| 秦雪英,陈大方,胡永华.孟德尔随机化方法在流行病学病因推断中的应用[J].Chinese journal of Epidemiology,2006,27(7):630-633 |
| 孟德尔随机化方法在流行病学病因推断中的应用 |
| Application of Mendelian randomization in the etiological study |
| Received:December 02, 2005 |
| DOI： |
| KeyWord: 孟德尔随机化 病因推断 流行病学 |
| English Key Word: Mendelian randomization Etiological inferences Epidemiology |
| FundProject: |
| Author Name | Affiliation | E-mail | | QIN Xue-ying | Department of Epidemiology and Bio-statistics, School of Public Health, Peking University, Beijing 100083, China | | | CHEN Da-fang | Department of Epidemiology and Bio-statistics, School of Public Health, Peking University, Beijing 100083, China | | | HU Yong-hua | Department of Epidemiology and Bio-statistics, School of Public Health, Peking University, Beijing 100083, China | yhhu@bjmu.edu.cn |
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| Abstract: |
| 目的阐述在观察性流行病学研究中如何运用孟德尔随机化方法进行科学合理的病因推断,以控制混杂因素和反向因果关联对结果的影响。方法以孟德尔独立分配定律为基础,已知不同基因型导致不同的中间表型(即待研究的暴露因素),用基因-疾病的因果链模拟暴露因素对疾病的作用,推导出暴露对疾病的真实效应值。结果基因-疾病的效应估计值能够反映暴露因素和疾病问的真实联系。由于配子形成时等位基因的随机分配,该效应估计值不会受到传统流行病学研究中的混杂因素的影响。结论孟德尔随机化的应用能够增强观察性流行病学中的病因推断,增进对潜在危险因素的认识,同时可能为研究设计和资料分析提供新思路,具有较大的应用前景。 |
| English Abstract: |
| Objective To explain how to use Mendelian randomization for reasonable etiological inferences to avoid confounding and reverse causation often seen in observational epidemiological studies. Methods Based on Law of segregation and current information that different genotype leads to changes of intermediate phenotype(standing for certain environmental exposure), gene-disease associations can mimic the impact of exposure on disease, and then deduce the unconfounding associations between exposure and disease Results A causal association between gene and disease can indeed mimic the effect of environmental exposure on the disease. Since the random assortment of alleles at the time of gamete formation,the effect values of genotype-disease will not be distorted by confounding factors,and may reflect the real association between exposure and disease. Conclusion Mendelian randomization principle can strengthen inferences in observational epidemiological studies for well understanding the important etiological factors,as well as provide new approaches for study design and data analysis,so it will be of great prospect. |
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