Yang Can, Li Cong, Chen Mengjie, Chen Xiaowei, Hou Lin, Zhao Hongyu
Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA.
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S67. doi: 10.1186/1753-6561-8-S1-S67. eCollection 2014.
Genetic Analysis Workshop 18 provided a platform for evaluating genomic prediction power based on single-nucleotide polymorphisms from single-nucleotide polymorphism array data and sequencing data. Also, Genetic Analysis Workshop 18 provided a diverse pedigree structure to be explored in prediction. In this study, we attempted to combine pedigree information with single-nucleotide polymorphism data to predict systolic blood pressure. Our results suggested that the prediction power based on pedigree information only could be unsatisfactory. Using additional information such as single-nucleotide polymorphism genotypes would improve prediction accuracy. In particular, the improvement can be significant when there exist a few single-nucleotide polymorphisms with relatively larger effect sizes. We also compared the prediction performance based on genome-wide association study data (ie, common variants) and sequencing data (ie, common variants plus low-frequency variants). The experimental result showed that inclusion of low frequency variants could not lead to improvement of prediction accuracy.
遗传分析研讨会18提供了一个平台，用于基于单核苷酸多态性芯片数据和测序数据中的单核苷酸多态性评估基因组预测能力。此外，遗传分析研讨会18提供了一个多样的家系结构以供在预测中探索。在本研究中，我们尝试将家系信息与单核苷酸多态性数据相结合来预测收缩压。我们的结果表明，仅基于家系信息的预测能力可能不尽人意。使用诸如单核苷酸多态性基因型等额外信息将提高预测准确性。特别是，当存在一些效应大小相对较大的单核苷酸多态性时，这种改进可能会很显著。我们还比较了基于全基因组关联研究数据（即常见变异）和测序数据（即常见变异加低频变异）的预测性能。实验结果表明，纳入低频变异并不能提高预测准确性。
