Evidence map›Paper›PMID 32291292›Full record

ArticleGenetics2020

On Negative Heritability and Negative Estimates of Heritability.

David Steinsaltz, Andy Dahl, Kenneth W Wachter

Abstract read
In one paragraph

Article in Genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
–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

25 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. Causal Relationships Between Screen Use, Reading, and Brain Development in Early Adolescents.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
    Article
  11. Article
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  14. Effects of Genetic Relatedness of Kin Pairs on Univariate ACE Model Performance.Twin research and human genetics : the official journal of the International Society for Twin Studies · 2023
    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

3 authors.

David SteinsaltzDepartment of Statistics, University of Oxford, OX1 3TG, United Kingdom steinsal@stats.ox.ac.uk andywdahl@gmail.com.
Andy DahlDepartment of Medicine, University of California, San Francisco, California, 94143 steinsal@stats.ox.ac.uk andywdahl@gmail.com.ORCID 0000-0001-6520-4766
Kenneth W WachterDepartment of Demography, University of California, Berkeley, California 94720.ORCID 0000-0003-2802-2212

Funding

Program Development (Pilot)P30AG012839 · NIA · UNIVERSITY OF CALIFORNIA BERKELEY · PI William H Dow · 1999 to 2026
$18.6M
Optimizing imputation for diverse populations in a distributed frameworkU01HG009080 · NHGRI · STANFORD UNIVERSITY · PI KENNY, EIMEAR ELIZABETH · 2016 to 2020
$5.2M
Biotechnology and Biological Sciences Research Council BB/S001824/1NHGRI NIH HHS U01 HG009080NIA NIH HHS P30 AG012839
6 · The paper itself

Abstract

We consider the problem of interpreting negative maximum likelihood estimates of heritability that sometimes arise from popular statistical models of additive genetic variation. These may result from random noise acting on estimates of genuinely positive heritability, but we argue that they may also arise from misspecification of the standard additive mechanism that is supposed to justify the statistical procedure. Researchers should be open to the possibility that negative heritability estimates could reflect a real physical feature of the biological process from which the data were sampled.

Indexed as

Genetic VariationModels, GeneticModels, StatisticalMultifactorial InheritancePhenotypeQuantitative Trait, HeritableHumansEpistasisGREMLHeritabilityLinear mixed modelModel misspecification

Identifiers

PMID32291292
PMCPMC7268991

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.