Genotype-Tissue-Protein: proteomic variation and quantitative trait loci (pQTL)

描述（由申请人提供）：拟议研究的目标是系统地表征多种人体组织的蛋白质组学变异，并改进人类基因组的注释。利用微阵列或RNA-seq定位基因表达数量性状（eQTL）为人类生物学和解释全基因组关联研究（GWAS）的基因型-疾病关联发现提供了丰富的信息来源。相比之下，研究蛋白质序列和丰度变化的工作要少得多，蛋白质组学变异的遗传基础在很大程度上仍未被探索。本研究的目的是量化蛋白质的丰度并对蛋白质变体进行分类

DESCRIPTION (provided by applicant): The goals of the proposed research are to systematically characterize proteomic variation in multiple human tissues and improve the annotation of the human genome. The mapping of gene expression quantitative traits (eQTL) using microarray or RNA-seq has provided a rich source of information for human biology and for interpreting genotype-disease association findings from genome-wide association studies (GWAS). In contrast, much less work has examined variation in protein sequence and abundance, and the genetic basis of proteomic variation remains largely unexplored. The objective of this research is to quantify the abundance of proteins and to catalog protein variants in at least five human tissues using an advanced quantitative mass-spectrometry-based platform. The three Specific Aims are to (1) Quantitatively measure protein abundance in 100 individuals across five tissues, (2) Characterize variation and functionally annotate the tissue-specific human translatome; and (3) Map genetic variation that influences protein abundance (pQTL). Mass spectrometry data will be used to verify previously predicted intergenic and intronic regions that encode protein, and to better annotate the translated region of the human genome. By preferentially selecting multi-tissue donors, this project maximizes the utilization of the GTEx resource and provides a unique opportunity for quantifying protein diversity and variation between individuals and across tissues. Proteomic variation represents a molecular phenotype downstream of RNA expression and may provide a critical link between RNA expression and phenotypes. We expect that the pQTL mapping analysis may capture post-transcriptional regulatory mechanisms that are not captured in eQTL mapping studies. Together, the new data generated in this research will have an important positive impact on biological and biomedical research, because they offer important clues for interpreting genotype-phenotype correlation identified through genome-wide association studies. They will also validate the annotation of the human transcriptome with regards to location of translation start sites, splice isoform diversity and heteroallele and editing expression. They will provide a rich resource for the human genome community. Ultimately, we expect the ensemble of molecular phenotypes and annotation will improve our ability for predicting an individual's disease susceptibility, as well as contribute to the design of individualized prevention and intervention strategies.