Identification and Validation of Genetic Variants that Influence Transcription Factor and Cell Signaling Protein Levels

Ronald J Hause,Amy L Stark,Nirav N Antao,Lidija K Gorsic,Sophie H Chung,Christopher D Brown,Shan S Wong,Daniel F Gill,Jamie L Myers,Lida Anita To,Kevin P White,M Eileen Dolan,Richard Baker Jones

doi:10.1016/j.ajhg.2014.07.005

Ronald J Hause, Amy L Stark + Show 11 more

Open Access

https://doi.org/10.1016/j.ajhg.2014.07.005

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Abstract

Many genetic variants associated with human disease have been found to be associated with alterations in mRNA expression. Although it is commonly assumed that mRNA expression changes will lead to consequent changes in protein levels, methodological challenges have limited our ability to test the degree to which this assumption holds true. Here, we further developed the micro-western array approach and globally examined relationships between human genetic variation and cellular protein levels. We collected more than 250,000 protein level measurements comprising 441 transcription factor and signaling protein isoforms across 68 Yoruba (YRI) HapMap lymphoblastoid cell lines (LCLs) and identified 12 cis and 160 trans protein level QTLs (pQTLs) at a false discovery rate (FDR) of 20%. Whereas up to two thirds of cis mRNA expression QTLs (eQTLs) were also pQTLs, many pQTLs were not associated with mRNA expression. Notably, we replicated and functionally validated a trans pQTL relationship between the KARS lysyl-tRNA synthetase locus and levels of the DIDO1 protein. This study demonstrates proof of concept in applying an antibody-based microarray approach to iteratively measure the levels of human proteins and relate these levels to human genome variation and other genomic data sets. Our results suggest that protein-based mechanisms might functionally buffer genetic alterations that influence mRNA expression levels and that pQTLs might contribute phenotypic diversity to a human population independently of influences on mRNA expression.

Highlights

We developed a standardized protocol using micro-western arrays (MWAs)[21] and reverse phase protein arrays (RPPAs) to quantify 441 proteins across 68 unrelated Yoruba (YRI) lymphoblastoid cell lines (LCLs) with a panel of antibodies directed at most human transcription factors (TFs) and many disease-related cell-signaling proteins
For overlap with expression QTLs (eQTLs) and protein level QTLs (pQTLs), we considered all SNPs in linkage disequilibrium (LD) (R2 > 80%) with the complex-trait-associated SNPs and filtered for common variants (MAF > 5%) in the YRI samples examined
To determine the enrichment for SNPs associated with each complex trait to be eQTLs or pQTLs, we focused on only the 7,222 primary-trait-associated SNPs before LD imputation to correct for LD-driven inflation of enrichment results

Summary

Introduction

Our ability to sequence genomes at an ever-increasing rate has resulted in the identification of many new common and rare genetic variants across human populations.[1,2,3] Much effort has been devoted to identifying relationships between genetic variation and complex human phenotypes, including susceptibility to disease and adverse drug response.[4,5,6] Developing a mechanistic biological understanding of such statistical associations represents a major ongoing challenge in human genomics.Expression quantitative trait locus (eQTL) mapping has been used to identify gene targets and mechanisms that link genome variation with complex phenotypic traits.[7,8,9] A fundamental assumption made in such studies is that genome variants associated with mRNA expression variation will be associated with protein-level variation that impacts a trait. The influence of genetic variation on mRNA levels may extend to protein levels, many posttranscriptional mechanisms, such as mRNA translation efficiency, protein stability and function, and posttranslational modification, can buffer changes in mRNA expression. These same mechanisms can introduce changes in protein levels under conditions of invariant mRNA expression. Such protein-centric mechanisms can be deciphered only by measuring genetic-, mRNA-, and protein-level variation among a population of individuals. Previous examinations of genetic influences on protein-level variation have observed markedly nonoverlapping loci regulating protein and transcript levels.[10,11,12]

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