Batch effects in population genomic studies with low‐coverage whole genome sequencing data: Causes, detection and mitigation
Batch effects in population genomic studies with low‐coverage whole genome sequencing data: Causes, detection and mitigation
复制标题
低覆盖率全基因组测序数据的群体基因组研究中的批次效应：原因、检测和缓解
DOI：
10.1111/1755-0998.13559
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发表时间：
2021
影响因子：
7.7
通讯作者：
Therkildsen, Nina Overgaard
中科院分区：
文献类型：
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作者：
Lou, Runyang Nicolas;Therkildsen, Nina Overgaard
在过去的几十年里，公开可用的测序数据的数量呈爆炸式增长。这为组合数据集提供了新的机会，以在群体基因组研究中实现前所未有的样本量、空间覆盖或时间复制。然而，一个普遍的担忧是，数据集之间的非生物差异可能会产生数据变化模式，从而混淆真实的生物模式，这一问题称为批次效应。在本文中，我们比较了从同一大西洋鳕鱼（Gadus morhua）种群生成的两批低覆盖率全基因组测序（lcWGS）数据。首先，我们表明，通过“批次效应朴素”生物信息学流程，批次效应会系统性地使我们的遗传多样性估计、种群结构推断和选择扫描产生偏差。然后，我们证明这些批次效应是由我们的数据集之间的多种技术差异造成的，包括测序化学（四通道与双通道）、测序运行、读段类型（单端与双端）、读段长度（125 与 150 bp）、DNA 降解水平（降解与保存良好）和测序深度（平均 0.8× 与 0.3×）。最后，我们说明了一组简单的生物信息学策略（例如不同的读数修剪和单核苷酸多态性过滤）可用于检测数据中的批次效应并显着减轻其影响。我们的结论是，只要明确考虑批次效应，组合数据集仍然是一种强大的方法。我们在本文中重点关注 lcWGS 数据，这些数据可能特别容易受到批次效应的某些原因的影响，但我们的许多结论也适用于其他测序策略。
Over the past few decades, there has been an explosion in the amount of publicly available sequencing data. This opens new opportunities for combining data sets to achieve unprecedented sample sizes, spatial coverage or temporal replication in population genomic studies. However, a common concern is that nonbiological differences between data sets may generate patterns of variation in the data that can confound real biological patterns, a problem known as batch effects. In this paper, we compare two batches of low‐coverage whole genome sequencing (lcWGS) data generated from the same populations of Atlantic cod (Gadus morhua). First, we show that with a “batch‐effect‐naive” bioinformatic pipeline, batch effects systematically biased our genetic diversity estimates, population structure inference and selection scans. We then demonstrate that these batch effects resulted from multiple technical differences between our data sets, including the sequencing chemistry (four‐channel vs. two‐channel), sequencing run, read type (single‐end vs. paired‐end), read length (125 vs. 150 bp), DNA degradation level (degraded vs. well preserved) and sequencing depth (0.8× vs. 0.3× on average). Lastly, we illustrate that a set of simple bioinformatic strategies (such as different read trimming and single nucleotide polymorphism filtering) can be used to detect batch effects in our data and substantially mitigate their impact. We conclude that combining data sets remains a powerful approach as long as batch effects are explicitly accounted for. We focus on lcWGS data in this paper, which may be particularly vulnerable to certain causes of batch effects, but many of our conclusions also apply to other sequencing strategies.