The population reference sample, POPRES: A resource for population, disease, and pharmacological genetics research
The population reference sample, POPRES: A resource for population, disease, and pharmacological genetics research
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DOI：
10.1016/j.ajhg.2008.08.005
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发表时间：
2008-09-12
影响因子：
9.8
通讯作者：
Lail, Eric H.
中科院分区：
文献类型：
--
作者：
Nelson, Matthew R.;Bryc, Katarzyna;Lail, Eric H.
技术和科学的进步，很大程度上源于人类基因组和HapMap项目，使得大规模的全基因组研究变得可行且成本有效。通过识别导致疾病风险变化的遗传因素、药物药代动力学、治疗效果和药物不良反应，这些进展有可能极大地影响药物的发现和开发。尽管技术取得了进步，但如果不能获得适当的样本，生物医学研究中的成功应用将受到限制。为了促进探索性遗传学研究，我们从世界各地参与多项研究的大量受试者中收集了DNA资源。这种不断增长的资源最初是用市售的全基因组500,000个单核苷酸多态性面板进行基因分型的。该项目包括近6000名非裔美国人、东亚人、南亚人、墨西哥人和欧洲人。通过对这些数据的主成分分析（PCA）确定了七个信息变异轴，证实了数据的整体完整性，并突出了不同群体遗传结构的重要特征。这种广泛的基因分型收集的潜在价值是通过在abacavirus相关的超敏反应全基因组分析中选择遗传匹配的群体对照来说明的。我们发现，基于原产国、各州身份距离和多维PCA的匹配同样可以很好地控制I型错误率。该参考样本的基因型和人口统计学数据可通过NCBI基因型和表型数据库（dbGaP）免费获取。
Technological and scientific advances, stemming in large part from the Human Genome and HapMap projects, have made large-scale, genome-wide investigations feasible and cost effective. These advances have the potential to dramatically impact drug discovery and development by identifying genetic factors that contribute to variation in disease risk as well as drug pharmacokinetics, treatment efficacy, and adverse drug reactions. In spite of the technological advancements, successful application in biomedical research Would be limited without access to suitable sample collections. To facilitate exploratory genetics research, we have assembled a DNA resource from a large number of subjects participating in multiple studies throughout the world. This growing resource was initially genotyped with a commercially available genome-wide 500,000 single-nucleotide polymorphism panel. This project includes nearly 6,000 subjects of African-American, East Asian, South Asian, Mexican, and European origin. Seven informative axes of variation identified via principal-component analysis (PCA) of these data confirm the overall integrity of the data and highlight important features of the genetic structure of diverse populations. The potential value of such extensively genotyped collections is illustrated by selection of genetically matched population controls in a genome-wide analysis of abacavir-associated hypersensitivity reaction. We find that matching based on country of origin, identity-by-state distance, and multidimensional PCA do similarly well to control the type I error rate. The genotype and demographic data from this reference sample are freely available through the NCBI database of Genotypes and Phenotypes (dbGaP).