ArticlePloS one2011
A robust statistical method for association-based eQTL analysis.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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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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
5 citing papers in PubMed.
- Mining and Analysis of Salt Tolerance Genes in Maize at the Seedling Stage.Current issues in molecular biology · 2026Article
- Race-Dependent Association of Single-Nucleotide Polymorphisms in TrkB Receptor in People Living with HIV and Depression.Neurotoxicity research · 2021Article
- An approach for normalization and quality control for NanoString RNA expression data.Briefings in bioinformatics · 2021Article
- Genome-wide eQTLs and heritability for gene expression traits in unrelated individuals.BMC genomics · 2014Article
- The Brassica rapa FLC homologue FLC2 is a key regulator of flowering time, identified through transcriptional co-expression networks.Journal of experimental botany · 2013Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundIt has been well established that theoretical kernel for recently surging genome-wide association study (GWAS) is statistical inference of linkage disequilibrium (LD) between a tested genetic marker and a putative locus affecting a disease trait. However, LD analysis is vulnerable to several confounding factors of which population stratification is the most prominent. Whilst many methods have been proposed to correct for the influence either through predicting the structure parameters or correcting inflation in the test statistic due to the stratification, these may not be feasible or may impose further statistical problems in practical implementation. METHODOLOGY: We propose here a novel statistical method to control spurious LD in GWAS from population structure by incorporating a control marker into testing for significance of genetic association of a polymorphic marker with phenotypic variation of a complex trait. The method avoids the need of structure prediction which may be infeasible or inadequate in practice and accounts properly for a varying effect of population stratification on different regions of the genome under study. Utility and statistical properties of the new method were tested through an intensive computer simulation study and an association-based genome-wide mapping of expression quantitative trait loci in genetically divergent human populations. RESULTS/
conclusionsThe analyses show that the new method confers an improved statistical power for detecting genuine genetic association in subpopulations and an effective control of spurious associations stemmed from population structure when compared with other two popularly implemented methods in the literature of GWAS.
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