Inference of nonlinear causal effects with GWAS summary data
利用GWAS汇总数据推断非线性因果效应AI 解读 ↓
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利用GWAS汇总数据推断非线性因果效应
- 研究问题
- 利用SNP作为工具变量时，如何在只能获得GWAS汇总数据、关系可能非线性且工具有效性可能受损的情形下推断基因效应？
- 方法与思路
- 作者将边际因果效应与非线性变换分开估计，分别结合切片逆回归、稀疏工具变量回归及比率调整的逆回归，构造推断程序。
- 材料陈述的贡献
- 摘要称，模型涵盖两阶段最小二乘并容许一定工具有效性违背；基因数据应用报告线性方法遗漏的关联，并提供Python实现。
- 阅读关注点
- 应核对非线性模型的识别条件、可容许的无效工具类型、边际因果效应定义，以及汇总数据与表达数据结合时的匹配要求。
- 阅读边界
- 这是arXiv预印本，同行评审或接收状态未核实；摘要未明确稳健性的条件及检验细节，作者报告的因果基因仍需审查识别依据。
原文摘要
Large-scale genome-wide association studies (GWAS) have offered an exciting opportunity to discover putative causal genes or risk factors associated with diseases by using SNPs as instrumental variables (IVs). However, conventional approaches assume linear causal relations partly for simplicity and partly for the availability of GWAS summary data. In this work, we propose a novel model {for transcriptome-wide association studies (TWAS)} to incorporate nonlinear relationships across IVs, an exposure/gene, and an outcome, which is robust against violations of the valid IV assumptions, permits the use of GWAS summary data, and covers two-stage least squares as a special case. We decouple the estimation of a marginal causal effect and a nonlinear transformation, where the former is estimated via sliced inverse regression and a sparse instrumental variable regression, and the latter is estimated by a ratio-adjusted inverse regression. On this ground, we propose an inferential procedure. An application of the proposed method to the ADNI gene expression data and the IGAP GWAS summary data identifies 18 causal genes associated with Alzheimer's disease, including APOE and TOMM40, in addition to 7 other genes missed by two-stage least squares considering only linear relationships. Our findings suggest that nonlinear modeling is required to unleash the power of IV regression for identifying potentially nonlinear gene-trait associations. Accompanying this paper is our Python library \texttt{nl-causal} (\url{https://nonlinear-causal.readthedocs.io/}) that implements the proposed method.