ArticleJournal of computational biology : a journal of computational molecular cell biology2022
Trade-offs of Linear Mixed Models in Genome-Wide Association Studies.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Confounding fuels misinterpretation in human genetics.Proceedings. Biological sciences · 2025Article
- Confounding Fuels Misinterpretation in Human Genetics.bioRxiv : the preprint server for biology · 2025Article
- Tumor suppressor p53 modulates activity-dependent synapse strengthening, autism-like behavior and hippocampus-dependent learning.Molecular psychiatry · 2023Article
- Limitations of principal components in quantitative genetic association models for human studies.eLife · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Motivated by empirical arguments that are well known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS. First, we study the sensitivity of LMMs to the inclusion of a candidate single nucleotide polymorphism (SNP) in the kinship matrix, which is often done in practice to speed up computations. Our results shed light on the size of the error incurred by including a candidate SNP, providing a justification to this technique to trade off velocity against veracity. Second, we investigate how mixed models can correct confounders in GWAS, which is widely accepted as an advantage of LMMs over traditional methods. We consider two sources of confounding factors-population stratification and environmental confounding factors-and study how different methods that are commonly used in practice trade off these two confounding factors differently.
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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.