| 谢宁,毕文健,张中文,邵方,魏永越,赵杨,张汝阳,陈峰.全基因组关联研究中极端不平衡数据的统计分析方法（一）[J].Chinese journal of Epidemiology,2024,45(11):1582-1589 |
| 全基因组关联研究中极端不平衡数据的统计分析方法（一） |
| Statistical methods for extremely unbalanced data in genome-wide association study (1) |
| Received:May 06, 2024 |
| DOI：10.3760/cma.j.cn112338-20240506-00235 |
| KeyWord: 极端不平衡数据 全基因组关联研究 渐近分布 罕见变异 |
| English Key Word: Extremely unbalanced data Genome-wide association study Asymptotic distribution Rare variants |
| FundProject:国家自然科学基金（82220108002，82273737） |
| Author Name | Affiliation | E-mail | | Xie Ning | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | | | Bi Wenjian | Department of Medical Genetics, School of Basic Medical Sciences, Peking University, Beijing 100191, China | | | Zhang Zhongwen | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | | | Shao Fang | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | | | Wei Yongyue | Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China | | | Zhao Yang | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
National International Science and Technology Cooperation Base, Nanjing Medical University, Nanjing 211166, China | | | Zhang Ruyang | Information Center, the Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou 213164, China | zhangruyang@njmu.edu.cn | | Chen Feng | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China
National International Science and Technology Cooperation Base, Nanjing Medical University, Nanjing 211166, China | fengchen@njmu.edu.cn |
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| Abstract: |
| 极端不平衡数据定义为自变量或因变量指标的取值呈现严重比例失衡的数据，例如病例-对照极度不平衡、疾病发病率极低、生存数据大量删失以及遗传位点为低频或罕见变异等。在此情境下，logistic回归模型、Cox比例风险回归模型等参数假设检验的经典统计量偏离理论渐近分布，难以控制第一类错误。近年来，随着超大型人群队列全基因组关联研究（GWAS）资源的日益共享与深度挖掘，高效准确处理独立或非独立样本极端不平衡数据的统计需求日益突出。本文介绍遗传统计中的经典统计分析方法，通过模拟试验展示经典统计方法在极端不平衡数据情境下的失效，旨在引起研究者对GWAS中极端不平衡数据的重视。 |
| English Abstract: |
| Extremely unbalanced data here refers to datasets where the values of independent or dependent variables exhibit severe unbalance in proportions, such as extremely unbalanced case-control ratio, very low incidence rate of disease, heavily censored time-to-event data, and low-frequency or rare variants. In such scenarios, the statistic derived from hypothesis test using the classical statistical method, e.g., logistic regression model and Cox proportional hazard regression model, might deviate from theoretical asymptotic distribution, resulting in inflation or deflation of type I error. With the increased availability and exploration of resources from large-scale population cohorts in genome-wide association study (GWAS), there is a growing demand for effective and accurate statistical approaches to handle extremely unbalanced data in independent and non-independent samples. Our study introduces classical statistical methods in genetic statistics firstly, then, summarizes the failure of classical statistical methods in dealing with extremely unbalanced data through simulation experiments to draw researchers' attention to the extremely unbalanced data in GWAS. |
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