DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies.

中文翻译：

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在测序和基于阵列的基因型数据中检测和估计人类 DNA 样本的污染。

DNA 样本污染是 DNA 测序研究中的一个严重问题，可能会导致系统的基因型错误分类和假阳性关联。尽管存在检测和过滤跨物种污染的方法，但很少有检测物种内样本污染的方法可用。在本文中，我们描述了基于 (1) 测序读数和基于阵列的基因型数据的组合、(2) 单独的序列读数和 (3) 单独的基于阵列的基因型数据来识别物种内 DNA 样本污染的方法。测序读数分析允许在生成序列数据之后但在变异调用之前进行污染检测；基于阵列的基因型数据分析允许在生成昂贵的序列数据之前进行污染检测。通过对计算机和实验污染样品的组合分析，我们表明我们的方法可以可靠地检测和估计低至 1% 的污染水平。我们评估 DNA 污染对基因型准确性的影响，并提出有效的策略来筛选和预防测序研究中的 DNA 污染。