Evidence map›Paper›PMID 38873115›Full record

ArticleFrontiers in genetics2024

Genetic diversity and population structure analysis of a diverse panel of pea (

Haftom Brhane, Cecilia Hammenhag

Abstract read
In one paragraph

Article in Frontiers in genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
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  10. Article
  11. Genomic prediction for rust resistance in pea.Frontiers in plant science · 2024
    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

2 authors.

Haftom BrhaneDepartment of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden.
Cecilia HammenhagDepartment of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breeding resilient cultivars with increased tolerance to environmental stress and enhanced resistance to pests and diseases demands pre-breeding efforts that include understanding genetic diversity. This study aimed to evaluate the genetic diversity and population structure of 265 pea accessions. The diversity arrays technology (DArT) genotyping method was employed to identify single-nucleotide polymorphisms (SNPs) and silico markers. After stringent filtering, 6966 SNP and 8,454 silico markers were selected for diversity analysis. Genetic diversity was estimated by grouping accessions based on plant material type, geographic origin, growth habit, and seed color. Generally, diversity estimations obtained using SNPs were similar to those estimated using silico markers. The polymorphism information content (PIC) of the SNP markers ranged from 0.0 to 0.5, with a quarter of them displaying PIC values exceeding 0.4, making them highly informative. Analysis based on plant material type revealed narrow observed heterozygosity (Ho = 0.02-0.03) and expected heterozygosity (He = 0.26-0.31), with landrace accessions exhibiting the highest diversity. Geographic origin-based diversity analysis revealed Ho = 0.02-0.03 and He = 0.22 to 0.30, with European accessions showing the greatest diversity. Moreover, private alleles unique to landrace (4) and European (22) accessions were also identified, which merit further investigation for their potential association with desirable traits. The analysis of molecular variance revealed a highly significant genetic differentiation among accession groups classified by seed color, growth habit, plant material types, and geographic origin (

Indexed as

DArTgenetic diversitypeapopulation genetic structuresingle-nucleotide polymorphism

Identifiers

PMID38873115
PMCPMC11169732

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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.