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

Hui Li, Rahul Mazumder and Xihong Lin ()
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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

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-43565-9

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