Evidence map›Paper›PMID 31201529›Full record

ReviewHuman genetics2020

Evolutionary perspectives on polygenic selection, missing heritability, and GWAS.

Lawrence H Uricchio

Abstract readReview
In one paragraph

Review in Human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. An introduction to polygenic scores - methodological basics and recent advances.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
    Article
  2. Unravelling the genetic architecture of cerebral small vessel disease in the context of stroke.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026
    Review
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  10. Review
  11. From genetic variation to precision medicine.Cambridge prisms. Precision medicine · 2023
    Article
  12. Competition-cooperation mechanism betweenFrontiers in microbiology · 2023
    Article
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  16. Linking the genomic signatures of human beat synchronization and learned song in birds.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2021
    Article
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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

1 author.

Lawrence H UricchioDepartment of Biology, Stanford University, Stanford, CA, USA. uricchil@gmail.com.ORCID http://orcid.org/0000-0001-9514-8945

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
A Stanford - SJSU Postdoctoral Training Program to Enhance URM TeachingK12GM088033 · NIGMS · STANFORD UNIVERSITY · PI BOOTHROYD, JOHN C · 2010 to 2016
$4.1M
National Institute of General Medical Sciences (US) K12GM088033NHGRI NIH HHS R01 HG005855NIGMS NIH HHS K12 GM088033NSF DBI-1458059
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have successfully identified many trait-associated variants, but there is still much we do not know about the genetic basis of complex traits. Here, we review recent theoretical and empirical literature regarding selection on complex traits to argue that "missing heritability" is as much an evolutionary problem as it is a statistical problem. We discuss empirical findings that suggest a role for selection in shaping the effect sizes and allele frequencies of causal variation underlying complex traits, and the limitations of these studies. We then use simulations of selection, realistic genome structure, and complex human demography to illustrate the results of recent theoretical work on polygenic selection, and show that statistical inference of causal loci is sharply affected by evolutionary processes. In particular, when selection acts on causal alleles, it hampers the ability to detect causal loci and constrains the transferability of GWAS results across populations. Last, we discuss the implications of these findings for future association studies, and suggest that future statistical methods to infer causal loci for genetic traits will benefit from explicit modeling of the joint distribution of effect sizes and allele frequencies under plausible evolutionary models.

Indexed as

Genome-Wide Association StudyModels, TheoreticalMultifactorial InheritancePhylogenyQuantitative Trait LociSelection, GeneticHumansPolymorphism, Single Nucleotide

Identifiers

PMID31201529
PMCPMC8059781

What OpenQuestion holds

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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.