AUTHOR=Yuan Kai , Zeng Tao , Chen Luonan 

TITLE=Interpreting Functional Impact of Genetic Variations by Network QTL for Genotype–Phenotype Association Study

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 9 - 2021

YEAR=2022

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2021.720321

DOI=10.3389/fcell.2021.720321

ISSN=2296-634X

ABSTRACT=An enormous challenge in the post-genome era is to annotate and resolve the consequences of genetic variation on diverse phenotypes. Genome-wide association study (GWAS) has been 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 SNPs or genes, thereby overlooking many network-associated phenotypic determinates. Such a problem motivates our network model of QTL, i.e. network QTL (nQTL), which is to detect the cascade association as genotype-network-phenotype rather than conventional genotype-expression-phenotype. Specifically, we develop the nQTL framework on the theory and approach of single-sample network, which can identify not only network traits for analyzing complex biological processes but also network signatures for characterizing targeted phenotype and corresponding subtypes. Our results show that nQTL can efficiently capture associations between SNPs and network traits (i.e. edge traits) in various data scenarios, comparing with traditional eQTL. Furthermore, we have carried on nQTL analysis on diverse biological and biomedical datasets, which strongly support nQTL is effective to detect network traits for various biological problems, and can discover many network signatures for discriminating phenotypes, which can help interpret the influence of nQTL (i.e. network signatures) on disease subtyping, disease prognosis, drug response, and pathogen factor association. Especially, in contrast to the conventional approaches, nQTL framework identified many nQTL traits on both human bulk and single-cell expression data, validated by matched scRNA-seq data in an independent or unsupervised manner. All these results strongly support that nQTL can simultaneously detect the global genotype-network-phenotype associations and the underlying network traits or network signatures with functional importance, by integrating multi-level omics data.