Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla (T.S.).
Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla (J.D.L., D.J.G.).
Circ Genom Precis Med. 2018 Dec;11(12):e002170. doi: 10.1161/CIRCGEN.118.002170.
Identifying genetic variation associated with plasma protein levels, and the mechanisms by which they act, could provide insight into alterable processes involved in regulation of protein levels. Although protein levels can be affected by genetic variants, their estimation can also be biased by missense variants in coding exons causing technical artifacts. Integrating genome sequence genotype data with mass spectrometry-based protein level estimation could reduce bias, thereby improving detection of variation that affects RNA or protein metabolism.
Here, we integrate the blood plasma protein levels of 664 proteins from 165 participants of the Tromsø Study, measured via tandem mass tag mass spectrometry, with whole-exome sequencing data to identify common and rare genetic variation associated with peptide and protein levels (protein quantitative trait loci [pQTLs]). We additionally use literature and database searches to prioritize putative functional variants for each pQTL.
We identify 109 independent associations (36 protein and 73 peptide) and use genotype data to exclude 49 (4 protein and 45 peptide) as technical artifacts. We describe 2 particular cases of rare variation: 1 associated with the complement pathway and 1 with platelet degranulation. We identify putative functional variants and show that pQTLs act through diverse molecular mechanisms that affect both RNA and protein metabolism.
We show that although the majority of pQTLs exert their effects by modulating RNA metabolism, many affect protein levels directly. Our work demonstrates the extent by which pQTL studies are affected by technical artifacts and highlights how prioritizing the functional variant in pQTL studies can lead to insights into the molecular steps by which a protein may be regulated.
鉴定与血浆蛋白水平相关的遗传变异及其作用机制,可以深入了解调节蛋白水平的可改变过程。尽管蛋白水平可能受遗传变异影响,但编码外显子中的错义变异也会导致技术伪影,从而影响其估计。将全基因组序列基因型数据与基于质谱的蛋白水平估计相结合,可减少偏差,从而提高检测影响 RNA 或蛋白代谢的变异的能力。
我们将来自特罗姆瑟研究的 165 名参与者的 664 种血浆蛋白的水平(通过串联质量标签质谱法测量)与全外显子组测序数据整合,以鉴定与肽和蛋白水平相关的常见和罕见遗传变异(蛋白数量性状基因座[pQTL])。我们还通过文献和数据库搜索,为每个 pQTL 优先考虑潜在的功能变异。
我们确定了 109 个独立的关联(36 个蛋白和 73 个肽),并使用基因型数据排除了 49 个(4 个蛋白和 45 个肽)作为技术伪影。我们描述了 2 个罕见变异的特殊情况:1 个与补体途径有关,1 个与血小板脱颗粒有关。我们确定了潜在的功能变异,并表明 pQTL 通过影响 RNA 和蛋白质代谢的多种分子机制发挥作用。
尽管大多数 pQTL 通过调节 RNA 代谢发挥作用,但许多 pQTL 直接影响蛋白水平。我们的工作表明,pQTL 研究受技术伪影影响的程度,并强调了在 pQTL 研究中优先考虑功能变异如何深入了解蛋白质可能受到调节的分子步骤。