The past decade has seen the maturation of multiple different forms of high‐dimensional molecular profiling to the point that these methods could be deployed in initially hundreds and more recently thousands of human samples. In the field of Alzheimer's disease (AD), these profiles have been applied to the target organ: the aging brain. In a growing number of cases, the same samples were profiled with multiple different approaches, yielding genetic, transcriptomic, epigenomic and proteomic data. Here, we review lessons learned so far as we move beyond quantitative trait locus (QTL) analyses which map the effect of genetic variation on molecular features to integrate multiple levels of “omic” data in an effort to identify the molecular drivers of AD. One thing is clear: no single layer of molecular or “omic” data is sufficient to capture the variance of AD or aging‐related cognitive decline. Nonetheless, reproducible findings are emerging from current efforts, and there is evidence of convergence using different approaches. Thus, we are on the cusp of an acceleration of truly integrative studies as the availability of large numbers of well‐characterized brain samples profiled in three or more dimensions enables the testing, comparison and refinement of analytic methods with which to dissect the molecular architecture of the aging brain.

中文翻译：

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综合多组学方法探索阿尔茨海默病机制的考虑。

在过去的十年里，多种不同形式的高维分子分析已经成熟到可以将这些方法部署在最初的数百个和最近数千个人类样本中的程度。在阿尔茨海默病 (AD) 领域，这些配置文件已应用于目标器官：老化的大脑。在越来越多的情况下，使用多种不同的方法对相同的样本进行分析，从而产生遗传、转录组、表观基因组和蛋白质组数据。在这里，我们回顾了迄今为止所学到的经验教训，因为我们超越了数量性状位点 (QTL) 分析，该分析绘制了遗传变异对分子特征的影响，以整合多个级别的“组学”数据，以确定 AD 的分子驱动因素。有一点很清楚：没有任何一层分子或“组学”数据足以捕捉 AD 或与衰老相关的认知衰退的差异。尽管如此，目前的努力正在出现可重复的发现，并且有证据表明使用不同的方法会趋于一致。因此，我们正处于加速真正综合研究的风口浪尖，因为在三个或更多维度上进行了大量表征良好的大脑样本的可用性使得能够测试、比较和改进分析方法来剖析分子结构。老化的大脑。