ArticleGenetics2019
Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives.
Article in Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- fSuSiE enables fine-mapping of QTLs from genome-scale molecular profiles.bioRxiv : the preprint server for biology · 2025Article
- Single-cell splicing QTL analysis in pancreatic islets.Frontiers in bioinformatics · 2025Article
- Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations.bioRxiv : the preprint server for biology · 2024Article
- Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder.Nature genetics · 2023Article
- Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.Nature genetics · 2021Article
- Article
- GBAT: a gene-based association test for robust detection of trans-gene regulation.Genome biology · 2020Article
- Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis.NPJ genomic medicine · 2020Article
- Genetic regulation of gene expression and splicing during a 10-year period of human aging.Genome biology · 2019Article
- Cell-Type Heterogeneity in Adipose Tissue Is Associated with Complex Traits and Reveals Disease-Relevant Cell-Specific eQTLs.American journal of human genetics · 2019Article
- Genetic effects on the commensal microbiota in inflammatory bowel disease patients.PLoS genetics · 2019Article
- Genetics ofeLife · 2018Article
- Covariate selection for association screening in multiphenotype genetic studies.Nature genetics · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
High-throughput measurements of molecular phenotypes provide an unprecedented opportunity to model cellular processes and their impact on disease. These highly structured datasets are usually strongly confounded, creating false positives and reducing power. This has motivated many approaches based on principal components analysis (PCA) to estimate and correct for confounders, which have become indispensable elements of association tests between molecular phenotypes and both genetic and nongenetic factors. Here, we show that these correction approaches induce a bias, and that it persists for large sample sizes and replicates out-of-sample. We prove this theoretically for PCA by deriving an analytic, deterministic, and intuitive bias approximation. We assess other methods with realistic simulations, which show that perturbing any of several basic parameters can cause false positive rate (FPR) inflation. Our experiments show the bias depends on covariate and confounder sparsity, effect sizes, and their correlation. Surprisingly, when the covariate and confounder have [Formula: see text], standard two-step methods all have [Formula: see text]-fold FPR inflation. Our analysis informs best practices for confounder correction in genomic studies, and suggests many false discoveries have been made and replicated in some differential expression analyses.
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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 OpenQuestion graph.