Evidence map›Paper›PMID 35974066›Full record

SynthesisScientific reports2022

Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification.

C Anilkumar, Rameswar Prasad Sah, T P Muhammed Azharudheen, Sasmita Behera, Namita Singh, Nitish Ranjan Prakash, N C Sunitha, B N Devanna, B C Marndi, B C Patra and 1 more

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
5.2field-weighted citation impact, top 4% of its field
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

14 citing papers in PubMed, 30 citations in OpenAlex.

  1. Article
  2. Article
  3. Meta-QTLs and candidate genes for kernel protein content in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Article
  4. Article
  5. Article
  6. Identification of a novel locus qGW12/OsPUB23 regulating grain shape and weight in rice (Oryza sativa L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024
    Article
  7. Review
  8. Article
  9. Fine mapping of interspecific secondary CSSL populations revealed key regulators for grain weight atPhysiology and molecular biology of plants : an international journal of functional plant biology · 2024
    Article
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  11. Article
  12. Article
  13. Article
  14. 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

11 authors at 4 institutions in 1 country.

C AnilkumarICAR-National Rice Research Institute, Cuttack, India. anilcgpb@gmail.com.
Rameswar Prasad SahICAR-National Rice Research Institute, Cuttack, India.
T P Muhammed AzharudheenICAR-National Rice Research Institute, Cuttack, India.
Sasmita BeheraICAR-National Rice Research Institute, Cuttack, India.
Namita SinghIndira Gandhi Krishi Vishwavidyalaya, Raipur, India.
Nitish Ranjan PrakashICAR-Central Soil Salinity Research Institute, Regional Research Station, Canning Town, India.
N C SunithaUniversity of Agricultural Sciences, Bangalore, India.
B N DevannaICAR-National Rice Research Institute, Cuttack, India.
B C MarndiICAR-National Rice Research Institute, Cuttack, India.
B C PatraICAR-National Rice Research Institute, Cuttack, India.
Sunil Kumar NairIndira Gandhi Krishi Vishwavidyalaya, Raipur, India.
Central Rice Research Institute · INIndira Gandhi Agricultural University · INCentral Soil Salinity Research Institute · INUniversity of Agricultural Sciences, Bangalore · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

Indexed as

OryzaQuantitative Trait LociAsparagineEdible GrainGenetic Association StudiesPhenotypePlant BreedingAsparagine

Identifiers

PMID35974066
PMCPMC9381546
OpenAlexW4291949412

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.