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In these studies, expression levels are viewed as quantitative traits, and gene
expression phenotypes are mapped to particular genomic loci by combining studies of
variation in gene expression patterns with genome-wide genotyping.
In fact, it is difficult to identify regulatory regions in the genome, let alone to predict
how polymorphisms in regulatory regions affect gene expression levels temporally or
spatially
Genome-scale eQTL mapping studies in nonhuman organisms have predominantly
focused on three objectives: (i) to identify QTLs associated with variation in transcript
abundances in defined mapping populations and categorize them as proximal or distal
to the locus of the transcript they affect, (ii) to determine the numbers, genomic
distributions and magnitudes of eQTL effects on transcript levels and (iii) to evaluate
whether eQTLs interact additively to control transcript levels.
One can view eQTL mapping as a sort of large-scale mutagenesis experiment, in which
•10 million common single nucleotide polymorphisms (SNPs) have been sprinkled
down on the human genome, and each individual receives a random collection of
these
trans-eQTL
quantita’ di mRNA
cis-eQTL
ChIP-seq
ChIP-seq
Gene expression differs among individuals and populations and is thought to be a major determinant of
phenotypic variation. Although variation and genetic loci responsible for RNA expression levels have been
analysed extensively in human populations1, 2, 3, 4, 5, our knowledge is limited regarding the differences in
human protein abundance and the genetic basis for this difference. Variation in messenger RNA expression is
not a perfect surrogate for protein expression because the latter is influenced by an array of posttranscriptional regulatory mechanisms, and, empirically, the correlation between protein and mRNA levels is
generally modest6, 7. Here we used isobaric tag-based quantitative mass spectrometry to determine relative
protein levels of 5,953 genes in lymphoblastoid cell lines from 95 diverse individuals genotyped in the HapMap
Project8, 9. We found that protein levels are heritable molecular phenotypes that exhibit considerable variation
between individuals, populations and sexes. Levels of specific sets of proteins involved in the same biological
process covary among individuals, indicating that these processes are tightly regulated at the protein level. We
identified cis-pQTLs (protein quantitative trait loci), including variants not detected by previous transcriptome
studies. This study demonstrates the feasibility of high-throughput human proteome quantification that, when
integrated with DNA variation and transcriptome information, adds a new dimension to the characterization of
gene expression regulation.