ArticleGenetics2008
Gene-centric genomewide association study via entropy.
Article in Genetics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed.
- Associations of Genetic Variants at Nongenic Susceptibility Loci with Breast Cancer Risk and Heterogeneity by Tumor Subtype in Southern Han Chinese Women.BioMed research international · 2016Trial
- Research on multi-trait genome association study method based on Shannon information entropy.BMC bioinformatics · 2026Article
- Article
- Sparse group variable selection for gene-environment interactions in the longitudinal study.Genetic epidemiology · 2022Article
- Information Theory in Computational Biology: Where We Stand Today.Entropy (Basel, Switzerland) · 2020Article
- Semiparametric Bayesian variable selection for gene-environment interactions.Statistics in medicine · 2020Article
- Genome-Wide Gene-Based Multi-Trait Analysis.Frontiers in genetics · 2020Article
- Turning genome-wide association study findings into opportunities for drug repositioning.Computational and structural biotechnology journal · 2020Review
- Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis.Genetic epidemiology · 2019Article
- Neurotransmitter Pathway Genes in Cognitive Decline During Aging: Evidence for GNG4 and KCNQ2 Genes.American journal of Alzheimer's disease and other dementias · 2018Article
- Dissecting gene-environment interactions: A penalized robust approach accounting for hierarchical structures.Statistics in medicine · 2018Article
- A Genome-wide Association Analysis in Four Populations Reveals Strong Genetic Heterogeneity For Birth Weight.Current genomics · 2016Article
- A Nonlinear Model for Gene-Based Gene-Environment Interaction.International journal of molecular sciences · 2016Review
- Risk Prediction Modeling of Sequencing Data Using a Forward Random Field Method.Scientific reports · 2016Article
- A penalized robust semiparametric approach for gene-environment interactions.Statistics in medicine · 2015Article
- Integrative analysis of gene-environment interactions under a multi-response partially linear varying coefficient model.Statistics in medicine · 2014Article
- Entropy-based selection for maternal-fetal genotype incompatibility with application to preterm prelabor rupture of membranes.BMC genetics · 2014Article
- A modified generalized Fisher method for combining probabilities from dependent tests.Frontiers in genetics · 2014Article
- Gene-based Genomewide Association Analysis: A Comparison Study.Current genomics · 2013Article
- Genetic association studies: an information content perspective.Current genomics · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.
Indexed as
Identifiers
What OpenQuestion holds
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.