10.3389/fgene.2019.00806.s002 Ying Wang Ying Wang Bo He Bo He Yuanyuan Zhao Yuanyuan Zhao Jill L. Reiter Jill L. Reiter Steven X. Chen Steven X. Chen Edward Simpson Edward Simpson Weixing Feng Weixing Feng Yunlong Liu Yunlong Liu Image_2_Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines.jpeg Frontiers 2019 functional genetic variants quantitative trait loci (QTLs) genetic regulatory pattern maximum likelihood estimation independent regulation 2019-09-10 04:54:14 Figure https://frontiersin.figshare.com/articles/figure/Image_2_Comprehensive_Cis-Regulation_Analysis_of_Genetic_Variants_in_Human_Lymphoblastoid_Cell_Lines_jpeg/9790004 <p>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.</p>