Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture
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Abstract
SNP-heritability is a fundamental quantity in the study of complex traits. Recent studies have shown that existing methods to estimate genome-wide SNP-heritability can yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and linkage disequilibrium (LD)-dependent genetic architectures, it remains unclear which estimates reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of genetic architecture, without specifying a heritability model or partitioning SNPs by allele frequency and/or LD. We show analytically and through extensive simulations starting from real genotypes (UK Biobank, N = 337 K) that, unlike existing methods, our closed-form estimator is robust across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.
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Recent analyses have demonstrated that estimates of heritability enrichment are sensitive to the assumed heritability model and that variation in heritability estimates from different statistical methods are influenced by demographic factors.
“Fourth, recent analyses have demonstrated that estimates of heritability enrichment are sensitive to the assumed heritability model and that variation in heritability estimates from different statistical methods are influenced by demographic factors75,76.”
From Quantifying the contribution of Neanderthal introgression to the heritability of complex traits (2021) · cites this paper as [76]
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Self-citation
- Authors
- Hou, K., Burch, K. S., Majumdar, A., Shi, H., Mancuso, N., Wu, Y., Sankararaman, S., & Pasaniuc, B.
- Affiliations
- University of California, Los Angeles (US)
- Harvard University (US)
- University of Southern California (US)
- Corresponding author
- Burch, Kathryn S.
- ORCID
- Venue
- Nature Genetics
- Published
- 2019-07-29
- Volume
- 51
- Issue
- 8
- Pages
- 1244-1251
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC6686906
- OpenAlex
- W2966861845
- Cited by
- 91
- References
- 50
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 9 · 2023: 12 · 2022: 21 · 2021: 16 · 2020: 16 · 2019: 4 · 2018: 1
- FWCI
- 8.59
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (33127)
- Medical Research Council (MC_PC_17228)
- National Institute of Mental Health (T32MH073526, R01MH115676)
- National Human Genome Research Institute (R01HG009120, R01HG006399, T32HG002536)
- National Cancer Institute (U01CA194393)
- National Institute of General Medical Sciences (R35GM125055)
- National Institute of Neurological Disorders and Stroke (T32NS048004)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- SNP heritability, heritability estimation, genetic architecture, linkage disequilibrium, allele frequency, complex traits, biobank-scale data, closed-form estimator, UK Biobank
- MeSH terms
- Humans, Models, Theoretical, Phenotype, Linkage Disequilibrium, Genome, Human, Biological Specimen Banks, Quantitative Trait, Heritable, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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