Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan.
BMC Genomics. 2012 Jul 28;13:346. doi: 10.1186/1471-2164-13-346.
Ancestry informative markers (AIMs) are a type of genetic marker that is informative for tracing the ancestral ethnicity of individuals. Application of AIMs has gained substantial attention in population genetics, forensic sciences, and medical genetics. Single nucleotide polymorphisms (SNPs), the materials of AIMs, are useful for classifying individuals from distinct continental origins but cannot discriminate individuals with subtle genetic differences from closely related ancestral lineages. Proof-of-principle studies have shown that gene expression (GE) also is a heritable human variation that exhibits differential intensity distributions among ethnic groups. GE supplies ethnic information supplemental to SNPs; this motivated us to integrate SNP and GE markers to construct AIM panels with a reduced number of required markers and provide high accuracy in ancestry inference. Few studies in the literature have considered GE in this aspect, and none have integrated SNP and GE markers to aid classification of samples from closely related ethnic populations.
We integrated a forward variable selection procedure into flexible discriminant analysis to identify key SNP and/or GE markers with the highest cross-validation prediction accuracy. By analyzing genome-wide SNP and/or GE markers in 210 independent samples from four ethnic groups in the HapMap II Project, we found that average testing accuracies for a majority of classification analyses were quite high, except for SNP-only analyses that were performed to discern study samples containing individuals from two close Asian populations. The average testing accuracies ranged from 0.53 to 0.79 for SNP-only analyses and increased to around 0.90 when GE markers were integrated together with SNP markers for the classification of samples from closely related Asian populations. Compared to GE-only analyses, integrative analyses of SNP and GE markers showed comparable testing accuracies and a reduced number of selected markers in AIM panels.
Integrative analysis of SNP and GE markers provides high-accuracy and/or cost-effective classification results for assigning samples from closely related or distantly related ancestral lineages to their original ancestral populations. User-friendly BIASLESS (Biomarkers Identification and Samples Subdivision) software was developed as an efficient tool for selecting key SNP and/or GE markers and then building models for sample subdivision. BIASLESS was programmed in R and R-GUI and is available online at http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm.
祖先信息标记物（AIMs）是一种遗传标记物，可用于追踪个体的祖先种族。AIMs 在群体遗传学、法医学和医学遗传学中得到了广泛关注。单核苷酸多态性（SNP）是 AIMs 的材料，对于将来自不同大陆起源的个体进行分类很有用，但无法区分来自密切相关的祖先谱系的个体之间的细微遗传差异。原理验证研究表明，基因表达（GE）也是一种可遗传的人类变异，在族群之间表现出不同的强度分布。GE 提供了 SNP 之外的种族信息；这促使我们整合 SNP 和 GE 标记物，构建所需标记物数量较少的 AIM 面板，并提供高精度的祖先推断。文献中很少有研究考虑到这一方面的 GE，也没有将 SNP 和 GE 标记物整合起来，以帮助对来自密切相关的种族群体的样本进行分类。
我们将一个正向变量选择过程整合到灵活判别分析中，以确定具有最高交叉验证预测准确性的关键 SNP 和/或 GE 标记物。通过分析来自 HapMap II 项目的四个族群的 210 个独立样本的全基因组 SNP 和/或 GE 标记物，我们发现，大多数分类分析的平均测试准确率都相当高，除了 SNP 仅分析，用于辨别包含来自两个亚洲近缘人群的个体的研究样本。SNP 仅分析的平均测试准确率为 0.53 至 0.79，当将 GE 标记物与 SNP 标记物一起整合用于分类来自亚洲近缘人群的样本时，准确率增加到 0.90 左右。与 GE 仅分析相比，SNP 和 GE 标记物的综合分析显示出可比的测试准确率和 AIM 面板中选择的标记物数量减少。
SNP 和 GE 标记物的综合分析为将来自密切相关或远缘祖先谱系的样本分配到其原始祖先群体提供了高精度和/或具有成本效益的分类结果。用户友好的 BIASLESS（生物标志物识别和样本细分）软件已被开发为一种有效的工具，用于选择关键的 SNP 和/或 GE 标记物，然后构建样本细分模型。BIASLESS 是用 R 和 R-GUI 编写的，并可在 http://www.stat.sinica.edu.tw/hsinchou/genetics/prediction/BIASLESS.htm 上在线获取。
