PCR techniques, both quantitative (qPCR) and non-quantitative, have been used to estimate allele frequency in a population. However, the labor required to sample numerous individuals, and subsequently handle each sample, makes quantification of rare mutations, including pesticide resistance genes at the early stages of resistance development, challenging. Meanwhile, pooling DNA from multiple individuals as a "bulk sample" may reduce handling costs. The qPCR output for a bulk sample, however, contains uncertainty owing to variations in DNA yields from each individual, in addition to measurement errors. In this study, we developed a statistical model for the interval estimation of allele frequency using ΔΔCq-based qPCR analyses of multiple bulk samples collected from a population. We assumed a gamma distribution as the individual DNA yield and developed an R package for parameter estimation, which was verified with real DNA samples from acaricide-resistant spider mites, as well as a numerical simulation. Our model resulted in unbiased point estimates of the allele frequency compared with simple averaging of the ΔΔCq values, while their confidence intervals suggest that collecting and pooling additional samples from individuals may produce higher precision than individual PCR tests with moderate sample sizes.

中文翻译：

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freqpcr：使用qPCRΔΔCq量度从大量样品中估算群体等位基因频率

定量（qPCR）和非定量PCR技术都已用于估计人群中的等位基因频率。但是，对大量个体进行采样并随后处理每个样本所需的工作使得对罕见突变（包括在抗性发展早期的农药抗性基因）进行定量分析具有挑战性。同时，汇集来自多个个体的DNA作为“大量样本”可以降低处理成本。但是，由于大量样品的qPCR输出，除了测量误差外，还由于每个个体的DNA产量变化而存在不确定性。在这项研究中，我们开发了一个统计模型，用于使用基于ΔΔCq的qPCR分析等位基因频率的间隔估计，该qPCR分析是从一个种群中收集的多个散装样品。我们假定伽玛分布为单个DNA的产量，并开发了用于参数估计的R包，并已使用抗杀螨剂的真实螨虫的真实DNA样品进行了验证，并进行了数值模拟。与对ΔΔCq值进行简单平均相比，我们的模型得出了等位基因频率的无偏点估计，而他们的置信区间表明，从个体中收集和合并其他样本可能比具有中等样本量的个体PCR测试产生更高的精度。