Evidence map›Paper›PMID 29505644›Full record

ArticleThe plant genome2018

Genetic Analysis of

Rahul Chandnani, Changsoo Kim, Hui Guo, Tariq Shehzad, Jason G Wallace, Daohua He, Zhengsheng Zhang, Jinesh D Patel, Jeevan Adhikari, Sameer Khanal and 1 more

Abstract read
In one paragraph

Article in The plant genome, 2018. 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. Genetic Analysis ofThe plant genome · 2018
    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.

Rahul Chandnani
Changsoo Kim
Hui Guo
Tariq Shehzad
Jason G Wallace
Daohua He
Zhengsheng Zhang
Jinesh D Patel
Jeevan Adhikari
Sameer Khanal
Andrew H Paterson

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In mapping populations segregating for many loci, the large amount of variation among genotypes often masks small-effect quantitative trait loci (QTL). This problem can be reduced by development of populations with fewer chromosome segments segregating. Here, we report early QTL detection in reciprocal advanced backcross populations from crosses between elite Gossypium hirsutum L. 'Acala Maxxa' (GH) and G. barbadense L. 'Pima S6' (GB). A total of 297 BCF and BCF progeny rows-127 segregating for GB chromosome segments in GH background and 170 segregating for GH chromosome segments in GB background-were evaluated in three environments. Totals of 3186 and 3026 polymorphic single-nucleotide polymorphisms (SNPs) in GH and GB backgrounds, respectively, were identified and used for trait mapping. Small-effect QTL (<10% variance explained) made up 87 and 100% of QTL in GH and GB backgrounds, respectively. In both species, favorable alleles were found with effects being masked or neutralized by unfavorable alleles, with greater scope for improvement of GH than GB by introgressive breeding. A total of three stable QTL-two in GH background for fiber elongation (ELO) and micronaire (MIC) and one in GB background for upper-half mean length (UHM)-were identified in two out of three environments. Curiously, only four QTL-three for UHM and one for ELO-showed the expected opposite effects in reciprocal backgrounds, perhaps reflecting the combined consequences of epistasis, small phenotypic effects, and low coverage of some genomic regions. Along with new information for marker-assisted breeding, this study adds to knowledge that can be used to unravel complex genetic networks governing fiber quality traits.

Indexed as

Cotton FiberQuantitative Trait LociCrosses, GeneticGossypiumPhenotypePlant BreedingPolymorphism, Single Nucleotide

Identifiers

PMID29505644
PMCPMC12962560

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.