Large-scale inference of population structure in presence of missingness using PCA | Zendy

Jonas Meisner | Zendy; Siyang Liu | Zendy; Mingxi Huang | Zendy; Anders Albrechtsen | Zendy

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Large-scale inference of population structure in presence of missingness using PCA

Author(s) -

Jonas Meisner,

Siyang Liu,

Mingxi Huang,

Anders Albrechtsen

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab027

Subject(s) - missing data , python (programming language) , inference , population , computer science , principal component analysis , data mining , scale (ratio) , statistics , artificial intelligence , machine learning , mathematics , cartography , demography , sociology , operating system , geography

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.

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