686, Argentina.ORCID 0000-0003-3906-5330

Funding

Biotechnology and Biological Sciences Research Council BB/P027849/1Wellcome Trust
6 · The paper itself

Abstract

Sunflower germplasm collections are valuable resources for broadening the genetic base of commercial hybrids and ameliorate the risk of climate events. Nowadays, the most studied worldwide sunflower pre-breeding collections belong to INTA (Argentina), INRA (France), and USDA-UBC (United States of America-Canada). In this work, we assess the amount and distribution of genetic diversity (GD) available within and between these collections to estimate the distribution pattern of global diversity. A mixed genotyping strategy was implemented, by combining proprietary genotyping-by-sequencing data with public whole-genome-sequencing data, to generate an integrative 11,834-common single nucleotide polymorphism matrix including the three breeding collections. In general, the GD estimates obtained were moderate. An analysis of molecular variance provided evidence of population structure between breeding collections. However, the optimal number of subpopulations, studied via discriminant analysis of principal components (K = 12), the bayesian STRUCTURE algorithm (K = 6) and distance-based methods (K = 9) remains unclear, since no single unifying characteristic is apparent for any of the inferred groups. Different overall patterns of linkage disequilibrium (LD) were observed across chromosomes, with Chr10, Chr17, Chr5, and Chr2 showing the highest LD. This work represents the largest and most comprehensive inter-breeding collection analysis of genomic diversity for cultivated sunflower conducted to date.

Indexed as

Linkage DisequilibriumPolymorphism, GeneticSeed BankChromosomes, PlantHelianthusPlant Breedingbreedinggenetic diversitylinkage disequilibriumpopulation structuresunflower

Identifiers

PMID32155892
PMCPMC7140877

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.