ArticleElectronic journal of statistics2018
Statistical properties of simple random-effects models for genetic heritability.
Article in Electronic journal of statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
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Who cites it
7 citing papers in PubMed.
- Article
- Heritability in the Rhesus Macaque (Macaca mulatta) Vertebral Column.American journal of primatology · 2025Article
- Trade-offs of Linear Mixed Models in Genome-Wide Association Studies.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- A model and test for coordinated polygenic epistasis in complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- 8q24 genetic variation and comprehensive haplotypes altering familial risk of prostate cancer.Nature communications · 2020Article
- A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits.American journal of human genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Random-effects models are a popular tool for analysing total narrow-sense heritability for quantitative phenotypes, on the basis of large-scale SNP data. Recently, there have been disputes over the validity of conclusions that may be drawn from such analysis. We derive some of the fundamental statistical properties of heritability estimates arising from these models, showing that the bias will generally be small. We show that that the score function may be manipulated into a form that facilitates intelligible interpretations of the results. We go on to use this score function to explore the behavior of the model when certain key assumptions of the model are not satisfied - shared environment, measurement error, and genetic effects that are confined to a small subset of sites. The variance and bias depend crucially on the variance of certain functionals of the singular values of the genotype matrix. A useful baseline is the singular value distribution associated with genotypes that are completely independent - that is, with no linkage and no relatedness - for a given number of individuals and sites. We calculate the corresponding variance and bias for this setting.
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Registered trials
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