Limitations of principal components in quantitative genetic association models for human studies

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Version of Record published: June 1, 2023 (Go to version)
Accepted: May 4, 2023
Accepted Manuscript published: May 4, 2023 (This version)
Received: April 4, 2022
Preprint posted: March 27, 2022 (Go to version)

Abstract
Data availability
Article and author information
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Abstract

Principal Component Analysis (PCA) and the Linear Mixed-effects Model (LMM), sometimes in combination, are the most common genetic association models. Previous PCA-LMM comparisons give mixed results, unclear guidance, and have several limitations, including not varying the number of principal components (PCs), simulating simple population structures, and inconsistent use of real data and power evaluations. We evaluate PCA and LMM both varying number of PCs in realistic genotype and complex trait simulations including admixed families, subpopulation trees, and real multiethnic human datasets with simulated traits. We find that LMM without PCs usually performs best, with the largest effects in family simulations and real human datasets and traits without environment effects. Poor PCA performance on human datasets is driven by large numbers of distant relatives more than the smaller number of closer relatives. While PCA was known to fail on family data, we report strong effects of family relatedness in genetically diverse human datasets, not avoided by pruning close relatives. Environment effects driven by geography and ethnicity are better modeled with LMM including those labels instead of PCs. This work better characterizes the severe limitations of PCA compared to LMM in modeling the complex relatedness structures of multiethnic human data for association studies.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Code is available at https://github.com/OchoaLab/pca-assoc-paper

The following previously published data sets were used

1. Fairley S
2. et al
(2020) 1000 Genomes Project, high-coverage version
International Genome Sample Resource.

ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000G_2504_high_coverage/working/20190425_NYGC_GATK/
1. Bergstrom A
2. et al
(2020) Human Genome Diversity Panel, whole-genome sequencing version
Wellcome Sanger Institute.

ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516/
1. Lazaridis I
2. et al
(2016) Human Origins
David Reich Lab.

https://reich.hms.harvard.edu/datasets

Article and author information

Author details

Yiqi Yao

Department of Biostatistics and Bioinformatics, Duke University, Durham, United States

Competing interests
Yiqi Yao, is affiliated with BenHealth Consulting. The author has no financial interests to declare..
Alejandro Ochoa

Department of Biostatistics and Bioinformatics, Duke University, Durham, United States

For correspondence
alejandro.ochoa@duke.edu

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0003-4928-3403

Funding

Whitehead Foundation

Alejandro Ochoa

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

Magnus Nordborg, Gregor Mendel Institute, Austria

Version history

Preprint posted: March 27, 2022 (view preprint)
Received: April 4, 2022
Accepted: May 4, 2023
Accepted Manuscript published: May 4, 2023 (version 1)
Version of Record published: June 1, 2023 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.