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协方差检验的极小极大功效函数分析：及其在纵向全基因组关联研究中的应用

Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies.

作者信息

Zhu Weicheng, Xu Sheng, Liu Catherine, Li Yehua

机构信息

Amazon Inc. Seattle WA, USA.

Global Statistics and Data Science, BeiGene Co., Ltd., China.

出版信息

Scand Stat Theory Appl. 2023 Mar;50(1):266-295. doi: 10.1111/sjos.12583. Epub 2022 Mar 13.

DOI:10.1111/sjos.12583
PMID:39076352
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11286231/
Abstract

We model the Alzheimer's Disease-related phenotype response variables observed on irregular time points in longitudinal Genome-Wide Association Studies as sparse functional data and propose nonparametric test procedures to detect functional genotype effects while controlling the confounding effects of environmental covariates. Our new functional analysis of covariance tests are based on a seemingly unrelated kernel smoother, which takes into account the within-subject temporal correlations, and thus enjoy improved power over existing functional tests. We show that the proposed test combined with a uniformly consistent nonparametric covariance function estimator enjoys the Wilks phenomenon and is minimax most powerful. Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where an application of the proposed test lead to the discovery of new genes that may be related to Alzheimer's Disease.

摘要

我们将纵向全基因组关联研究中在不规则时间点观察到的与阿尔茨海默病相关的表型反应变量建模为稀疏函数数据，并提出非参数检验程序，以在控制环境协变量的混杂效应的同时检测功能基因型效应。我们新的协方差函数分析检验基于一个看似不相关的核平滑器，该平滑器考虑了个体内部的时间相关性，因此比现有的函数检验具有更高的功效。我们表明，所提出的检验与一致的非参数协方差函数估计器相结合，具有威尔克斯现象，并且是极小极大最强大的。本文编写中使用的数据来自阿尔茨海默病神经影像倡议（ADNI）数据库，所提出检验的应用导致发现了可能与阿尔茨海默病相关的新基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/8993f61125a4/nihms-1958414-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/40d0b70dce50/nihms-1958414-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/38c64f859034/nihms-1958414-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/850fbb32d64c/nihms-1958414-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/c27bf5f81938/nihms-1958414-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/8993f61125a4/nihms-1958414-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/40d0b70dce50/nihms-1958414-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/38c64f859034/nihms-1958414-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/850fbb32d64c/nihms-1958414-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/c27bf5f81938/nihms-1958414-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da18/11286231/8993f61125a4/nihms-1958414-f0005.jpg

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本文引用的文献

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FEATURE SELECTION FOR GENERALIZED VARYING COEFFICIENT MIXED-EFFECT MODELS WITH APPLICATION TO OBESITY GWAS.广义变系数混合效应模型的特征选择及其在肥胖全基因组关联研究中的应用
Ann Appl Stat. 2020 Mar;14(1):276-298. doi: 10.1214/19-aoas1310. Epub 2020 Apr 16.
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A Functional Varying-Coefficient Single-Index Model for Functional Response Data.用于函数响应数据的功能变系数单指标模型。
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Generalized Scalar-on-Image Regression Models via Total Variation.
基于全变差的广义图像上标量回归模型
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FGWAS: Functional genome wide association analysis.功能基因组全基因组关联分析（FGWAS）。
Neuroimage. 2017 Oct 1;159:107-121. doi: 10.1016/j.neuroimage.2017.07.030. Epub 2017 Jul 20.
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10 Years of GWAS Discovery: Biology, Function, and Translation.全基因组关联研究十年发现：生物学、功能与转化
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Multiple phenotype association tests using summary statistics in genome-wide association studies.在全基因组关联研究中使用汇总统计量进行多表型关联测试。
Biometrics. 2018 Mar;74(1):165-175. doi: 10.1111/biom.12735. Epub 2017 Jun 26.
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The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants.重新审视并更新了针对低频变异的（著名的）全基因组关联研究（GWAS）P值阈值。
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On Varying-coefficient Independence Screening for High-dimensional Varying-coefficient Models.关于高维变系数模型的变系数独立筛选
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The Alzheimer's Disease Neuroimaging Initiative: a review of papers published since its inception.阿尔茨海默病神经影像学倡议：成立以来发表论文的综述。
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