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Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix

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  • Hui Li

    (Harvard T.H. Chan School of Public Health, Department of Biostatistics)

  • Rahul Mazumder

    (Operations Research and Statistics group)

  • Xihong Lin

    (Harvard T.H. Chan School of Public Health, Department of Biostatistics
    Harvard University, Department of Statistics)

Abstract

Existing SNP-heritability estimators that leverage summary statistics from genome-wide association studies (GWAS) are much less efficient (i.e., have larger standard errors) than the restricted maximum likelihood (REML) estimators which require access to individual-level data. We introduce a new method for local heritability estimation—Heritability Estimation with high Efficiency using LD and association Summary Statistics (HEELS)—that significantly improves the statistical efficiency of summary-statistics-based heritability estimator and attains comparable statistical efficiency as REML (with a relative statistical efficiency >92%). Moreover, we propose representing the empirical LD matrix as the sum of a low-rank matrix and a banded matrix. We show that this way of modeling the LD can not only reduce the storage and memory cost, but also improve the computational efficiency of heritability estimation. We demonstrate the statistical efficiency of HEELS and the advantages of our proposed LD approximation strategies both in simulations and through empirical analyses of the UK Biobank data.

Suggested Citation

  • Hui Li & Rahul Mazumder & Xihong Lin, 2023. "Accurate and efficient estimation of local heritability using summary statistics and the linkage disequilibrium matrix," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43565-9
    DOI: 10.1038/s41467-023-43565-9
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    References listed on IDEAS

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    1. Clare Bycroft & Colin Freeman & Desislava Petkova & Gavin Band & Lloyd T. Elliott & Kevin Sharp & Allan Motyer & Damjan Vukcevic & Olivier Delaneau & Jared O’Connell & Adrian Cortes & Samantha Welsh &, 2018. "The UK Biobank resource with deep phenotyping and genomic data," Nature, Nature, vol. 562(7726), pages 203-209, October.
    2. Xiang Zhou & Peter Carbonetto & Matthew Stephens, 2013. "Polygenic Modeling with Bayesian Sparse Linear Mixed Models," PLOS Genetics, Public Library of Science, vol. 9(2), pages 1-14, February.
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