Quantitative Trait Loci Mapping of the Mouse Plasma Proteome (pQTL)

Abstract

A current challenge in the era of genome-wide studies is to determine the responsible genes and mechanisms underlying newly identified loci. Screening of the plasma proteome by high-throughput mass spectrometry (MALDI-TOF MS) is considered a promising approach for identification of metabolic and disease processes. Therefore, plasma proteome screening might be particularly useful for identifying responsible genes when combined with analysis of variation in the genome. Here, we describe a proteomic quantitative trait locus (pQTL) study of plasma proteome screens in an F 2 intercross of 455 mice mapped with 177 genetic markers across the genome. A total of 69 of 176 peptides revealed significant LOD scores ($5.35) demonstrating strong genetic regulation of distinct components of the plasma proteome. Analyses were confirmed by mechanistic studies and MALDI-TOF/TOF, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses of the two strongest pQTLs: A pQTL for mass-to-charge ratio (m/z) 3494 (LOD 24.9, D11Mit151) was identified as the N-terminal 35 amino acids of hemoglobin subunit A (Hba) and caused by genetic variation in Hba. Another pQTL for m/z 8713 (LOD 36.4; D1Mit111) was caused by variation in apolipoprotein A2 (Apoa2) and cosegregated with HDL cholesterol. Taken together, we show that genome-wide plasma proteome profiling in combination with genome-wide genetic screening aids in the identification of causal genetic variants affecting abundance of plasma proteins.T HE central dogma of molecular biology states that sequence information is carried from DNA to RNA to protein (Crick 1970) and it is commonly accepted that differences in protein function or abundance are in most cases responsible for phenotypic differences and susceptibility to disease. To date, mapping complex traits with global gene expression-designated expression quantitative trait loci (eQTL) mapping-has been remarkably successful in identifying the underlying molecular mechanisms (reviewed by Cookson et al. 2009). That approach has been greatly facilitated by advances in expression array technology. However, mRNA levels explain only 40% of the variability in protein levels (Schwanhausser et al. 2011). Therefore, using the proteome as a surrogate, which might be more closely related to the phenotype level, may provide novel insights into genetic regulation of certain phenotypes (Supporting Information, Figure S1).The global analysis of proteins has been exceedingly difficult in the past (Gstaiger and Aebersold 2009) and only few studies have previously attempted a combined analysis of proteome-wide and genome-wide data: An approach in yeast BY4716xRM11-1a segregants (n = 98) successfully identified genetically determined proteins using label-free liquid chromatography-tandem mass spectrometry (LC-MS/ MS) (Foss et al. 2007). In mice, Klose et al. (2002) have investigated brain proteome expression in 64 N2 progeny of a C57BL/6 · SPR backcross using two-dimensional gel electrophoresis. A recent study performed comparative analy...

Heritability and genetic basis of protein level variation in an outbred population

“…Mass spectrometry of protein fragments has evolved from quantification of a limited number of peptides (Foss et al 2007;Lu et al 2007;de Godoy et al 2008;Ramakrishnan et al 2009;Ghazalpour et al 2011) to thousands of proteins (Marguerat et al 2012;Picotti et al 2013;Skelly et al 2013;Wu et al 2013). These advances have enabled mapping of alleles that contribute to the variation in protein abundance in yeast (Foss et al 2007(Foss et al , 2011Skelly et al 2013), mouse (Ghazalpour et al 2011;Holdt et al 2013), and human Wu et al 2013). Due to limited statistical power and inherently low-throughput measurement, mass spectrometry-based studies have mainly focused on the effects of cis variants and established that about half of cis alleles associated with a protein level also affect transcript abundance (Ghazalpour et al 2011;Skelly et al 2013;Wu et al 2013).…”

Decoding neuroproteomics: integrating the genome, translatome and functional anatomy

“…Quantitative trait loci (QTL) have been extensively mapped to a variety of complex traits using transcriptomics in terms of the relationship between genomic variants and RNA expression (eQTL) and, more recently, variants have also been related to protein abundance (pQTL) 87 . In the prefrontal cortex, for example, such variants reportedly affect the expression of more than 100 genes 88 .…”

Multilayered Genetic and Omics Dissection of Mitochondrial Activity in a Mouse Reference Population

“…This reduces the technical reproducibility but moreover means that the number of peptides consistently quantified across all (or most) samples decreases as the study size increases (Karpievitch et al, 2012). Consequently, discovery mass spectrometry strategy has yielded mixed results in large population studies (Ghazalpour et al, 2011; Holdt et al, 2013), particularly as specific peptides of interest cannot be targeted, and the most consistently identified peptides are biased toward those of higher abundance (Callister et al, 2006). To overcome these hurdles, selected reaction monitoring (SRM) was developed, which perfects technical reproducibility and allows consistent multiplexed quantitation of target proteins by deploying a mass spectrometric measurement assay that is specific for each targeted peptide (Lange et al, 2008a).…”