ArticleHuman heredity2014
Using gene expression to improve the power of genome-wide association analysis.
Article in Human heredity, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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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
8 citing papers in PubMed, 9 citations in OpenAlex.
- Leveraging omics data to boost the power of genome-wide association studies.HGG advances · 2022Article
- Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus.Journal of medical genetics · 2021Review
- Leveraging existing GWAS summary data of genetically correlated and uncorrelated traits to improve power for a new GWAS.Genetic epidemiology · 2020Article
- Benefits and Challenges of Rare Genetic Variation in Alzheimer's Disease.Current genetic medicine reports · 2019Article
- Article
- Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants.Human genetics · 2017Article
- Powerful association test combining rare variant and gene expression using family data from Genetic Analysis Workshop 19.BMC proceedings · 2016Article
- Genetics of human metabolism: an update.Human molecular genetics · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
BACKGROUND/
aimsGenome-wide association (GWA) studies have reported susceptible regions in the human genome for many common diseases and traits; however, these loci only explain a minority of trait heritability. To boost the power of a GWA study, substantial research endeavors have been focused on integrating other available genomic information in the analysis. Advances in high through-put technologies have generated a wealth of genomic data and made combining SNP and gene expression data become feasible.
resultsIn this paper, we propose a novel procedure to incorporate gene expression information into GWA analysis. This procedure utilizes weights constructed by gene expression measurements to adjust p values from a GWA analysis. RESULTS from simulation analyses indicate that the proposed procedures may achieve substantial power gains, while controlling family-wise type I error rates at the nominal level. To demonstrate the implementation of our proposed approach, we apply the weight adjustment procedure to a GWA study on serum interferon-regulated chemokine levels in systemic lupus erythematosus patients. The study results can provide valuable insights for the functional interpretation of GWA signals. AVAILABILITY: The R source code for implementing the proposed weighting procedure is available at http://www.biostat.umn.edu/∼yho/research.html.
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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.