Evidence map›Paper›PMID 31165258›Full record

ReviewHuman genetics2020

Missing heritability of complex diseases: case solved?

Emmanuelle Génin

Abstract readReview
PubMed Publisher
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 87 papers.

0numbers the graph read from it
0cells of the map it votes in
87citing 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

87 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. The genetics of hypertension.Nature reviews. Nephrology · 2026
    Review
  11. Article
  12. Review
  13. Article
  14. Review
  15. Article
  16. Observational
  17. Article
  18. Review
  19. Article
  20. Review

27 more citing papers are in PubMed but not listed here.

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.

Emmanuelle GéninInserm, Univ Brest, EFS, CHU Brest, UMR 1078, GGB (Génétique, Génomique fonctionnelle et Biotechnologies), Faculté de Médecine, Bâtiment E-IBRBS 2ieme étage, 22 avenue Camille Desmoulins, 29200, Brest Cedex 3, France. emmanuelle.genin@inserm.fr.ORCID http://orcid.org/0000-0003-4117-2813

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

About 10 years ago, after the first large-scale genome-wide association studies (GWAS) were conducted to find genes associated with common complex diseases, investigators were surprised to find that the amount of heritability explained by the significant hits was very low for almost all the studied traits. Indeed, when compared to heritability estimates expected from the observed trait concordance within families, the heritability explained by the associated variants was always much smaller, more than ten times smaller for some traits. There was thus a problem of "missing heritability" and different hypotheses were proposed to help find this "missing heritability". These hypotheses involved novel research strategies in which different groups engaged including among others increasing sample sizes of GWAS or looking for rare variants and structural variations that were not captured by the SNP-chips used in GWAS. How successful have these efforts been in finding the "missing heritability"? Could it be that the problem of "missing heritability" was ill-defined? These are the questions that will be addressed in this paper by taking some different examples of complex traits.

Indexed as

Genome-Wide Association StudyInheritance PatternsModels, GeneticDiseaseHumansPhenotype

Identifiers

What OpenQuestion holds

Textmetadata
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.