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<p>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.</p>

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Improved heritability partitioning and enrichment analyses using summary statistics with graphREML

  • 期刊论文

  • 发表日期：

    2026年7月1日

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

摘要

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.