ArticleGenetics2017
A Powerful Variant-Set Association Test Based on Chi-Square Distribution.
Article in Genetics, 2017. 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
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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
6 citing papers in PubMed.
- Robust tests for combining p-values under arbitrary dependency structures.Scientific reports · 2022Article
- Article
- Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.PLoS genetics · 2020Article
- An adaptive test for meta-analysis of rare variant association studies.Genetic epidemiology · 2020Article
- Integration of methylation QTL and enhancer-target gene maps with schizophrenia GWAS summary results identifies novel genes.Bioinformatics (Oxford, England) · 2019Article
- A novel gene-set association test based on variance-gamma distribution.Statistical methods in medical research · 2019Article
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
Detecting the association between a set of variants and a given phenotype has attracted a large amount of attention in the scientific community, although it is a difficult task. Recently, several related statistical approaches have been proposed in the literature; powerful statistical tests are still highly desired and yet to be developed in this area. In this paper, we propose a powerful test that combines information from each individual single nucleotide polymorphism (SNP) based on principal component analysis without relying on the eigenvalues associated with the principal components. We compare the proposed approach with some popular tests through a simulation study and real data applications. Our results show that, in general, the new test is more powerful than its competitors considered in this study; the gain in detecting power can be substantial in many situations.
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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.