Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island.
Center for Computational Molecular Biology, Brown University, Providence, Rhode Island.
Cancer Epidemiol Biomarkers Prev. 2017 Oct;26(10):1531-1539. doi: 10.1158/1055-9965.EPI-17-0360. Epub 2017 Jul 27.
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, suggesting that germline variants influence ALL risk. Although multiple genome-wide association (GWA) studies have identified variants predisposing children to ALL, it remains unclear whether genetic heterogeneity affects ALL susceptibility and how interactions within and among genes containing ALL-associated variants influence ALL risk. Here, we jointly analyzed two published datasets of case-control GWA summary statistics along with germline data from ALL case-parent trios. We used the gene-level association method PEGASUS to identify genes with multiple variants associated with ALL. We then used PEGASUS gene scores as input to the network analysis algorithm HotNet2 to characterize the genomic architecture of ALL. Using PEGASUS, we confirmed associations previously observed at genes such as , and , and we identified novel candidate gene associations. Using HotNet2, we uncovered significant gene subnetworks that may underlie inherited ALL risk: a subnetwork involved in B-cell differentiation containing the ALL-associated gene , and a subnetwork of homeobox genes, including Gene and network analysis uncovered loci associated with ALL that are missed by GWA studies, such as Furthermore, ALL-associated loci do not appear to interact directly with each other to influence ALL risk, and instead appear to influence leukemogenesis through multiple, complex pathways. We present a new pipeline for analysis of association studies that yields new insight into the etiology of ALL and can be applied in future studies to shed light on the genomic underpinnings of cancer. .
急性淋巴细胞白血病(ALL)是最常见的儿童癌症,这表明种系变异会影响 ALL 的风险。尽管多项全基因组关联(GWA)研究已经确定了使儿童易患 ALL 的变异,但仍不清楚遗传异质性是否会影响 ALL 的易感性,以及包含 ALL 相关变异的基因内和基因间的相互作用如何影响 ALL 的风险。在这里,我们联合分析了两项已发表的病例对照 GWA 汇总统计数据以及 ALL 病例-父母三体型的种系数据。我们使用基因水平关联方法 PEGASUS 来识别与 ALL 相关的多个变异的基因。然后,我们将 PEGASUS 基因评分作为输入,用于网络分析算法 HotNet2 来描述 ALL 的基因组结构。使用 PEGASUS,我们证实了先前在基因如、和中观察到的关联,并且确定了新的候选基因关联。使用 HotNet2,我们发现了可能是遗传 ALL 风险基础的显著基因子网络:一个涉及 B 细胞分化的子网络,包含 ALL 相关基因,以及一个包含同源盒基因的子网络,包括基因和网络分析揭示了全基因组关联研究遗漏的与 ALL 相关的基因座,例如此外,ALL 相关基因座似乎彼此之间没有直接相互作用以影响 ALL 的风险,而是通过多个复杂途径影响白血病的发生。我们提出了一种用于基因座分析的新方法,为 ALL 的病因学提供了新的见解,并可应用于未来的研究,以揭示癌症的基因组基础。