Large-scale inference of population structure in presence of missingness using PCA
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Large-scale inference of population structure in presence of missingness using PCA. / Meisner, Jonas; Liu, Siyang; Huang, Mingxi; Albrechtsen, Anders.
In: Bioinformatics, Vol. 37, No. 13, 2021, p. 1868-1875.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Large-scale inference of population structure in presence of missingness using PCA
AU - Meisner, Jonas
AU - Liu, Siyang
AU - Huang, Mingxi
AU - Albrechtsen, Anders
PY - 2021
Y1 - 2021
N2 - 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 can not 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 0.08x. 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: EMU is written in Python and is freely available at https://github.com/rosemeis/emu.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
AB - 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 can not 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 0.08x. 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: EMU is written in Python and is freely available at https://github.com/rosemeis/emu.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
U2 - 10.1093/bioinformatics/btab027
DO - 10.1093/bioinformatics/btab027
M3 - Journal article
C2 - 33459779
VL - 37
SP - 1868
EP - 1875
JO - Computer Applications in the Biosciences
JF - Computer Applications in the Biosciences
SN - 1471-2105
IS - 13
ER -
ID: 258551145