A0753
Title: High-dimensional statistical inference for linkage disequilibrium score regression and its cross-ancestry extensions
Authors: Fei Xue - Purdue University (United States) [presenting]
Bingxin Zhao - University of Pennsylvania (United States)
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 is investigated within a fixed-effect data integration framework, underscoring its ability to merge multi-source GWAS data and reference panels. In particular, genome-wide dependence is considered among the high-dimensional GWAS summary statistics, along with the block-diagonal dependence pattern in estimated LD scores. The analysis uncovers several key factors of both the original GWAS and reference panel datasets that determine the performance of LDSC. It is shown that it is relatively feasible for LDSC-based estimators to achieve asymptotic normality when applied to genome-wide genetic variants, whereas it becomes considerably challenging when the focus is on a much smaller subset of genetic variants (e.g., in partitioned heritability analysis). Moreover, by modelling the disparities in LD patterns across different populations, it is unveiled that LDSC can be expanded to conduct cross-ancestry analyses using data from distinct global populations.
