The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is "mediation" by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are "cis-mediators" of trans-eQTLs, including those "cis-hubs" involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

中文翻译：

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使用基因组介导分析识别许多人体组织中反式 eQTL 的顺式介体。


遗传性遗传变异对人类基因表达的影响是众所周知的。大多数已知的表达数量性状位点 (eQTL) 影响局部基因 (cis-eQTL) 的表达。需要更多的研究来确定遗传变异对远缘基因（反式 eQTL）的影响并了解其生物学机制。一种常见的反式 eQTL 机制是本地（顺式）转录物的“介导”。因此，介导分析可应用于全基因组 SNP 和表达数据，以便识别作为反式 eQTL 的“顺式介体”的转录本，包括参与许多转基因调节的那些“顺式中枢”。识别此类介质有助于我们了解调控网络，并提出反式 eQTL 的生物学机制，这两者都与了解复杂疾病的易感性相关。来自基因型组织表达 (GTEx) 程序的多组织表达数据为研究跨人体组织类型的顺式介导提供了独特的机会。然而，生物系统中存在复杂的隐藏混杂效应可能会使中介分析具有挑战性，并且容易产生混杂偏差，特别是在不同样本中进行时。为了解决这个问题，我们提出了一种新方法：带有自适应混杂调整的基因组中介分析（GMAC）。它可以搜索非常大的变量池，并为每个中介测试自适应地选择潜在的混杂变量。对模拟数据和 GTEx 数据的分析表明，GMAC 对混杂因素的自适应选择提高了中介分析的功效和精度。 GMAC 在 GTEx 数据中的应用为观察到的跨组织类型的顺式集线器和反式 eQTL 调控模式提供了新的见解。