Heritability enrichment analysis using data from Genome-Wide Association Studies (GWAS) is often used to understand the functional basis of genetic architecture. Stratified LD score regression (SLDSC) 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 partition and enrichment analysis method. graphREML operates on GWAS summary statistics and linkage disequilibrium graphical models (LDGMs), whose sparsity makes 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 to the moment-based S-LDSC approach. graphREML can also more flexibly model the relationship between the annotations of a SNP and its heritability, producing well-calibrated estimates of per-SNP heritability.

Xihong Lin is a consultant of AbbVie Pharmaceuticals and Verily Life Sciences. The other authors declare no competing interests.

This work was supported by grants R35-CA197449, U19-CA203654, R01-HL163560, U01-HG012064, and U01-HG009088 (to Xihong Lin.) and by grant R35 GM155278 (to Luke O'Connor).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

The baselineLD annotations can be downloaded on Google Cloud (\href). The constrained gene sets can be downloaded from the AMM Github repository \newline (\href). LDGM precision matrices derived from the 1000 Genome are available from Zenodo \newline (\href).

https://github.com/huilisabrina/graphREML