Age, Sex, and Genetic Architecture of Human Gene Expression in EBV Transformed Cell Lines

TL;DR

This study investigates how age, sex, and genetic variation influence gene expression in EBV-transformed cell lines, revealing consistent biological patterns and genetic associations across tissues and individuals.

Contribution

It demonstrates that EBV cell lines retain individual-specific genetic and non-germline information, and identifies age, sex, and genetic factors affecting gene expression.

Findings

Age affects 949 genes, including immune-related genes like MHC Class I.

Sex influences genes related to X-inactivation, testis, and hormone response.

494 transcripts are associated with cis genetic variants, aiding understanding of genetic regulation.

Abstract

Individual expression profiles from EBV transformed cell lines are an emerging resource for genomic investigation. In this study we characterize the effects of age, sex, and genetic variation on gene expression by surveying public datasets of such profiles. We establish that the expression space of cell lines maintains genetic as well as non-germline information, in an individual-specific and cross-tissue manner. Age of donor is associated with the expression of 949 genes in the derived cell line. Age-associated genes include over-representation of immune-related genes, specifically MHC Class I genes, a phenomenon that replicates across tissues and organisms. Sex associated genes in these cell lines include likely candidates, such as genes that escape X-inactivation,testis specific expressed genes, androgen and estrogen specific genes, but also gene families previously unknown to be sex associated such as common microRNA targets (MIR-490, V_ARP1_01, MIR-489). Finally, we report 494 transcripts whose expression levels are associated with a genetic variant in cis, overlapping and validating previous reports. Incorporating age in analysis of association facilitates additional discovery of trans-acting regulatory genetic variants. Our findings promote expression profiling of transformed cell lines as a vehicle for understanding cellular systems beyond the specific lines.