ArticleGenetic epidemiology2009
A new association test to test multiple-marker association.
Article in Genetic epidemiology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 7 citations in OpenAlex.
- On multi-marker tests for association in case-control studies.Frontiers in genetics · 2013Article
- A composite likelihood approach to latent multivariate Gaussian modeling of SNP data with application to genetic association testing.Biometrics · 2012Article
- Powerful multi-marker association tests: unifying genomic distance-based regression and logistic regression.Genetic epidemiology · 2010Article
- A unified framework for detecting genetic association with multiple SNPs in a candidate gene or region: contrasting genotype scores and LD patterns between cases and controls.Human heredity · 2010Article
- Incorporating multiple-marker information to detect risk loci for rheumatoid arthritis.BMC proceedings · 2009Article
- Multistage analysis strategies for genome-wide association studies: summary of group 3 contributions to Genetic Analysis Workshop 16.Genetic epidemiology · 2009Article
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 at 2 institutions in 2 countries.
Funding
Abstract
As a result of the availability of a very large numbers of single nucleotide polymorphisms, there has been increasing interest in genetic associations involving several closely linked loci. Methods for detection of association between traits and multiple genetic polymorphisms are being rapidly developed, which include the Hotelling's T(2) test and the LD contrast (LDC) tests. The Hotelling's T(2) test can be considered as a test to compare the means of the genotypic score in cases and controls; while the (LDC) tests can be considered as a test to compare the variance-covariance matrices of the genotypic score in cases and controls. In this article, we propose a likelihood ratio test which simultaneously compares the means and the variance-covariance matrices of the genotypic score in cases and controls. We use simulation studies to evaluate the type I error rate of the proposed test, and compare the power of the test with the Hotelling's T(2) test and the LDC tests. The simulation results show that when marginal effects of the disease loci are strong, the proposed test is more powerful than the LDC tests and similar with or slightly less powerful than the Hotelling's T(2) test. If there are interaction effects and weak or no marginal effects, the proposed method is more powerful than the Hotelling's T(2) test and slightly more powerful than the LDC tests.
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.