Vaitsiakhovich Tatsiana, Drichel Dmitriy, Angisch Marina, Becker Tim, Herold Christine, Lacour André
Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University of Bonn, Sigmund-Freud-Str., D-53105 Bonn, Germany.
German Center for Neurodegenerative Diseases (DZNE), Ludwig-Erhard-Allee 2, D-53175 Bonn, Germany.
BMC Proc. 2014 Jun 17;8(Suppl 1):S83. doi: 10.1186/1753-6561-8-S1-S83. eCollection 2014.
We present a genome-wide association study of a quantitative trait, "progression of systolic blood pressure in time," in which 142 unrelated individuals of the Genetic Analysis Workshop 18 real genotype data were analyzed. Information on systolic blood pressure and other phenotypic covariates was missing at certain time points for a considerable part of the sample. We observed that the dropout process causing missingness is not independent of the initial systolic blood pressure; that is, the data is not missing completely at random. However, after the adjustment for age, the impact of systolic blood pressure on dropouts was no longer significant. Therefore, we decided to impute missing phenotype values by using information from individuals with complete phenotypic data. Progression of systolic blood pressure (∆SBP/∆t) was defined based on the imputed phenotypes and analyzed in a genome-wide fashion. We also conducted an exhaustive genome-wide search for interaction between single-nucleotide polymorphisms (7.14 × 10(10) tests) under an allelic model. The suggested data imputation and the association analysis strategy proved to be valid in the sense that there was no evidence of genome-wide inflation or increased type I error in general. Furthermore, we detected 2 single-nucleotide polymorphisms (SNPs) that met the criterion for genome-wide significance (p≤5 × 10(-8)), which was also confirmed via Monte-Carlo simulation. In view of the rather small sample size, however, the results have to be followed-up in larger studies.
我们开展了一项针对数量性状“收缩压随时间的变化”的全基因组关联研究，对遗传分析研讨会18的真实基因型数据中的142名无亲缘关系个体进行了分析。在相当一部分样本的某些时间点，收缩压及其他表型协变量的信息存在缺失。我们观察到，导致数据缺失的脱落过程并非独立于初始收缩压；也就是说，数据并非完全随机缺失。然而，在对年龄进行调整后，收缩压对脱落的影响不再显著。因此，我们决定利用具有完整表型数据个体的信息来插补缺失的表型值。基于插补后的表型定义了收缩压变化（∆SBP/∆t），并以全基因组方式进行分析。我们还在等位基因模型下对单核苷酸多态性之间的相互作用进行了详尽的全基因组搜索（7.14×10¹⁰次检验）。所建议的数据插补和关联分析策略在总体上没有全基因组膨胀或I型错误增加的证据这一意义上被证明是有效的。此外，我们检测到2个符合全基因组显著性标准（p≤5×10⁻⁸）的单核苷酸多态性（SNP），这也通过蒙特卡洛模拟得到了证实。然而，鉴于样本量相当小，这些结果必须在更大规模的研究中进行后续验证。
