DataSheet1_Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study.ZIP

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（post-genome era）的一项重大挑战，是注释并阐明遗传变异对多种表型的影响。全基因组关联研究（genome-wide association study, GWAS）是一种广为人知的方法，可从海量遗传变异中识别复杂性状的潜在遗传位点；在此之后，识别表达数量性状位点（expression quantitative trait loci, eQTL）便成为关键任务。然而，传统eQTL方法通常忽略了单核苷酸多态性（single-nucleotide polymorphisms, SNPs）或基因的系统性调控作用，因此遗漏了诸多与网络相关的表型决定因子。这一研究痛点促使我们开展基于网络的数量性状位点（quantitative trait loci, QTL）研究，即网络QTL（network QTL, nQTL）：相较于传统eQTL中“基因型→表达→表型”的级联关联模式，nQTL旨在检测“基因型→网络→表型”的级联关联。具体而言，我们基于单样本网络（single-sample networks）的理论与方法构建了nQTL分析框架，该框架不仅可识别用于解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），还可提取用于表征目标表型及其对应亚型的网络特征（例如从网络性状中筛选得到的互作基因生物标志物候选集）。我们的研究结果显示，相较于传统eQTL方法，nQTL框架可在多种模拟数据场景中高效捕获单核苷酸多态性与网络性状（即边性状）之间的关联。此外，我们在多组生物学与生物医学数据集上开展了nQTL分析。该分析可有效检测各类生物学问题中的网络性状，并能发现诸多用于区分表型的网络特征，这有助于阐释nQTL对疾病分型、疾病预后、药物响应以及病原体因子关联的调控影响。尤为关键的是，与传统方法相比，nQTL框架还可从人类批量表达数据中识别出诸多网络性状，且可通过独立或无监督模式下的匹配单细胞RNA测序（single-cell RNA-seq）数据进行验证。上述所有研究结果均有力证实，nQTL及其检测框架可同时探究全局基因型-网络-表型关联，以及具备功能影响与重要价值的潜在网络性状或网络特征。