Evidence map›Paper›PMID 27920780›Full record

ArticleFrontiers in plant science2016

Genome-Enabled Prediction Models for Yield Related Traits in Chickpea.

Manish Roorkiwal, Abhishek Rathore, Roma R Das, Muneendra K Singh, Ankit Jain, Samineni Srinivasan, Pooran M Gaur, Bharadwaj Chellapilla, Shailesh Tripathi, Yongle Li and 6 more

Abstract read
In one paragraph

Article in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.

0numbers the graph read from it
0cells of the map it votes in
67citing 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

67 citing papers in PubMed.

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  11. Genomic prediction for rust resistance in pea.Frontiers in plant science · 2024
    Article
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  16. Article
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7 more citing papers are in PubMed but not listed here.

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

16 authors.

Manish RoorkiwalResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid Tropics Hyderabad, India.
Abhishek RathoreResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid Tropics Hyderabad, India.
Roma R DasResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid Tropics Hyderabad, India.
Muneendra K SinghResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid Tropics Hyderabad, India.
Ankit JainResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid Tropics Hyderabad, India.
Samineni SrinivasanResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid Tropics Hyderabad, India.
Pooran M GaurResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid Tropics Hyderabad, India.
Bharadwaj ChellapillaDivision of Genetics, Indian Agricultural Research Institute Delhi, India.
Shailesh TripathiDivision of Genetics, Indian Agricultural Research Institute Delhi, India.
Yongle LiAustralian Centre for Plant Functional Genomics, University of Adelaide Adelaide, SA, Australia.
John M HickeyThe Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh Easter Bush, UK.
Aaron LorenzDepartment of Agronomy and Horticulture, University of Nebraska Lincoln, OR, USA.
Tim SuttonAustralian Centre for Plant Functional Genomics, University of AdelaideAdelaide, SA, Australia; Crop Improvement, South Australian Research and Development InstituteUrrbrae, SA, Australia.
Jose CrossaInternational Maize and Wheat Improvement Center Mexico, Mexico.
Jean-Luc JanninkSchool of Integrative Plant Science, Cornell University Ithaca, NY, USA.
Rajeev K VarshneyResearch Program-Grain Legumes, International Crops Research Institute for the Semi-Arid TropicsHyderabad, India; School of Plant Biology and Institute of Agriculture, The University of Western AustraliaWestern Australia WA, Australia.

Funding

Biotechnology and Biological Sciences Research Council BBS/E/D/20211553Biotechnology and Biological Sciences Research Council BBS/E/D/20211554Medical Research Council MR/M000370/1
6 · The paper itself

Abstract

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011-12 and 2012-13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits

Indexed as

chickpeagenetic gaingenomic prediction accuracygenomic selectionpopulation structureprediction modelstraining population

Identifiers

PMID27920780
PMCPMC5118446

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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.