A PCA-based method for ancestral informative markers selection in structured populations  

作　　者：ZHANG Feng1,3, ZHANG Lei1,3 & DENG Hong-Wen1,2,3 1 Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Molecular Genetics, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China 2 Institute of Bioscience and Biotechnology, School of Science, Beijing Jiaotong University, Beijing 100044, China 3 Departments of Orthopedic Surgery and Basic Medical Science, School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA 

出　　处：《Science China(Life Sciences)》2009年第10期972-976,共5页中国科学（生命科学英文版）

基　　金：Supported by Xi’an Jiaotong University, NIH (Grant Nos. R01 AR050496, R21 AG 027110, R01 AG026564 and P50 AR055081);Fok Ying Tung Education Foundation and Framingham Heart Study and the Framingham SHARe Project

摘　　要：Identification of population structure can help trace population histories and identify disease genes. Structured association (SA) is a commonly used approach for population structure identification and association mapping. A major issue with SA is that its performance greatly depends on the informa-tiveness and the numbers of ancestral informative markers (AIMs). Present major AIM selection meth-ods mostly require prior individual ancestry information, which is usually not available or uncertain in practice. To address this potential weakness, we herein develop a novel approach for AIM selection based on principle component analysis (PCA), which does not require prior ancestry information of study subjects. Our simulation and real genetic data analysis results suggest that, with equivalent AIMs, PCA-based selected AIMs can significantly increase the accuracy of inferred individual ancestries compared with traditionally randomly selected AIMs. Our method can easily be applied to whole genome data to select a set of highly informative AIMs in population structure, which can then be used to identify potential population structure and correct possible statistical biases caused by population stratification.Identification of population structure can help trace population histories and identify disease genes. Structured association (SA) is a commonly used approach for population structure identification and association mapping. A major issue with SA is that its performance greatly depends on the informa-tiveness and the numbers of ancestral informative markers (AIMs). Present major AIM selection meth-ods mostly require prior individual ancestry information, which is usually not available or uncertain in practice. To address this potential weakness, we herein develop a novel approach for AIM selection based on principle component analysis (PCA), which does not require prior ancestry information of study subjects. Our simulation and real genetic data analysis results suggest that, with equivalent AIMs, PCA-based selected AIMs can significantly increase the accuracy of inferred individual ancestries compared with traditionally randomly selected AIMs. Our method can easily be applied to whole genome data to select a set of highly informative AIMs in population structure, which can then be used to identify potential population structure and correct possible statistical biases caused by population stratification.

关 键 词：POPULATION structure PRINCIPLE COMPONENT analysis ancestral INFORMATIVE MARKERS 

分 类 号：C921[社会学—人口学]