Evidence map›Paper›PMID 31136078›Full record

ArticleMolecular ecology resources2019

Rapid identification and interpretation of gene-environment associations using the new R.SamBada landscape genomics pipeline.

Solange Duruz, Natalia Sevane, Oliver Selmoni, Elia Vajana, Kevin Leempoel, Sylvie Stucki, Pablo Orozco-terWengel, Estelle Rochat, Susana Dunner, NEXTGEN Consortium and 3 more

Abstract read
In one paragraph

Article in Molecular ecology resources, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Mapping genomic adaptation to environmental heterogeneity in Indian native goat populations through landscape genomics.Mammalian genome : official journal of the International Mammalian Genome Society · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Solange DuruzLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-1235-6747
Natalia SevaneDepartamento de Producción Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid, Spain.ORCID https://orcid.org/0000-0003-4766-6291
Oliver SelmoniLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID https://orcid.org/0000-0003-0904-5486
Elia VajanaLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID https://orcid.org/0000-0003-1340-3389
Kevin LeempoelDepartment of Biology, Stanford University, Stanford, California.ORCID https://orcid.org/0000-0001-7335-7930
Sylvie StuckiLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-1624-1576
Pablo Orozco-terWengelSchool of Biosciences, Cardiff University, Cardiff, Wales, UK.ORCID https://orcid.org/0000-0002-7951-4148
Estelle RochatLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-7978-5239
Susana DunnerDepartamento de Producción Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, Madrid, Spain.ORCID https://orcid.org/0000-0001-8637-2208
NEXTGEN Consortium
CLIMGEN Consortium
Michael W BrufordSchool of Biosciences, Cardiff University, Cardiff, Wales, UK.ORCID https://orcid.org/0000-0001-6357-6080
Stéphane JoostLaboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-1184-7501

Funding

Biotechnology and Biological Sciences Research Council BB/M019276/1FACCE ERA-NET Plus ANR-14-JFAC-0002-01FP7 Food, Agriculture and Fisheries, Biotechnology 244356H2020 Marie Skłodowska-Curie Actions DLV-655100
6 · The paper itself

Abstract

samβada is a genome-environment association software, designed to search for signatures of local adaptation. However, pre- and postprocessing of data can be labour-intensive, preventing wider uptake of the method. We have now developed R.SamBada, an r-package providing a pipeline for landscape genomic analysis based on samβada, spanning from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, R.SamBada standardizes the landscape genomics pipeline and eases the search for candidate genes of local adaptation, enhancing reproducibility of landscape genomic studies. The efficiency and power of the pipeline is illustrated using two examples: sheep populations from Morocco with no evident population structure and Lidia cattle from Spain displaying population substructuring. In both cases, R.SamBada enabled rapid identification and interpretation of candidate genes, which are further discussed in the light of local adaptation. The package is available in the r CRAN package repository and on GitHub (github.com/SolangeD/R.SamBada).

Indexed as

Adaptation, BiologicalEnvironmental ExposureAnimalsCattleComputational BiologyGenomicsMoroccoSheepSoftwareSpaingene-environment associationlandscape genomicsLidia cattle breedlocal adaptationMoroccan sheepr-package

Identifiers

PMID31136078
PMCPMC6790591

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.