ArticleeLife2023
Limitations of principal components in quantitative genetic association models for human studies.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
28 citing papers in PubMed.
- Within-sibling attenuation of polygenic risk score accuracy: investigating the effects of principal component analysis, LD score regression, and mixed model association in the UK Biobank.Human genetics · 2026Article
- Aging Trajectory Analysis of Asphalt: Differential Regulation of UV Aging Processes by Anti-Aging Agents with Varied Mechanisms.Materials (Basel, Switzerland) · 2026Article
- When can whole-genome SNP heritability be reliably estimated from summary statistics?bioRxiv : the preprint server for biology · 2026Article
- Quantifying direct genetic signal captured by principal component adjustment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Synergistic effects of ABCG2 Q141K variant in combination with alcohol consumption and male sex on gout risk in a rare-event Taiwanese cohort.Scientific reports · 2026Article
- Revisiting AI Interpretability in Precision Oncology: Why Predictive Accuracy Does Not Ensure Stable Feature Importance.Cancers · 2026Article
- Genome-wide association study of the reproductive, body size, and carcass-related latent and directly measured traits in admixed beef heifers.Frontiers in genetics · 2026Article
- Article
- Grizzly bear population genomics across a coastal-interior ecotone in British Columbia, Canada.G3 (Bethesda, Md.) · 2025Article
- Coancestry superposed on admixed populations yields measures of relatedness at individual-level resolution.PLoS computational biology · 2025Article
- Confounding fuels misinterpretation in human genetics.Proceedings. Biological sciences · 2025Article
- Microsatellite-based genetic diversity assessment of Donggala cattleVeterinary world · 2025Article
- PCA-Driven Multivariate Trait Integration in Alfalfa Breeding: A Selection Model for High-Yield and Stable Progenies.Plants (Basel, Switzerland) · 2025Article
- Limitations of Linear Dimensional Reduction Methods in Chronic Obstructive Pulmonary Disease Phenotyping.American journal of respiratory and critical care medicine · 2025Article
- Confounding Fuels Misinterpretation in Human Genetics.bioRxiv : the preprint server for biology · 2025Article
- Decoding cancer prognosis with deep learning: the ASD-cancer framework for tumor microenvironment analysis.mSystems · 2025Article
- Survival of the fittest: genomic investigations of the bay scallop reveal a shift in population structure through a summer mortality event.BMC genomics · 2025Article
- A Litmus Test for Confounding in Polygenic Scores.bioRxiv : the preprint server for biology · 2025Article
- Genetic Nurture Effects on Type 2 Diabetes Among Chinese Han Adults: A Family-Based Design.Biomedicines · 2025Article
- Inhibitory KIRs decrease HLA class II-mediated protection in Type 1 Diabetes.PLoS genetics · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.