Scalable summary-statistics-based heritability estimation method with individual genotype level accuracy
- 1Department of Computer Science, University of California, Los Angeles, Los Angeles, California 90095, USA;
- 2Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts 02115, USA;
- 3Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;
- 4Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA;
- 5Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, USA
Abstract
SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale data sets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. Although the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary-statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared with other summary statistic methods and on par with methods that rely on individual-level data.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279207.124.
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Freely available online through the Genome Research Open Access option.
- Received February 26, 2024.
- Accepted July 12, 2024.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
