Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Department of Data Science, The Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan.
Eur J Hum Genet. 2019 Jan;27(1):140-149. doi: 10.1038/s41431-018-0251-y. Epub 2018 Sep 10.
Although the detection of predictive biomarkers is of particular importance for the development of accurate molecular diagnostics, conventional statistical analyses based on gene-by-treatment interaction tests lack sufficient statistical power for this purpose, especially in large-scale clinical genome-wide studies that require an adjustment for multiplicity of a huge number of tests. Here we demonstrate an alternative efficient multi-subgroup screening method using multidimensional hierarchical mixture models developed to overcome this issue, with application to stroke and breast cancer randomized clinical trials with genomic data. We show that estimated effect size distributions of single nucleotide polymorphisms (SNPs) associated with outcomes, which could provide clues for exploring predictive biomarkers, optimizing individualized treatments, and understanding biological mechanisms of diseases. Furthermore, using this method we detected three new SNPs that are associated with blood homocysteine levels, which are strongly associated with the risk of stroke. We also detected six new SNPs that are associated with progression-free survival in breast cancer patients.
虽然预测生物标志物的检测对于开发准确的分子诊断具有重要意义，但基于基因与治疗相互作用检验的传统统计分析在这方面缺乏足够的统计能力，特别是在需要对大量检验进行多重调整的大规模临床全基因组研究中。在这里，我们展示了一种替代的高效多亚组筛选方法，该方法使用多维分层混合模型来克服这一问题，并应用于具有基因组数据的中风和乳腺癌随机临床试验。我们表明，与结局相关的单核苷酸多态性（SNP）的估计效应大小分布可以为探索预测生物标志物、优化个体化治疗和理解疾病的生物学机制提供线索。此外，使用这种方法，我们检测到了三个与血液同型半胱氨酸水平相关的新的 SNP，这些 SNP 与中风风险密切相关。我们还检测到了六个与乳腺癌患者无进展生存期相关的新的 SNP。
