Evidence map›Paper›PMID 35247120›Full record

ArticleRice (New York, N.Y.)2022

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines.

Apurva Khanna, Mahender Anumalla, Margaret Catolos, Jérôme Bartholomé, Roberto Fritsche-Neto, John Damien Platten, Daniel Joseph Pisano, Alaine Gulles, Ma Teresa Sta Cruz, Joie Ramos and 3 more

Abstract read
In one paragraph

Article in Rice (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

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24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Article
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  4. 110 years of rice breeding at LSU: realized genetic gains and future optimization.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
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  15. Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024
    Article
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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.

Apurva KhannaRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Mahender AnumallaRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Margaret CatolosRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Jérôme BartholoméAGAP Institute, CIRAD, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France.
Roberto Fritsche-NetoRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
John Damien PlattenRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Daniel Joseph PisanoRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Alaine GullesRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Ma Teresa Sta CruzRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Joie RamosRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Gem FaustinoRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Sankalp BhosaleRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines.
Waseem HussainRice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines. waseem.hussain@irri.org.ORCID http://orcid.org/0000-0002-6861-0193

Funding

Bill and Melinda Gates Foundation A-2017-129
6 · The paper itself

Abstract

Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43-0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

Indexed as

Breeding panelDrought breedingGenetic trendsHistorical dataRice

Identifiers

PMID35247120
PMCPMC8898209

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LicenceCC BY
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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.