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Am J Hum Genet. 2014 Aug 7;95(2):194-208. doi: 10.1016/j.ajhg.2014.07.005. Epub 2014 Jul 31.

Identification and validation of genetic variants that influence transcription factor and cell signaling protein levels.

Author information

1
Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA.
2
Department of Medicine, University of Chicago, Chicago, IL 60637, USA.
3
Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA.
4
Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
5
Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA.
6
Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Committee on Clinical Pharmacology and Pharmacogenomics, University of Chicago, Chicago, IL 60637, USA. Electronic address: edolan@medicine.bsd.uchicago.edu.
7
Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA; Ben May Department for Cancer Research, University of Chicago, Chicago, IL 60637, USA; Committee on Clinical Pharmacology and Pharmacogenomics, University of Chicago, Chicago, IL 60637, USA. Electronic address: richardbjones@gmail.com.

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.

PMID:
25087611
PMCID:
PMC4129400
DOI:
10.1016/j.ajhg.2014.07.005
[Indexed for MEDLINE]
Free PMC Article

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