Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad534

Jun Young Park, Jang Jae Lee, Younghwa Lee, Dongsoo Lee, Jungsoo Gim, Lindsay Farrer, Kun Ho Lee, Sungho Won

{"title":"Machine learning-based quantification for disease uncertainty increases the statistical power of genetic association studies.","authors":"Jun Young Park, Jang Jae Lee, Younghwa Lee, Dongsoo Lee, Jungsoo Gim, Lindsay Farrer, Kun Ho Lee, Sungho Won","doi":"10.1093/bioinformatics/btad534","DOIUrl":null,"url":null,"abstract":"Motivation: Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model.Results: Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A.Availability and implementation: Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539075/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btad534","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

Motivation: Allowance for increasingly large samples is a key to identify the association of genetic variants with Alzheimer's disease (AD) in genome-wide association studies (GWAS). Accordingly, we aimed to develop a method that incorporates patients with mild cognitive impairment and unknown cognitive status in GWAS using a machine learning-based AD prediction model.

Results: Simulation analyses showed that weighting imputed phenotypes method increased the statistical power compared to ordinary logistic regression using only AD cases and controls. Applied to real-world data, the penalized logistic method had the highest AUC (0.96) for AD prediction and weighting imputed phenotypes method performed well in terms of power. We identified an association (P<5.0×10-8) of AD with several variants in the APOE region and rs143625563 in LMX1A. Our method, which allows the inclusion of individuals with mild cognitive impairment, improves the statistical power of GWAS for AD. We discovered a novel association with LMX1A.

Availability and implementation: Simulation codes can be accessed at https://github.com/Junkkkk/wGEE_GWAS.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于机器学习的疾病不确定性量化增加了遗传关联研究的统计能力。

动机：在全基因组关联研究（GWAS）中，考虑到越来越大的样本是确定遗传变异与阿尔茨海默病（AD）关联的关键。因此，我们旨在开发一种方法，使用基于机器学习的AD预测模型，将轻度认知障碍和未知认知状态的患者纳入GWAS。结果：模拟分析表明，与仅使用AD病例和对照的普通逻辑回归相比，加权估算表型方法增加了统计能力。应用于真实世界的数据，惩罚逻辑方法具有最高的AD预测AUC（0.96），加权估算表型方法在功率方面表现良好。我们确定了一个关联（PA可用性和实现：模拟代码可以访问https://github.com/Junkkkk/wGEE_GWAS.

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.