Although quantitative trait locus (QTL) associations have been identified for many molecular traits such as gene expression, it remains challenging to distinguish the causal nucleotide from nearby variants. In addition to traditional QTLs by association, allele-specific (AS) QTLs are a powerful measure of cis-regulation that are concordant with traditional QTLs but typically less susceptible to technical/environmental noise. However, existing methods for estimating causal variant probabilities (i.e., fine mapping) cannot produce valid estimates from asQTL signals due to complexities in linkage disequilibrium (LD). We introduce PLASMA (Population Allele-Specific Mapping), a fine-mapping method that integrates QTL and asQTL information to improve accuracy. In simulations, PLASMA accurately prioritizes causal variants over a wide range of genetic architectures. Applied to RNA-seq data from 524 kidney tumor samples, PLASMA achieves a greater power at 50 samples than conventional QTL-based fine mapping at 500 samples, with more than 17% of loci fine mapped to within five causal variants, compared to 2% by QTL-based fine mapping, and a 6.9-fold overall reduction in median credible set size compared to QTL-based fine mapping when applied to H3K27AC ChIP-seq from just 28 prostate tumor/normal samples. Variants in the PLASMA credible sets for RNA-seq and ChIP-seq were enriched for open chromatin and chromatin looping, respectively, at a comparable or greater degree than credible variants from existing methods while containing far fewer markers. Our results demonstrate how integrating AS activity can substantially improve the detection of causal variants from existing molecular data.

中文翻译：

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使用 PLASMA 进行等位基因特异性 QTL 精细定位。


尽管数量性状基因座（QTL）关联已被确定为许多分子性状（例如基因表达），但区分因果核苷酸与附近的变异仍然具有挑战性。除了传统的关联 QTL 之外，等位基因特异性 (AS) QTL 是一种强大的顺式调控措施，它与传统 QTL 一致，但通常不易受到技术/环境噪音的影响。然而，由于连锁不平衡（LD）的复杂性，现有的估计因果变异概率（即精细作图）的方法无法从asQTL信号产生有效的估计。我们引入了 PLASMA（群体等位基因特异性作图），这是一种整合 QTL 和 asQTL 信息以提高准确性的精细作图方法。在模拟中，PLASMA 可以准确地优先考虑各种遗传结构中的因果变异。应用于 524 个肾肿瘤样本的 RNA-seq 数据时，PLASMA 在 50 个样本上实现了比传统的基于 QTL 的精细定位在 500 个样本上更高的功效，超过 17% 的基因座精细映射到 5 个因果变异内，而这一比例为 2%当应用于来自仅 28 个前列腺肿瘤/正常样本的 H3K27AC ChIP-seq 时，与基于 QTL 的精细作图相比，中位可信集大小整体减少了 6.9 倍。 RNA-seq 和 ChIP-seq 的 PLASMA 可信组中的变体分别富集了开放染色质和染色质环，其程度与现有方法的可信变体相当或更高，同时包含的标记物少得多。我们的结果证明整合 AS 活性如何能够显着改善从现有分子数据中检测因果变异。