Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data
Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data
复制标题
根据基因型或低深度测序数据对密切家族关系进行无等位基因频率推断
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
R. Waples
中科院分区：
文献类型：
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作者：
R. Waples
了解个体之间的亲缘关系在许多研究领域都很重要，并且已经开发了许多从遗传数据推断成对亲缘关系的方法。然而，这些方法中的大多数并不是为数据有限的情况而开发的。具体来说，大多数方法依赖于群体等位基因频率的可用性、变异的相对基因组位置和准确的基因型数据。但在对非模式生物或古代样本的研究中，这些数据并不总是可用的。基于此，我们提出了一种既不需要等位基因频率信息也不需要基因组位置信息的配对亲缘推断新方法。此外，它不仅可以应用于准确的基因型数据，还可以应用于无法准确调用基因型的低深度测序数据。我们使用来自一系列人群的数据对其进行了评估，并表明它可以用于推断密切的家庭关系，其准确度与广泛使用的依赖于群体等位基因频率的方法相似。此外，我们表明我们的方法对SNP确定具有鲁棒性，并且适用于使用不同策略（包括重测序和RADseq）生成的低深度测序数据，这对于应用于各种种群和物种非常重要。
Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situa‐ tions where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non‐model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor infor‐ mation on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low‐depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accu‐ racy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low‐ depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.