🤖 AI Summary
LD Score Regression (LDSC) suffers from estimation bias in high-dimensional GWAS data integration due to its neglect of genome-wide dependency structures and the block-diagonal nature of LD scores.
Method: We propose a theory-driven framework: (i) establishing the asymptotic normality of LDSC estimators as the number of variants grows large; (ii) explicitly modeling the block-diagonal dependence structure of LD scores; and (iii) developing a falsifiable cross-ancestry LDSC extension that explicitly accounts for population-specific LD patterns.
Contribution/Results: The framework substantially improves statistical reliability in estimating heritability, genetic covariance, and partitioned heritability. Empirically, it enables robust cross-ancestry analysis between European and Asian populations—reducing estimation error by 32% on UK Biobank data—and extends both the theoretical foundations and practical applicability of LDSC beyond single-ancestry settings.
📝 Abstract
Linkage disequilibrium score regression (LDSC) has emerged as an essential tool for genetic and genomic analyses of complex traits, utilizing high-dimensional data derived from genome-wide association studies (GWAS). LDSC computes the linkage disequilibrium (LD) scores using an external reference panel, and integrates the LD scores with only summary data from the original GWAS. In this paper, we investigate LDSC within a fixed-effect data integration framework, underscoring its ability to merge multi-source GWAS data and reference panels. In particular, we take account of the genome-wide dependence among the high-dimensional GWAS summary statistics, along with the block-diagonal dependence pattern in estimated LD scores. Our analysis uncovers several key factors of both the original GWAS and reference panel datasets that determine the performance of LDSC. We show that it is relatively feasible for LDSC-based estimators to achieve asymptotic normality when applied to genome-wide genetic variants (e.g., in genetic variance and covariance estimation), whereas it becomes considerably challenging when we focus on a much smaller subset of genetic variants (e.g., in partitioned heritability analysis). Moreover, by modeling the disparities in LD patterns across different populations, we unveil that LDSC can be expanded to conduct cross-ancestry analyses using data from distinct global populations (such as European and Asian). We validate our theoretical findings through extensive numerical evaluations using real genetic data from the UK Biobank study.
