High-dimensional statistical inference for linkage disequilibrium score regression and its cross-ancestry extensions
连锁不平衡评分回归及跨祖源扩展的高维统计推断AI 解读 ↓
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连锁不平衡评分回归及跨祖源扩展的高维统计推断
- 研究问题
- 整合 GWAS 汇总统计量与外部参考面板时，哪些数据特征影响 LDSC 推断，跨祖源差异又如何影响方法扩展？
- 方法与思路
- 作者采用固定效应数据整合框架，同时考虑 GWAS 汇总统计量的全基因组依赖、估计评分的块对角依赖和群体间 LD 差异。
- 材料陈述的贡献
- 摘要称分析揭示影响 LDSC 表现的关键因素，指出全基因组与小子集分析的渐近正态性难度不同，并提出跨祖源扩展。
- 阅读关注点
- 应核对参考面板与原始样本的规模要求、依赖结构条件、小子集分析的限制，以及跨祖源建模和真实数据验证方式。
- 阅读边界
- 材料为版本未注明的 OpenAlex 索引摘要；渐近正态性的具体条件及 UK Biobank 数值评估结果未提供，不能推定跨群体普遍有效。
原文摘要
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 multisource 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 show that LDSC can be expanded to conduct cross-ancestry analyses using data from genetically distinct global populations. We validate our theoretical findings through extensive numerical evaluations using real genetic data from the UK Biobank study.