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

Frontiers in Genetics

Edward Simpson¹  Jill L. Reiter²  Steven X. Chen²  Yunlong Liu²  Yuanyuan Zhao³  Bo He⁴  Ying Wang⁴  Weixing Feng⁴ 

[1] BioHealth Informatics, School of Informatics and Computing, Indiana University, Indianapolis, IN, United States;Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, IN, United States;Heilongjiang Provincial Hospital, Harbin, Heilongjiang, China;Institute of Intelligent System and Bioinformatics, College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China;

关键词: functional genetic variants;    quantitative trait loci (QTLs);    genetic regulatory pattern;    maximum likelihood estimation;    independent regulation;   

DOI  :  10.3389/fgene.2019.00806

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown