Previous expression quantitative trait loci (eQTL) studies have identified thousands of genetic variants to be associated with gene expression at the mRNA level in the human liver. However, protein expression often correlates poorly with mRNA levels. Thus, protein quantitative trait loci (pQTL) study is required to identify genetic variants that regulate protein expression in human livers. We conducted a genome-wide pQTL study in 287 normal human liver samples and identified 900 local pQTL variants and 4026 distant pQTL variants. We further discovered 53 genome hotspots of pQTL variants. Transcriptional region mapping analysis showed that 1133 pQTL variants are in transcriptional regulatory regions. Genomic region enrichment analysis of the identified pQTL variants revealed 804 potential regulatory interactions among 595 predicted regulators (e.g., non-coding RNAs) and 394 proteins. Moreover, pQTL variants and trait-variant integration analysis implied several novel mechanisms underlying the relationships between protein expression and liver diseases, such as alcohol dependence. Notably, over 2000 of the identified pQTL variants have not been reported in previous eQTL studies, suggesting extensive involvement of genetic polymorphisms in post-transcriptional regulation of protein expression in human livers. We have partially established protein expression regulation networks in human livers and generated a wealth of pQTL data that could serve as a valuable resource for the scientific community.

中文翻译：

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对人类肝脏中蛋白质表达调控网络进行全基因组 pQTL 分析。


先前的表达数量性状位点 (eQTL) 研究已鉴定出数千种与人类肝脏 mRNA 水平的基因表达相关的遗传变异。然而，蛋白质表达通常与 mRNA 水平相关性较差。因此，需要进行蛋白质数量性状位点（pQTL）研究来鉴定调节人类肝脏蛋白质表达的遗传变异。我们对 287 个正常人肝脏样本进行了全基因组 pQTL 研究，并鉴定了 900 个局部 pQTL 变异和 4026 个远距离 pQTL 变异。我们进一步发现了 53 个 pQTL 变异的基因组热点。转录区作图分析表明，1133个pQTL变异位于转录调控区。对已识别的 pQTL 变体的基因组区域富集分析揭示了 595 个预测调节因子（例如非编码 RNA）和 394 个蛋白质之间的 804 种潜在调节相互作用。此外，pQTL 变异和性状变异整合分析暗示了蛋白质表达与肝脏疾病（例如酒精依赖）之间关系的几种新机制。值得注意的是，超过 2000 个已识别的 pQTL 变异在之前的 eQTL 研究中尚未报道，这表明遗传多态性广泛参与人类肝脏蛋白质表达的转录后调控。我们已经在人类肝脏中部分建立了蛋白质表达调控网络，并生成了大量的 pQTL 数据，可以作为科学界的宝贵资源。