HEDE: Heritability estimation in high dimensions by ensembling debiased estimators in genome-wide association studies
Year: 2026
Type: article
Abstract: Estimating heritability using a large number of SNPs in Genome-Wide Association Studies (GWAS) is of substantial interest. Various approaches have emerged over the years, broadly categorized as either random effects or fixed effects heritability methods. These methods are sensitive to their assumptions on the underlying signal structure. We propose a robust fixed effect-based ensemble approach to estimate heritability or the signal-to-noise ratio in high-dimensional linear models where the sample size and dimension grow proportionally. Our method ensembles post-processed versions of the debiased lasso and debiased ridge estimators, and incorporates a data-driven strategy for hyperparameter selection that significantly boosts estimation performance. We establish rigorous consistency guarantees that hold despite adaptive tuning. Extensive simulations demonstrate our method’s superiority over state-of-the-art methods across various signal structures and genetic architectures, ranging from sparse to relatively dense and from evenly to unevenly distributed signals. Furthermore, we discuss advantages of fixed effects heritability estimation compared to random effects estimation. Our theoretical guarantees hold for realistic distributions observed in genetic studies, where genotypes typically take on discrete values and are often well modeled by sub-Gaussian random variables. We establish our theoretical results by deriving uniform bounds, built upon the convex Gaussian min-max theorem, and leveraging universality results. Finally, we showcase the efficacy of our approach in estimating height and BMI heritability using the GWAS data from the UK Biobank.
Source: The Annals of Applied Statistics
DOI: 10.1214/26-aoas2152
Authors: Yanke Song, Xihong Lin, Pragya Sur
Institution: Harvard University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Integrative Analysis of Lung Cancer Etiology and Risk, Harvard Chan School NIEHS Center for Environmental Health, Inference for Functionals in High-Dimensional Regression, G9000085835, Predictive Modeling of the Functional and Phenotypic Impacts of Genetic Variants, Statistical Methods for Integrative Analysis of Large-Scale Multi-Ethnic Whole Genome Sequencing Studies and Biobanks of Common Diseases, Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
OpenAlex ID: W7214579035