全基因组关联研究中SNPs的网络分类

Abhijit R. Tendulkar, N. Stojanovic, Robert Barber
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引用次数: 0

摘要

在过去几年中,复杂表型遗传基础的全基因组关联研究,特别是人类疾病的全基因组关联研究一直在稳步发展。然而,人类基因组中多态性位点的数量,包括但不限于单核苷酸多态性(SNPs),是如此之大,以至于识别这些少数对感兴趣的条件有重大影响的组合仍然是一项艰巨的任务。在这篇论文中,我们提出了一个新的网络解决方案,以及一个实现它的程序GeneNAB,以计算识别和排序可能与感兴趣的表型相关的snp,全基因组。我们期望该程序的输出将有助于指导这些snp的进一步实验室和临床研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Web–Enabled Classification of SNPs for Genome–Wide Association Studies
Whole genome association studies of the genetic underpinnings of complex phenotypes, and human diseases in particular, have been steadily gaining momentum over the past several years. Yet, the number of polymorphic sites in the human genome, including, but not limited to, single nucleotide polymorphisms (SNPs) is so large that identifying the combination of these few that have a significant effect on the condition of interest remains an overwhelming task. In this manuscript we present a new networked solution, and a program GeneNAB implementing it, to the computational identification and ranking of SNPs likely to be relevant for the phenotype of interest, genome-wide. We expect that the output of this program will be useful to guide further laboratory and clinical studies of these SNPs.
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