Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which > 80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyse jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses.

中文翻译：

---

血浆中人蛋白质丰度的完全联合贝叶斯定量性状基因座图谱

分子定量性状基因座（QTL）分析越来越多地用于探索复杂性状的遗传结构，但是现有研究并未利用共享的调控模式，并且承受着巨大的多重负担，这阻碍了对反式关联等弱信号的检测。在这里，我们介绍了一个完全多变量的蛋白质组学QTL（pQTL）分析，该分析是使用我们最近提出的贝叶斯方法LOCUS对来自两个临床队列的数据进行的，血浆蛋白水平通过质谱和基于适体的测定进行了定量。我们的两阶段研究确定了第一个队列中的136个pQTL关联，其中> 80％在第二个独立队列中重复，并且具有功能基因组元件和疾病风险基因座的丰富性。此外，通过两种蛋白质组学技术量化了蛋白质丰度的pQTL中有78％在所有试验中得到了证实。我们在（1）这些数据和（2）模拟真实数据的合成数据上与标准单变量QTL映射进行了全面比较，显示LOCUS如何在整个基因组范围内借鉴相关蛋白质水平和标记物的强度来有效地提高统计能力。值得注意的是，单变量方法会丢失LOCUS发现的15％的pQTL，包括成功进行独立验证的多个反式和多效命中。最后，对来自这两个队列的大量临床数据的分析表明，LOCUS鉴定的遗传驱动蛋白与低度炎症，胰岛素抵抗和血脂异常有关，因此可能是代谢疾病的内表型。尽管对pQTL的临床作用的考虑超出了我们的工作范围，但这些发现产生了有用的假设，有待进一步研究。可从我们的可搜索数据库在线访问所有结果。由于其有效的变体贝叶斯实现，LOCUS可以共同分析数千个特征和数百万个标记。它的适用性超出了pQTL研究的范围，为大规模的全基因组关联和QTL分析开辟了新的前景。