Quantitative trait locus (QTL) analyses of multiomic molecular traits, such as gene transcription (eQTL), DNA methylation (mQTL) and histone modification (haQTL), have been widely used to infer the functional effects of genome variants. However, the QTL discovery is largely restricted by the limited study sample size, which demands higher threshold of minor allele frequency and then causes heavy missing molecular trait–variant associations. This happens prominently in single-cell level molecular QTL studies because of sample availability and cost. It is urgent to propose a method to solve this problem in order to enhance discoveries of current molecular QTL studies with small sample size. In this study, we presented an efficient computational framework called xQTLImp to impute missing molecular QTL associations. In the local-region imputation, xQTLImp uses multivariate Gaussian model to impute the missing associations by leveraging known association statistics of variants and the linkage disequilibrium (LD) around. In the genome-wide imputation, novel procedures are implemented to improve efficiency, including dynamically constructing a reused LD buffer, adopting multiple heuristic strategies and parallel computing. Experiments on various multiomic bulk and single-cell sequencing-based QTL datasets have demonstrated high imputation accuracy and novel QTL discovery ability of xQTLImp. Finally, a C++ software package is freely available at https://github.com/stormlovetao/QTLIMP.

中文翻译：

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使用汇总统计插补增强小样本量的分子 QTL 研究的发现

多组分子性状的数量性状基因座 (QTL) 分析，如基因转录 (eQTL)、DNA 甲基化 (mQTL) 和组蛋白修饰 (haQTL)，已被广泛用于推断基因组变异的功能效应。然而，QTL 的发现在很大程度上受到有限的研究样本量的限制，这需要更高的次要等位基因频率阈值，然后导致严重缺失的分子性状 - 变异关联。由于样本的可用性和成本，这在单细胞水平的分子 QTL 研究中尤为突出。迫切需要提出一种方法来解决这个问题，以增强当前小样本分子 QTL 研究的发现。在这项研究中，我们提出了一个名为 xQTLImp 的有效计算框架来估算缺失的分子 QTL 关联。在局部区域插补中，xQTLImp 使用多元高斯模型通过利用已知的变体关联统计和周围的连锁不平衡 (LD) 来估算缺失的关联。在全基因组插补中，实施了新的程序以提高效率，包括动态构建重用的 LD 缓冲区、采用多种启发式策略和并行计算。对各种基于多组体和单细胞测序的 QTL 数据集的实验证明了 xQTLImp 的高插补精度和新的 QTL 发现能力。最后，在 https://github.com/stormlovetao/QTLIMP 上免费提供了一个 C++ 软件包。实施了新的程序以提高效率，包括动态构建重用的LD缓冲区，采用多种启发式策略和并行计算。对各种基于多组体和单细胞测序的 QTL 数据集的实验证明了 xQTLImp 的高插补精度和新的 QTL 发现能力。最后，在 https://github.com/stormlovetao/QTLIMP 上免费提供了一个 C++ 软件包。实施了新的程序以提高效率，包括动态构建重用的LD缓冲区，采用多种启发式策略和并行计算。对各种基于多组体和单细胞测序的 QTL 数据集的实验证明了 xQTLImp 的高插补精度和新的 QTL 发现能力。最后，在 https://github.com/stormlovetao/QTLIMP 上免费提供了一个 C++ 软件包。