Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies.
Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies.
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DOI：
10.1371/journal.pgen.1007142
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发表时间：
2017-12
期刊：
影响因子：
4.5
通讯作者：
Zhang ZD
中科院分区：
文献类型：
--
作者：
Lin JR;Zhang Q;Cai Y;Morrow BE;Zhang ZD
罕见的主效应变异在人类复杂疾病中起着重要作用，可以通过基于测序的全基因组关联研究发现。在这里，我们介绍了一个综合的方法，结合罕见变异关联测试与基因网络和表型信息，以确定与人类复杂疾病的罕见变异牵连的风险基因。我们的数据整合方法遵循“发现驱动”的策略，而不依赖于有关疾病的先验知识，从而保持了全基因组关联研究的无偏特征。仿真结果表明，我们的方法可以超过广泛使用的罕见变异关联测试方法2到3倍。在一个小型疾病队列的病例研究中，我们发现了可能作为22q11.2缺失综合征患者先天性心脏病遗传修饰因子的假定风险基因和相应的罕见变异。这些变异被仅依赖于罕见变异关联检验的常规方法遗漏。病例对照测序研究是一种很有前途的设计，以揭示人类复杂疾病的风险基因涉及罕见的变异。最近开发的不同类型的罕见变异相关性测试提高了统计能力，以确定疾病基因的风险罕见变异。然而，最近基于测序的全基因组关联研究都没有确定罕见变异或基于它们的基因的强大疾病关联。由于在真实的应用中可以可行地实现的样本量有限，当前的稀有变异关联测试对于大多数风险基因只能产生边缘关联信号。在这里，我们提出了一个集成的方法，结合关联信号与正交的生物学证据来发现测序研究中的风险基因。为了解决缺乏权力的问题，我们的方法被证明可以有效地发现风险基因的边缘关联信号在数据模拟。事实上，在我们的病例研究中展示的一个真实的应用中，我们的方法揭示了22 q11.2缺失综合征中先天性心脏病的重要风险基因，这些基因在以前的研究中被遗漏。
Rare variants of major effect play an important role in human complex diseases and can be discovered by sequencing-based genome-wide association studies. Here, we introduce an integrated approach that combines the rare variant association test with gene network and phenotype information to identify risk genes implicated by rare variants for human complex diseases. Our data integration method follows a 'discovery-driven' strategy without relying on prior knowledge about the disease and thus maintains the unbiased character of genome-wide association studies. Simulations reveal that our method can outperform a widely-used rare variant association test method by 2 to 3 times. In a case study of a small disease cohort, we uncovered putative risk genes and the corresponding rare variants that may act as genetic modifiers of congenital heart disease in 22q11.2 deletion syndrome patients. These variants were missed by a conventional approach that relied on the rare variant association test alone. Case-control sequencing studies are a promising design to uncover risk genes of human complex diseases implicated by rare variants. The recent development of different types of rare variant association tests has improved the statistical power to identify disease genes that harbor risk rare variants. However, none of the recent sequencing-based genome-wide association studies identified robust disease association of rare variants or genes based on them. Due to limited sample sizes that can be feasibly achieved in real applications, current rare variant association tests can only generate marginal association signals for most risk genes. Here we proposed an integrated method that combined association signals with orthogonal biological evidence to uncover risk genes in sequencing studies. Designed to address the lack-of-power issue, our method was shown to effectively uncover risk genes with marginal association signals in data simulation. Indeed, in a real application demonstrated in our case study our method disclosed important risk genes of congenital heart disease in 22q11.2 deletion syndrome that were missed by the previous study.
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