Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines
Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines

Ying Wang1, Bo He1, Yuanyuan Zhao2Jill L. Reiter3Steven X. Chen3Edward Simpson4, Weixing Feng1* and Yunlong Liu1, 3*

·        1Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, China

·        2Heilongjiang Provincial Hospital, China

·        3Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, United States

·        4BioHealth informatics, school of informatics and computing, Indiana University, United States

Genetic variants can influence the expression of mRNA and protein. Genetic regulatory loci such as expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) exist in several species. However, it remains unclear how human genetic variants regulate mRNA and protein expression. Here, we characterized six mechanistic models for the genetic regulatory patterns of single nucleotide polymorphisms (SNPs) and their actions on post-transcriptional expression. Data from Yoruba HapMap lymphoblastoid cell lines were analyzed to identify human cis-eQTLs and pQTLs, as well as protein-specific QTLs (psQTLs). Our results indicated that genetic regulatory loci primarily affected mRNA and protein abundance in patterns where the two were well-correlated. While this finding was observed in both humans and mice (57.5% and 70.3%, respectively), the genetic regulatory patterns differed between species implying evolutionary differences. Mouse SNPs generally targeted changes in transcript expression (51%), whereas in humans they largely regulated protein abundance, independent of transcription levels (55.9%). The latter independent function can be explained by psQTLs. Our analysis suggests that local functional genetic variants in the human genome mainly modulate protein abundance independent of mRNA levels through post-transcriptional mechanisms. These findings clarify the impact of genetic variation on phenotype, which is of particular relevance to disease risk and treatment response.

Keywords: Functional genetic variants, quantitative trait loci (QTLs), Genetic regulatory pattern, maximum likelihood estimation, Independent regulation

Received: 30 May 2019; Accepted: 31 Jul 2019. 

Edited by:

Meng Zhou, Wenzhou Medical University, China

Reviewed by:

Yungang Xu, University of Texas Health Science Center at Houston, United States 
Ruiping Wang, University of Texas MD Anderson Cancer Center, United States  

Copyright: © 2019 Wang, He, Zhao, Reiter, Chen, Simpson, Feng and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: 
Prof. Weixing Feng, Harbin Engineering University, Institute of Intelligent System and Bioinformatics, College of Automation, Harbin, China, fengweixing@hrbeu.edu.cn 
Prof. Yunlong Liu, Indiana University, Center for Computational Biology and Bioinformatics, School of Medicine, Indianapolis, Indiana, United States, yunliu@iu.edu