Improved heritability partitioning and enrichment analyses using summary statistics with graphREML.

Authors

Hui Li, Tushar Kamath, Rahul Mazumder, Xihong Lin, Luke Jen O'Connor

Published in

Nature genetics. Jul 01, 2026. Epub Jul 01, 2026.

Abstract

Heritability enrichment analysis using data from genome-wide association studies is often used to understand the functional basis of genetic architecture. Stratified linkage disequilibrium score regression (S-LDSC) is a widely used method-of-moments estimator for heritability enrichment, but S-LDSC has low statistical power compared with likelihood-based approaches. We introduce graphREML, a precise and powerful likelihood-based heritability partitioning and enrichment analysis method. It utilizes summary statistics from genome-wide association studies and sparse linkage disequilibrium graphical models, which make likelihood calculations tractable. We validate our method using extensive simulations and in analyses of a wide range of real traits. On average across traits, graphREML produces enrichment estimates that are concordant with S-LDSC, indicating that both methods are unbiased; however, graphREML identifies 2.5 times more significant trait-annotation enrichments, demonstrating greater power compared with the moment-based S-LDSC approach. Furthermore, graphREML flexibly models the relationship between the annotations of an SNP and its heritability, producing well-calibrated estimates of per-SNP heritability.

PMID:
42386933
Bibliographic data and abstract were imported from PubMed on 02 Jul 2026.

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