MOTIVATION: Principal 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. RESULTS: We 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 IMPLEMENTATION: EMU is written in Python and is freely available at https://github.com/rosemeis/emu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. MATERIALS AND METHODS: We have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure. RESULTS: Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. CONCLUSION: We show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.