Department of Genetics, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire 03755, USA.
Department of Radiology and Imaging Sciences, Center for Neuroimaging and Indiana Alzheimer's Disease Center, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
BioData Min. 2014 Aug 22;7:17. doi: 10.1186/1756-0381-7-17. eCollection 2014.
Alzheimer's disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer's disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships.
We found a statistically significant synergistic interaction among two SNPs located in the intergenic region of an olfactory gene cluster. This model did not replicate in an independent dataset. However, genes in this region have high-confidence biological relationships and are consistent with previous findings implicating sensory processes in Alzheimer's disease.
Previous genetic studies of Alzheimer's disease have revealed only a small portion of the overall variability due to DNA sequence differences. Some of this missing heritability is likely due to complex gene-gene and gene-environment interactions. We have introduced here a novel bioinformatics analysis pipeline that embraces the complexity of the genetic architecture of Alzheimer's disease while at the same time harnessing the power of functional genomics. These findings represent novel hypotheses about the genetic basis of this complex disease and provide open-access methods that others can use in their own studies.
阿尔茨海默病是最常见的进行性痴呆形式，目前尚无已知的治愈方法。发病原因尚未完全了解，但预计遗传因素将起重要作用。我们在这里提出了一种生物信息学方法，用于分析灰质密度作为迟发性阿尔茨海默病的内表型的遗传分析。我们的方法结合了基因-基因相互作用的机器学习分析和大规模功能基因组学数据，以评估生物关系。
我们发现两个位于嗅觉基因簇基因间区的 SNP 之间存在统计学上显著的协同相互作用。该模型在独立数据集未复制。然而，该区域的基因具有高度置信度的生物学关系，与先前发现的涉及阿尔茨海默病的感觉过程一致。
以前对阿尔茨海默病的遗传研究仅揭示了由于 DNA 序列差异导致的整体可变性的一小部分。这种遗传缺失的一部分可能是由于复杂的基因-基因和基因-环境相互作用。我们在这里引入了一种新的生物信息学分析管道，它既包含了阿尔茨海默病遗传结构的复杂性，同时又利用了功能基因组学的力量。这些发现代表了对这种复杂疾病遗传基础的新假设，并提供了其他人可以在自己的研究中使用的开放获取方法。
