Evidence map›Paper›PMID 32637052›Full record

ReviewComputational and structural biotechnology journal2020

Statistical methods for SNP heritability estimation and partition: A review.

Huanhuan Zhu, Xiang Zhou

Open access · goldAbstract readReview
In one paragraph

Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 1 pooled it
4.0field-weighted citation impact, top 5% of its field
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

53 citing papers in PubMed, 1 synthesis or guideline pooled it, 92 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. A genome-wide association study identified 10 novel genomic loci associated with intrinsic capacity.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025
    Article
  13. Article
  14. Article
  15. Review
  16. Participation bias in the estimation of heritability and genetic correlation.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  17. Genome-Wide Association Study for Weight-Related Traits inAnimals : an open access journal from MDPI · 2025
    Article
  18. Article
  19. Article
  20. Multivariate Bayesian variable selection for multi-trait genetic fine mapping.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025
    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

2 authors at 1 institution in 1 country.

Huanhuan ZhuDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
Xiang ZhouDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.
University of Michigan · US

Funding

Statistical Methods for Modeling Polygenic Architecture in Association and Re-sequencing StudiesR01HG009124 · NHGRI · YALE UNIVERSITY · PI Xiang Zhou · 2017 to 2026
$3.0M
NHGRI NIH HHS R01 HG009124
6 · The paper itself

Abstract

In GWAS studies, SNP heritability measures the proportion of phenotypic variance explained by all measured SNPs. Accurate estimation of SNP heritability can help us better understand the degree to which measured genetic variants influence phenotypes. Over the last decade, a variety of statistical methods and software tools have been developed for SNP heritability estimation with different data types including genotype array data, imputed genotype data, whole-genome sequencing data, RNA sequencing data, and bisulfite sequencing data. However, a thorough technical review of these methods, especially from a statistical and computational viewpoint, is currently missing. To fill this knowledge gap, we present a comprehensive review on a broad category of recently developed and commonly used SNP heritability estimation methods. We focus on their modeling assumptions; their interconnected relationships; their applicability to quantitative, binary and count phenotypes; their use of individual level data versus summary statistics, as well as their utility for SNP heritability partitioning. We hope that this review will serve as a useful reference for both methodologists who develop heritability estimation methods and practitioners who perform heritability analysis.

Indexed as

Linear mixed modelMethod of momentsREMLSNP heritabilitySummary statistics

Identifiers

PMID32637052
PMCPMC7330487
OpenAlexW3036943654

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.