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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Nonrandom missing data can bias Principal Component Analysis inference of population genetic structure.

Xueling Yi¹, Emily K Latch¹

¹Behavioral and Molecular Ecology Research Group, Department of Biological Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.

Molecular Ecology Resources

|August 31, 2021

Summary

Missing data in population genetics can distort principal component analysis (PCA) results. Biased missingness in individuals can falsely suggest admixture, impacting population structure interpretation.

Keywords:

RADseq next-generation sequencing population structure principal component analysis

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Area of Science:

Population genetics
Genomics
Bioinformatics

Background:

Next-generation sequencing (NGS) generates substantial missing data in population genetics studies.
Principal Component Analysis (PCA) is a common tool for visualizing population structure, often using mean imputation for missing data.

Purpose of the Study:

To investigate the impact of missing data, particularly nonrandomly distributed missingness, on PCA in population genetic analyses.
To evaluate how biased missing data affects the interpretation of population structure in both simulated and empirical datasets.

Main Methods:

Simulated population genetic data with varying levels and patterns of missingness (random vs. biased).
Empirical data from big brown bat (Eptesicus fuscus) using restriction site-associated DNA sequencing (RADseq) with different missing data filters.
Principal Component Analysis (PCA) applied to simulated and empirical datasets.

Main Results:

Biased missing data in individuals shifts them towards the origin in PCA plots, mimicking admixed individuals.
This effect was observed in both simulated data and empirical RADseq data from big brown bats.
Nonrandom missing data is common in non-model organisms due to sample quality variations.

Conclusions:

Missing data, especially when nonrandomly distributed, can significantly bias PCA results and lead to misinterpretation of population structure.
Researchers should carefully consider and visualize missing data patterns when using PCA in population genetics.
Recommendations include plotting PCA with missingness gradients, cautious interpretation of central samples, and using complementary analyses for validation.