Evidence map›Paper›PMID 28893854›Full record

ArticleGenetics2017

Will Big Data Close the Missing Heritability Gap?

Hwasoon Kim, Alexander Grueneberg, Ana I Vazquez, Stephen Hsu, Gustavo de Los Campos

Abstract read
In one paragraph

Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

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

  1. Pooled it
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  12. Human height: a model common complex trait.Annals of human biology · 2023
    Review
  13. Article
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  16. Mapping pleiotropic loci using a fast-sequential testing algorithm.European journal of human genetics : EJHG · 2021
    Article
  17. 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

5 authors.

Hwasoon KimDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824.ORCID 0000-0002-0561-3186
Alexander GruenebergDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824.
Ana I VazquezDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824.
Stephen HsuDepartment of Physics and Astronomy, Michigan State University, East Lansing, Michigan 48824.ORCID 0000-0002-1808-2361
Gustavo de Los CamposDepartment of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824 gustavoc@msu.edu.ORCID 0000-0001-5692-7129

Funding

Statistical Tools for Whole-Genome Prediction of Complex Traits and DiseasesR01GM101219 · NIGMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DE LOS CAMPOS, GUSTAVO · 2012 to 2017
$1.6M
Factors Affecting Prediction Accuracy of Complex Human Traits and DiseasesR01GM099992 · NIGMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI DE LOS CAMPOS, GUSTAVO · 2012 to 2015
$1.0M
NIGMS NIH HHS R01 GM099992NIGMS NIH HHS R01 GM101219
6 · The paper itself

Abstract

Despite the important discoveries reported by genome-wide association (GWA) studies, for most traits and diseases the prediction R-squared (R-sq.) achieved with genetic scores remains considerably lower than the trait heritability. Modern biobanks will soon deliver unprecedentedly large biomedical data sets: Will the advent of big data close the gap between the trait heritability and the proportion of variance that can be explained by a genomic predictor? We addressed this question using Bayesian methods and a data analysis approach that produces a surface response relating prediction R-sq. with sample size and model complexity (

Indexed as

Genetic VariationQuantitative Trait, HeritableAdultAgedBody HeightData AccuracyDatabases, FactualFemaleGenome-Wide Association StudyHumansMaleMiddle AgedModels, GeneticSample SizeBayesianBGLRbig datagenomic predictionGenomic SelectionGenPredprediction of complex traitsShared Data ResourcesUK Biobankwhole-genome regressions

Identifiers

PMID28893854
PMCPMC5676235

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.