ArticleStatistics and its interface2011
Controlling Population Structure in Human Genetic Association Studies with Samples of Unrelated Individuals.
Article in Statistics and its interface, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Article
- Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations.bioRxiv : the preprint server for biology · 2024Article
- Gene-based association tests in family samples using GWAS summary statistics.Genetic epidemiology · 2024Article
- Germline determinants of aberrant signaling pathways in cancer.NPJ precision oncology · 2024Article
- Limitations of principal components in quantitative genetic association models for human studies.eLife · 2023Article
- Evaluation of linkage disequilibrium, population structure, and genetic diversity in the U.S. peanut mini core collection.BMC genomics · 2019Article
- Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models.Genetics · 2019Article
- Influence of common and rare genetic variation on warfarin dose among African-Americans and European-Americans using the exome array.Pharmacogenomics · 2017Article
- Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.Genetics · 2015Article
- Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.Human heredity · 2015Article
- A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model.Human heredity · 2015Article
- A statistical framework for testing the causal effects of fetal drive.Frontiers in genetics · 2014Article
- Double genomic control is not effective to correct for population stratification in meta-analysis for genome-wide association studies.Frontiers in genetics · 2012Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In genetic studies, associations between genotypes and phenotypes may be confounded by unrecognized population structure and/or admixture. Studies have shown that even in European populations, which are thought to be relatively homogeneous, population stratification exists and can affect the validity of association studies. A number of methods have been proposed to address this issue in recent years. Among them, the mixed-model based approach and the principal component-based approach have several advantages over other methods. However, these approaches have not been thoroughly evaluated on large human datasets. The objectives of this study are to (1) evaluate and compare the performance of the mixed-model approach and the principal component-based approach for genetic association mapping using human data consisting of unrelated individuals, and (2) understand the relationship between these two approaches. To achieve these goals, we simulate datasets based on the HapMap data under various scenarios. Our results indicate that the mixed-model approach performs well in controlling for population structure/admixture. It has similar performance as that based on principal component analysis. However, the approach combining mixed-model and principal component analysis does not perform as well as either method itself.
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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.