DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies

因果推理 计算机科学 参数统计 统计假设检验 回归 统计推断 推论 算法 人工智能 计量经济学 数学 机器学习 统计
作者
Ruoyu He,Mingyang Liu,Zhaotong Lin,Zhong Zhuang,Xiaotong Shen,Wei Pan
出处
期刊:Biostatistics [Oxford University Press]
卷期号:25 (2): 468-485 被引量:5
标识
DOI:10.1093/biostatistics/kxac051
摘要

Summary Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene’s expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
吹雪完成签到,获得积分0
刚刚
满天星辰独览完成签到 ,获得积分10
1秒前
明理采珊完成签到,获得积分10
1秒前
和谐乌冬面完成签到 ,获得积分10
3秒前
3秒前
畜牧笑笑完成签到 ,获得积分10
4秒前
xiaoyuan完成签到,获得积分10
4秒前
科研小白完成签到,获得积分10
5秒前
所所应助如风随水采纳,获得10
5秒前
我陈雯雯实名上网完成签到,获得积分10
5秒前
啦啦啦完成签到 ,获得积分10
5秒前
falling_learning完成签到,获得积分10
5秒前
丫头完成签到,获得积分10
6秒前
晨曦关注了科研通微信公众号
6秒前
chilin完成签到,获得积分10
6秒前
清爽笑翠完成签到 ,获得积分10
7秒前
seattle完成签到,获得积分10
8秒前
front发布了新的文献求助10
8秒前
9秒前
9秒前
shfgref完成签到,获得积分10
9秒前
shangchen完成签到,获得积分10
10秒前
10秒前
Lqiang完成签到,获得积分10
10秒前
呆鹅喵喵完成签到,获得积分10
10秒前
典雅的迎波完成签到,获得积分10
10秒前
考研小白完成签到,获得积分10
11秒前
飘逸的苡完成签到 ,获得积分10
11秒前
小北完成签到,获得积分10
12秒前
田様应助和谐的映秋采纳,获得10
13秒前
Owen应助Mandarine采纳,获得10
14秒前
阿斯披粼完成签到,获得积分10
14秒前
yunjian1583完成签到,获得积分10
15秒前
wonderful完成签到,获得积分10
15秒前
MQQ完成签到 ,获得积分10
16秒前
FR完成签到,获得积分10
16秒前
西瓜橙子完成签到,获得积分10
16秒前
如风随水发布了新的文献求助10
16秒前
16秒前
manfullmoon完成签到,获得积分10
17秒前
高分求助中
The Mother of All Tableaux Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 2400
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications 800
Official Methods of Analysis of AOAC INTERNATIONAL 600
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 588
Residual Stress Measurement by X-Ray Diffraction, 2003 Edition HS-784/2003 588
T/CIET 1202-2025 可吸收再生氧化纤维素止血材料 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3950076
求助须知:如何正确求助?哪些是违规求助? 3495418
关于积分的说明 11077056
捐赠科研通 3225984
什么是DOI,文献DOI怎么找? 1783357
邀请新用户注册赠送积分活动 867663
科研通“疑难数据库(出版商)”最低求助积分说明 800855