Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Cells. 2022 Jul 16;11(14):2219. doi: 10.3390/cells11142219.
Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success.
Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction networks to create higher-level gene modules.
We demonstrate our method's utility in three case-control studies of Alzheimer's disease (AD) for three different race/ethnic populations (Whites, African descent and Hispanics). We discovered 182 AD-associated genes from gene modules shared between these populations, highlighting new gene modules associated with AD.
Our innovative framework has the potential to identify robust discoveries across populations based on gene modules, as demonstrated in AD.
全基因组关联研究已经成功鉴定出与多种疾病相关的变异。然而，由于遗传组成的巨大差异，将发现推广到不同人群仍然具有挑战性。进行基因表达推断的方法试图解决基因发现的可转移性问题，但收效甚微。
在这里，我们引入了一种结合基因表达推断和基因模块发现的流水线，包括密集基因模块搜索和基因集变异分析，以解决可转移性问题。我们的方法将推断的基因表达的关联概率与选定的表型一起输入蛋白质相互作用网络中的组织特异性基因模块发现中，以创建更高层次的基因模块。
我们在针对三个不同种族/民族（白人、非洲裔和西班牙裔）的阿尔茨海默病（AD）的三个病例对照研究中证明了我们方法的实用性。我们从这些人群之间共享的基因模块中发现了 182 个与 AD 相关的基因，突出了与 AD 相关的新基因模块。
我们的创新框架有可能基于基因模块在人群中识别稳健的发现，正如 AD 中所证明的那样。
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