Evidence map›Paper›PMID 37307746›Full record

ReviewCurrent opinion in plant biology2023

The design of mapping populations: Impacts of geographic scale on genetic architecture and mapping efficacy for defense and immunity.

Andrew D Gloss, Margaret C Steiner, John Novembre, Joy Bergelson

Abstract readReview
In one paragraph

Review in Current opinion in plant biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Convergent evolution increases boron transport through SNPs and tandem duplications atProceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
  4. 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

4 authors.

Andrew D GlossDepartment of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA. Electronic address: ag8612@nyu.edu.
Margaret C SteinerDepartment of Human Genetics, University of Chicago, Chicago, IL, USA.
John NovembreDepartment of Human Genetics, University of Chicago, Chicago, IL, USA; Department of Ecology & Evolution, University of Chicago, Chicago, IL, USA.
Joy BergelsonDepartment of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA. Electronic address: jb7684@nyu.edu.

Funding

Theory, Methods, and Resources for Understanding and Leveraging Spatial Variation in Population Genetic DataR35GM149521 · NIGMS · UNIVERSITY OF CHICAGO · PI John Novembre · 2023 to 2026
$1.7M
Extending Tools for Visualization of Geographic Structure in Population Genomic DataR01GM132383 · NIGMS · UNIVERSITY OF CHICAGO · PI NOVEMBRE, JOHN · 2019 to 2022
$1.4M
NIGMS NIH HHS R01 GM132383NIGMS NIH HHS R35 GM149521
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have yielded tremendous insight into the genetic architecture of trait variation. However, the collections of loci they uncover are far from exhaustive. As many of the complicating factors that confound or limit the efficacy of GWAS are exaggerated over broad geographic scales, a shift toward more analyses using mapping panels sampled from narrow geographic localities ("local" populations) could provide novel, complementary insights. Here, we present an overview of the major complicating factors, review mounting evidence from genomic analyses that these factors are pervasive, and synthesize theoretical and empirical evidence for the power of GWAS in local populations.

Indexed as

Genome-Wide Association StudyQuantitative Trait LociGenomicsPhenotypePolymorphism, Single Nucleotide

Identifiers

PMID37307746
PMCPMC10441534

What OpenQuestion holds

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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.