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Ø Bleka¹, J González-Bao², M de la Puente²

¹Department of Forensic Sciences, Oslo University Hospital, Norway.

Forensic science international. Genetics

|March 9, 2026

概括

我们使用EuroForMix模型中的标记放大效率 (MAE) 改进了混合样本中未知DNA贡献者的基因型预测. 这种方法提高了大规模并行测序数据的准确性,而不需要外部校准.

科学领域:

法医遗传学法医遗传学
计算生物学是一种计算生物学.
统计建模统计建模

背景情况:

从DNA混合物中准确地预测基因型在法医中至关重要.
大规模并行测序 (MPS) 生成复杂的混合数据.
像EuroForMix这样的现有模型可能需要外部校准数据.

研究的目的:

扩展EuroForMix模型,以在MPS混合数据中改进基因型预测.
将标记放大效率 (MAE) 参数直接纳入模型.
消除对外部校准数据的需求,通过从混合物档案本身估计MAE.

主要方法:

开发了一种两步程序: 1. 根据混合物概况估计MAE. 2. 2. 2. 这是一个很棒的节目. 在扩展的EuroForMix模型中使用MAE估计作为固定输入.
评估了四种MAE估计方法:两种经验方法和两种贝叶斯方法 (使用变量推理).
预测性能在五个数据集 (SNP,微型,STR和CE-STR) 上使用Brier分数,准确性和校准,包括高确定性值 (≥0.95) 进行了评估.

主要成果:

与默认的EuroForMix模型相比,基于MAE的方法显著改善了基因型预测,特别是对于MPS数据集.
经验MAE估计产生了更具决定性的预测和更高的高确定性覆盖率.
贝叶斯的MAE估计提供了更保守和更好的校准概率.
对于毛细管电泳STR数据的改进是适度的;装箱式EuroForMix版本对高密度SNP面板具有竞争力.

结论:

纳入MAE参数可以提高基因型预测在DNA混合分析中的准确性,特别是在MPS数据中.
两步MAE估计策略提供了一个完全联合推断的计算可处理和有效的替代方案.
贝叶斯对MAE估计的方法提供了强大的和精确校准的预测,而经验方法提供了更高的确定性覆盖范围.

关键词:

基因组混合物的解解.在MPS中,MPS是MPS.标记器放大效率的效率是什么变化推理的推理是变化的.

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