EJHG：识别汉族遗传结构模型建立

近日，《欧洲人类遗传学杂志》在线发表了上海生科院计算生物学研究所徐书华研究组的研究成果“A panel of ancestry informative markers to estimate and correct potential effects of population stratification in Han Chinese”。该项工作针对复杂疾病关联研究中由于群体遗传结构（或群体分层）导致假阳性结果的问题，建立了一套识别汉族内部遗传结构和控制复杂疾病关联分析中群体分层的遗传标记。这套标记尤其适用于“post-GWAS”时代的基于候选基因策略的关联研究。

关联分析是研究复杂疾病遗传影响因素并建立“表型-基因型”联系的重要手段。基于人群样本设计的关联研究，尤其是基于“病例-对照”设计的关联研究，经常面临的一个困难是人群遗传结构或群体分层作为一种严重的混杂因素导致的假阳性结果。汉族的历史悠久、起源复杂，加之几千年的不同程度的基因交流和民族融合，使得汉族遗传成分极其复杂。在徐书华前期研究中，已经发现汉族人群内部的遗传结构，并往往导致汉族人群关联分析中的假阳性结果。因此，如何识别并控制人群遗传结构对关联分析结果的影响，成为复杂疾病基因定位研究中不可回避的问题。

该项研究通过在5500例汉族个体的全基因组数据中筛选，建立起可以高度识别汉族人群遗传结构的DNA标记，并进一步通过实验数据和计算机仿真评估了这套标记的效能。研究成果对今后在汉族人群中从事关联分析的研究具有实际应用价值。通过这套标记，直接依据DNA信息对遗传结构进行识别、对样本进行客观分类和筛选，是关联分析合理实验设计的前提，也是保证关联分析结果可靠性的必要条件。

该工作由计算生物学所徐书华研究员与上海交通大学师咏勇教授以及复旦大学金力教授合作完成。计算生物学所博士生秦鹏飞等实施了具体分析工作。该研究工作得到了国家自然科学基金、中国科学院、上海市科委、德国马普学会、香港王宽诚教育基金会等基金的资助。(生物谷Bioon.com)

生物谷推荐英文摘要：

European Journal of Human Genetics doi:10.1038/ejhg.2013.11

A panel of ancestry informative markers to estimate and correct potential effects of population stratification in Han Chinese

Population stratification acts as a confounding factor in genetic association studies and may lead to false-positive or false-negative results. Previous studies have analyzed the genetic substructures in Han Chinese population, the largest ethnic group in the world comprising ~20% of the global human population. In this study, we examined 5540 Han Chinese individuals with about 1 million single-nucleotide polymorphisms (SNPs) and screened a panel of ancestry informative markers (AIMs) to facilitate the discerning and controlling of population structure in future association studies on Han Chinese. Based on genome-wide data, we first confirmed our previous observation of the north–south differentiation in Han Chinese population. Second, we developed a panel of 150 validated SNP AIMs to determine the northern or southern origin of each Han Chinese individual. We further evaluated the performance of our AIMs panel in association studies in simulation analysis. Our results showed that this AIMs panel had sufficient power to discern and control population stratification in Han Chinese, which could significantly reduce false-positive rates in both genome-wide association studies (GWAS) and candidate gene association studies (CGAS). We suggest this AIMs panel be genotyped and used to control and correct population stratification in the study design or data analysis of future association studies, especially in CGAS which is the most popular approach to validate previous reports on genetic associations of diseases in post-GWAS era.