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利用Monte-Carlo模拟评价基因水平的关联分析方法

Evaluation of Gene Based Association Tests by Monte-Carlo Simulation
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摘要 采用Monte-Carlo模拟了多种参数条件下的遗传关联数据,计算多种基因水平的关联分析方法的统计效能.结果显示:主成分Logistic回归分析(累积贡献率为95%)和我们之前发展的基于LD结构和Fisher组合法的新方法(LD-Fisher)在多种参数条件下都具有很高的统计效能,Fisher组合法只有在单个易感SNP的P值较小时或者存在多个P值不太小的易感SNP时表现较好.主成分Logistic回归分析和LD-Fisher可以作为基因水平关联分析的较为理想的分析方法. Monte-Carlo is adopted to evaluate the power of multiple gene based association tests. With PCA logistic regression （95% cumulative variance） and our previously developed method, the LD-Fisher is found to have the highest power, and performs well under all simulated circumstances. Fisher combination test presents high power only if the P value of the susceptibility SNP takes very small value or there are multiple susceptibility SNPs. In conclusion, PCA logistic regression and LD-Fisher can be used as gene based association tests for complex diseases.
作者 桂娟娟 罗旭红 张添方 华启航 董长征
机构地区 宁波大学医学院
出处 《宁波大学学报（理工版）》 CAS 2015年第4期104-108,共5页 Journal of Ningbo University：Natural Science and Engineering Edition
基金 国家自然科学基金(31000594) 浙江省自然科学基金(Y2100240) 宁波市自然科学基金(2015A610190) 宁波大学学科项目(XKL141060)
关键词 遗传关联数据 基因水平的关联分析 MONTE-CARLO模拟 统计效能 genetic association data gene based association test Monte-Carlo simulation power
分类号 Q51 [生物学—生物化学]
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参考文献15

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宁波大学学报（理工版）
2015年 第4期

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