Climente-González Héctor, Lonjou Christine, Lesueur Fabienne, Stoppa-Lyonnet Dominique, Andrieu Nadine, Azencott Chloé-Agathe
Institut Curie, PSL Research University, Paris, France.
INSERM, U900, Paris, France.
PLoS Comput Biol. 2021 Mar 18;17(3):e1008819. doi: 10.1371/journal.pcbi.1008819. eCollection 2021 Mar.
Genome-wide association studies (GWAS) explore the genetic causes of complex diseases. However, classical approaches ignore the biological context of the genetic variants and genes under study. To address this shortcoming, one can use biological networks, which model functional relationships, to search for functionally related susceptibility loci. Many such network methods exist, each arising from different mathematical frameworks, pre-processing steps, and assumptions about the network properties of the susceptibility mechanism. Unsurprisingly, this results in disparate solutions. To explore how to exploit these heterogeneous approaches, we selected six network methods and applied them to GENESIS, a nationwide French study on familial breast cancer. First, we verified that network methods recovered more interpretable results than a standard GWAS. We addressed the heterogeneity of their solutions by studying their overlap, computing what we called the consensus. The key gene in this consensus solution was COPS5, a gene related to multiple cancer hallmarks. Another issue we observed was that network methods were unstable, selecting very different genes on different subsamples of GENESIS. Therefore, we proposed a stable consensus solution formed by the 68 genes most consistently selected across multiple subsamples. This solution was also enriched in genes known to be associated with breast cancer susceptibility (BLM, CASP8, CASP10, DNAJC1, FGFR2, MRPS30, and SLC4A7, P-value = 3 × 10-4). The most connected gene was CUL3, a regulator of several genes linked to cancer progression. Lastly, we evaluated the biases of each method and the impact of their parameters on the outcome. In general, network methods preferred highly connected genes, even after random rewirings that stripped the connections of any biological meaning. In conclusion, we present the advantages of network-guided GWAS, characterize their shortcomings, and provide strategies to address them. To compute the consensus networks, implementations of all six methods are available at https://github.com/hclimente/gwas-tools.
全基因组关联研究（GWAS）探索复杂疾病的遗传病因。然而，传统方法忽略了所研究的遗传变异和基因的生物学背景。为了解决这一缺点，可以使用对功能关系进行建模的生物网络来搜索功能相关的易感位点。存在许多这样的网络方法，每种方法都源于不同的数学框架、预处理步骤以及关于易感机制网络特性的假设。不出所料，这导致了不同的解决方案。为了探索如何利用这些不同的方法，我们选择了六种网络方法并将它们应用于GENESIS，这是一项关于家族性乳腺癌的法国全国性研究。首先，我们验证了网络方法比标准GWAS能得出更具可解释性的结果。我们通过研究它们之间重叠部分来解决其解决方案的异质性，计算我们所称的共识。这个共识解决方案中的关键基因是COPS5，一个与多种癌症特征相关的基因。我们观察到的另一个问题是网络方法不稳定，在GENESIS的不同子样本上选择非常不同的基因。因此，我们提出了一个由在多个子样本中最一致被选中的68个基因组成的稳定共识解决方案。这个解决方案中也富含已知与乳腺癌易感性相关的基因（BLM、CASP8、CASP10、DNAJC1、FGFR2、MRPS30和SLC4A7，P值 = 3×10 - 4）。连接性最强的基因是CUL3，它是与癌症进展相关的几个基因的调节因子。最后，我们评估了每种方法的偏差及其参数对结果的影响。一般来说，网络方法更倾向于连接性高的基因，即使在进行了随机重连从而消除了任何生物学意义上的连接之后也是如此。总之，我们展示了网络引导的GWAS的优势，描述了它们的缺点，并提供了解决这些问题的策略。为了计算共识网络，所有六种方法的实现可在https://github.com/hclimente/gwas-tools上获取。
