Pradeep Varathan Pugalenthi, Bing He, Linhui Xie, Kwangsik Nho, Andrew J Saykin, Jingwen Yan
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引用次数: 0
Abstract
Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.
阿尔茨海默病(AD)是一种高度遗传性脑痴呆症,同时伴有认知功能的严重衰竭。大规模的全基因组关联研究(GWAS)发现了一系列与阿尔茨海默病及相关特征有显著关联的 SNPs。全基因组关联研究的结果通常会以群集的形式出现,在这些群集中,一个最重要的 SNP 被其他重要性较低的邻近 SNP 所包围。尽管在 GWAS 中,即使是关联性最强的 SNP 也不能保证其功能性,但主导 SNP 一直是该领域的研究重点,而其余的关联则被推断为多余的。最近的深度基因组注释工具可以从DNA序列的一个片段预测功能,其精确度大大提高,从而可以通过体内诱变来研究SNP等位基因的功能效应。在本项目中,我们探讨了APOE区域周围的顶级AD GWAS命中基因对染色质功能的影响,以及这种影响是否会因遗传背景(即相邻SNP的等位基因)而改变。我们的研究结果表明,在同一LD区块中高度相关的SNPs可能会对下游功能产生不同的影响。尽管一些 GWAS 引导 SNPs 显示出了显性功能效应,与邻近 SNP 等位基因无关,但其他几个 SNPs 在某些遗传背景下确实表现出了增强的功能丧失或增益,这表明 LD 区块中隐藏着潜在的额外信息。
期刊介绍:
BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.
Topical areas include, but are not limited to:
-Development, evaluation, and application of novel data mining and machine learning algorithms.
-Adaptation, evaluation, and application of traditional data mining and machine learning algorithms.
-Open-source software for the application of data mining and machine learning algorithms.
-Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies.
-Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.