Large-scale inference of population structure in presence of missingness using PCA
Large-scale inference of population structure in presence of missingness using PCA
复制标题
使用 PCA 在存在缺失的情况下大规模推断种群结构
DOI：
10.1093/bioinformatics/btab027
复制
发表时间：
2021
期刊：
影响因子：
5.8
通讯作者：
Albrechtsen Anders
中科院分区：
文献类型：
--
作者：
Meisner Jonas;Liu Siyang;Huang Mingxi;Albrechtsen Anders
主成分分析（PCA）是遗传学中常用的工具，用于捕获和可视化种群结构。由于测序技术的进步，例如广泛使用的无创产前检测，正在产生超低覆盖率测序的大量数据集。这些数据集的特点是有大量的缺失的基因型information.ResultsWe提出EMU，一种方法，用于推断人口结构中存在猖獗的非随机缺失。我们通过模拟表明，几种常用的PCA方法不能处理来自各种来源的缺失数据，这导致了有偏见的结果，因为个人被投射到PC空间的基础上，他们的缺失量。在准确性方面，EMU优于现有的方法，该方法在具有竞争力的速度的同时还可以容纳遗漏。我们进一步在中国百万富翁计划第一阶段数据集的大约10万个个体上测试了EMU，这些个体被浅测序到大约0.08×。从这些数据中，我们能够捕捉到汉族的人口结构，并在CPU小时而不是CPU年的问题上重现以前的分析。EMU在缺失情况下准确推断种群结构的能力将随着大规模遗传数据集数量的增加而变得越来越重要。可用性和实现EMU是用Python编写的，可以在https://github.com/rosemeis/emu.Supplementary信息上免费获得补充数据可以在Bioinformatics online上获得。
MotivationPrincipal component analysis (PCA) is a commonly used tool in genetics to capture and visualize population structure. Due to technological advances in sequencing, such as the widely used non-invasive prenatal test, massive datasets of ultra-low coverage sequencing are being generated. These datasets are characterized by having a large amount of missing genotype information.ResultsWe present EMU, a method for inferring population structure in the presence of rampant non-random missingness. We show through simulations that several commonly used PCA methods cannot handle missing data arisen from various sources, which leads to biased results as individuals are projected into the PC space based on their amount of missingness. In terms of accuracy, EMU outperforms an existing method that also accommodates missingness while being competitively fast. We further tested EMU on around 100K individuals of the Phase 1 dataset of the Chinese Millionome Project, that were shallowly sequenced to around 0.08×. From this data we are able to capture the population structure of the Han Chinese and to reproduce previous analysis in a matter of CPU hours instead of CPU years. EMU’s capability to accurately infer population structure in the presence of missingness will be of increasing importance with the rising number of large-scale genetic datasets.Availability and implementationEMU is written in Python and is freely available at https://github.com/rosemeis/emu.Supplementary informationSupplementary data are available atBioinformaticsonline.