ArticleBMC genetics2017
Gain of power of the general regression model compared to Cochran-Armitage Trend tests: simulation study and application to bipolar disorder.
Article in BMC genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- On the analysis of genetic association with long-read sequencing data.PLoS genetics · 2025Article
- Genome-wide association study of early-onset and late-onset postpartum depression: the IGEDEPP prospective study.European psychiatry : the journal of the Association of European Psychiatrists · 2024Article
- The X factor: A robust and powerful approach to X-chromosome-inclusive whole-genome association studies.Genetic epidemiology · 2021Article
- Identification of Recessively Inherited Genetic Variants Potentially Linked to Pancreatic Cancer Risk.Frontiers in oncology · 2021Article
- Non-additive (dominance) effects of genetic variants associated with refractive error and myopia.Molecular genetics and genomics : MGG · 2020Article
- Replicability analysis in genome-wide association studies via Cartesian hidden Markov models.BMC bioinformatics · 2019Article
- Decomposing Pearson's χAnnals of human genetics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundMost genome-wide association studies assumed an additive model of inheritance which may result in significant loss of power when there is a strong departure from additivity. The General Regression Model (GRM), which allows performing an assumption-free test for association by testing for both additive effect and deviation from additive effect, may be more appropriate for association tests. Additionally, GRM allows testing the underlying genetic model. We compared the power of GRM association test to additive and other Cochran-Armitage Trend (CAT) tests through simulations and by applying GRM to a large case/control sample, the bipolar Welcome Trust Case Control Cohort data. Simulations were performed on two sets of case/control samples (1000/1000 and 2000/2000), using a large panel of genetic models. Four association tests (GRM and additive, recessive and dominant CAT tests) were applied to all replicates.
resultsWe showed that GRM power to detect association was similar or greater than the additive CAT test, in particular in case of recessive inheritance, with up to 67% gain in power. GRM analysis of genome-wide bipolar disorder Welcome Trust Consortium data (1998 cases/3004 controls) showed significant association in the 16p12 region (rs420259; P = 3.4E-7) which has not been identified using the additive CAT test. As expected, rs42025 fitted a non-additive (recessive) model.
conclusionsGRM provides increased power compared to the additive CAT test for association studies and is easily applicable.
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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 Socratic graph.