Consistency of heritability estimation from summary statistics in high-dimensional linear models
高维线性模型中基于汇总统计量的遗传力估计的一致性AI 解读 ↓
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高维线性模型中基于汇总统计量的遗传力估计的一致性
- 研究问题
- 摘要称，本文研究GWAS中高维多基因线性模型下，基于汇总统计量的两种遗传力估计量——LDSC回归与GWASH——的渐近性质，考察其在基本形式及加权、标准化等常见修正下何时具有一致性。
- 方法与思路
- 作者提出弱依赖（WD）与有界峰度效应（BKE）两个条件，在高斯与非高斯预测变量下推导基本LDSC（固定截距）与GWASH的一致性，并讨论加权、标准化及群体分层对估计的影响，辅以模拟。
- 材料陈述的贡献
- 摘要称，WD与BKE在若干变体下是两种估计量一致性的充分条件；对高斯预测变量也是GWASH（含截断）一致性的必要条件；适当截断的加权不改变一致性，而标准化在条件不满足时引入偏误；群体分层下所有考察的估计量均有偏，自由截距LDSC亦不能纠正。
- 阅读关注点
- 应核对WD与BKE的精确定义、渐近框架与正则条件、截断权重的具体形式、标准化与群体分层偏误的推导假设，以及模拟设置能否支持非高斯情形下的必要性论断。
- 阅读边界
- 仅依据arXiv预印本摘要，未读全文，不能据此声称已通过同行评审或期刊接收；摘要未说明具体数据、样本量、数值结果与证明细节，结论的适用范围需以全文为准。
原文摘要
In Genome-Wide Association Studies (GWAS), heritability is defined as the fraction of variance of an outcome explained by a large number of genetic predictors in a high-dimensional polygenic linear model. This work studies the asymptotic properties of the most common estimator of heritability from summary statistics called linkage disequilibrium score (LDSC) regression, together with a simpler and closely related estimator called GWAS heritability (GWASH). These estimators are analyzed in their basic versions and under various modifications used in practice including weighting and standardization. We show that, with some variations, two conditions which we call weak dependence (WD) and bounded-kurtosis effects (BKE) are sufficient for consistency of both the basic LDSC with fixed intercept and GWASH estimators, for both Gaussian and non-Gaussian predictors. For Gaussian predictors it is shown that these conditions are also necessary for consistency of GWASH (with truncation) and simulations suggest that necessity holds too when the predictors are non-Gaussian. We also show that, with properly truncated weights, weighting does not change the consistency results, but standardization of the predictors and outcome, as done in practice, introduces bias in both LDSC and GWASH if the two essential conditions are violated. Finally, we show that, when population stratification is present, all the estimators considered are biased, and the bias is not remedied by using the LDSC regression estimator with free intercept, as originally suggested by the authors of that estimator. These findings allow the user of the above estimates to evaluate whether they are useful for the data at hand.