Yuan Kai, Zeng Tao, Chen Luonan
Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China.
Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Front Cell Dev Biol. 2022 Jan 26;9:720321. doi: 10.3389/fcell.2021.720321. eCollection 2021.
An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. The genome-wide association study (GWAS) is a well-known method to identify potential genetic loci for complex traits from huge genetic variations, following which it is crucial to identify expression quantitative trait loci (eQTL). However, the conventional eQTL methods usually disregard the systematical role of single-nucleotide polymorphisms (SNPs) or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates us to recognize the network-based quantitative trait loci (QTL), i.e., network QTL (nQTL), which is to detect the cascade association as genotype → network → phenotype rather than conventional genotype → expression → phenotype in eQTL. Specifically, we develop the nQTL framework on the theory and approach of single-sample networks, which can identify not only network traits (e.g., the gene subnetwork associated with genotype) for analyzing complex biological processes but also network signatures (e.g., the interactive gene biomarker candidates screened from network traits) for characterizing targeted phenotype and corresponding subtypes. Our results show that the nQTL framework can efficiently capture associations between SNPs and network traits (i.e., edge traits) in various simulated data scenarios, compared with traditional eQTL methods. Furthermore, we have carried out nQTL analysis on diverse biological and biomedical datasets. Our analysis is effective in detecting network traits for various biological problems and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL on disease subtyping, disease prognosis, drug response, and pathogen factor association. Particularly, in contrast to the conventional approaches, the nQTL framework could also identify many network traits from human bulk expression data, validated by matched single-cell RNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL and its detection framework can simultaneously explore the global genotype-network-phenotype associations and the underlying network traits or network signatures with functional impact and importance.
后基因组时代面临的一项巨大挑战是注释和解析遗传变异对多种表型的影响。全基因组关联研究（GWAS）是一种从海量遗传变异中识别复杂性状潜在基因座的著名方法，在此之后，识别表达数量性状基因座（eQTL）至关重要。然而，传统的eQTL方法通常忽略了单核苷酸多态性（SNP）或基因的系统作用，从而忽略了许多与网络相关的表型决定因素。这一问题促使我们认识到基于网络的数量性状基因座（QTL），即网络QTL（nQTL），它检测的是基因型→网络→表型的级联关联，而不是eQTL中传统的基因型→表达→表型。具体而言，我们基于单样本网络的理论和方法开发了nQTL框架，该框架不仅可以识别用于分析复杂生物学过程的网络性状（例如与基因型相关的基因子网），还可以识别用于表征目标表型及其相应亚型的网络特征（例如从网络性状中筛选出的相互作用基因生物标志物候选物）。我们的结果表明，与传统的eQTL方法相比，nQTL框架能够在各种模拟数据场景中有效地捕捉SNP与网络性状（即边性状）之间的关联。此外，我们对各种生物和生物医学数据集进行了nQTL分析。我们的分析对于检测各种生物学问题的网络性状有效，并且可以发现许多用于区分表型的网络特征，这有助于解释nQTL对疾病亚型分类、疾病预后、药物反应和病原体因素关联的影响。特别地，与传统方法相比，nQTL框架还可以从人类大量表达数据中识别许多网络性状，并通过独立或无监督方式的匹配单细胞RNA测序数据进行验证。所有这些结果都有力地支持了nQTL及其检测框架能够同时探索全局基因型-网络-表型关联以及具有功能影响和重要性的潜在网络性状或网络特征。
