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首页> 美国卫生研究院文献>G3: GenesGenomesGenetics >Integrating Rare-Variant Testing Function Prediction and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)
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Integrating Rare-Variant Testing Function Prediction and Gene Network in Composite Resequencing-Based Genome-Wide Association Studies (CR-GWAS)

机译：在基于复合测序的全基因组关联研究（CR-GWAS）中整合稀有变异测试功能预测和基因网络

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High-density array-based genome-wide association studies (GWAS) are complemented by exome sequencing and whole-genome resequencing-based association studies. Here we present a composite resequencing-based genome-wide association study (CR-GWAS) strategy that systematically exploits collective biological information and analytical tools for a robust analysis. We showcased the utility of this strategy by using Arabidopsis (Arabidopsis thaliana) resequencing data. Bioinformatic predictions of biological function alteration at each locus were integrated into the process of association testing of both common and rare variants for complex traits with a suite of statistics. Significant signals were then filtered with a priori candidate loci generated from genome database and gene network models to obtain a posteriori candidate loci. A probabilistic gene network (AraNet) that interrogates network neighborhoods of genes was then used to expand the filtering power to examine the significant testing signals. Using this strategy, we confirmed the known true positives and identified several new promising associations. Promising genes (AP1, FCA, FRI, FLC, FLM, SPL5, FY, and DCL2) were shown to control for flowering time through either common variants or rare variants within a diverse set of Arabidopsis accessions. Although many of these candidate genes were cloned earlier with mutational studies, identifying their allele variation contribution to overall phenotypic variation among diverse natural accessions is critical. Our rare allele testing established a greater number of connections than previous analyses in which this issue was not addressed. More importantly, our results demonstrated the potential of integrating various biological, statistical, and bioinformatic tools into complex trait dissection.
机译：基于外源基因组测序和基于全基因组重测序的关联研究对基于高密度阵列的全基因组关联研究（GWAS）进行了补充。在这里，我们提出了一种基于复合的基于重测序的全基因组关联研究（CR-GWAS）策略，该策略可系统地利用集体生物学信息和分析工具进行可靠的分析。我们通过使用拟南芥（Arabidopsis thaliana）重测序数据展示了该策略的实用性。每个位点的生物功能改变的生物信息学预测已整合到具有复杂性状的常见和稀有变体的关联测试过程中，并具有一套统计数据。然后用从基因组数据库和基因网络模型生成的先验候选基因座过滤重要信号，以获得后验候选基因座。然后，使用一个查询基因网络邻域的概率基因网络（AraNet）来扩展过滤能力，以检查重要的测试信号。使用此策略，我们确认了已知的真实肯定并确定了几个新的有希望的关联。有前途的基因（AP1，FCA，FRI，FLC，FLM，SPL5，FY和DCL2）显示可通过多种拟南芥属种中的常见变体或稀有变体控制开花时间。尽管这些候选基因中有许多是较早通过突变研究克隆的，但鉴定其等位基因变异对多种自然种之间总体表型变异的贡献至关重要。与之前未解决此问题的分析相比，我们罕见的等位基因测试建立了更多的连接。更重要的是，我们的结果证明了将各种生物学，统计学和生物信息学工具整合到复杂性状解剖中的潜力。

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