Evidence map›Paper›PMID 24481204›Full record

ArticleEvolution, medicine, and public health2013

Windfalls and pitfalls: Applications of population genetics to the search for disease genes.

Michael D Edge, Prakash Gorroochurn, Noah A Rosenberg

Abstract read
In one paragraph

Article in Evolution, medicine, and public health, 2013. 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

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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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Mathematical bounds on rTheoretical population biology · 2025
    Article
  4. Article
  5. Mathematical bounds onbioRxiv : the preprint server for biology · 2024
    Article
  6. Article
  7. Article
  8. Tree-based QTL mapping with expected local genetic relatedness matrices.bioRxiv : the preprint server for biology · 2023
    Article
  9. Review
  10. Review
  11. 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

3 authors.

Michael D EdgeDepartment of Biology, Stanford University, Stanford, CA 94305-5020, USA and Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
Prakash Gorroochurn
Noah A Rosenberg

Funding

Population genetics for large-scale sequencing studies of diverse populationsR01HG005855 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Noah Rosenberg, Paul A Scheet · 2010 to 2026
$5.8M
NHGRI NIH HHS R01 HG005855
6 · The paper itself

Abstract

Association mapping can be viewed as an application of population genetics and evolutionary biology to the problem of identifying genes causally connected to phenotypes. However, some population-genetic principles important to the design and analysis of association studies have not been widely understood or have even been generally misunderstood. Some of these principles underlie techniques that can aid in the discovery of genetic variants that influence phenotypes ('windfalls'), whereas others can interfere with study design or interpretation of results ('pitfalls'). Here, considering examples involving genetic variant discovery, linkage disequilibrium, power to detect associations, population stratification and genotype imputation, we address misunderstandings in the application of population genetics to association studies, and we illuminate how some surprising results in association contexts can be easily explained when considered from evolutionary and population-genetic perspectives. Through our examples, we argue that population-genetic thinking-which takes a theoretical view of the evolutionary forces that guide the emergence and propagation of genetic variants-substantially informs the design and interpretation of genetic association studies. In particular, population-genetic thinking sheds light on genetic confounding, on the relationships between association signals of typed markers and causal variants, and on the advantages and disadvantages of particular strategies for measuring genetic variation in association studies.

Indexed as

genome-wide association studiesimputationlinkage disequilibriumpopulation stratification

Identifiers

PMID24481204
PMCPMC3868415

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.