ArticleGenetic epidemiology2013
A geometric framework for evaluating rare variant tests of association.
Article in Genetic epidemiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants.Frontiers in genetics · 2020Article
- Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20.BMC genetics · 2018Article
- Evaluating the performance of gene-based tests of genetic association when testing for association between methylation and change in triglyceride levels at GAW20.BMC proceedings · 2018Article
- IMPROVED PERFORMANCE OF GENE SET ANALYSIS ON GENOME-WIDE TRANSCRIPTOMICS DATA WHEN USING GENE ACTIVITY STATE ESTIMATES.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association.Frontiers in genetics · 2017Article
- A general approach for combining diverse rare variant association tests provides improved robustness across a wider range of genetic architectures.European journal of human genetics : EJHG · 2016Article
- A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests.BMC proceedings · 2016Article
- A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures.BMC proceedings · 2016Article
- Analysis of rare variant population structure in Europeans explains differential stratification of gene-based tests.European journal of human genetics : EJHG · 2014Article
- Pathway analysis approaches for rare and common variants: insights from Genetic Analysis Workshop 18.Genetic epidemiology · 2014Article
- Testing genetic association with rare and common variants in family data.Genetic epidemiology · 2014Article
- Modest impact on risk for autism spectrum disorder of rare copy number variants at 15q11.2, specifically breakpoints 1 to 2.Autism research : official journal of the International Society for Autism Research · 2014Article
- A generalized genetic random field method for the genetic association analysis of sequencing data.Genetic epidemiology · 2014Article
- Evaluation of the power and type I error of recently proposed family-based tests of association for rare variants.BMC proceedings · 2014Article
- Evaluating the impact of genotype errors on rare variant tests of association.Frontiers in genetics · 2014Article
- Evaluating the concordance between sequencing, imputation and microarray genotype calls in the GAW18 data.BMC proceedings · 2014Article
- Application of family-based tests of association for rare variants to pathways.BMC proceedings · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The wave of next-generation sequencing data has arrived. However, many questions still remain about how to best analyze sequence data, particularly the contribution of rare genetic variants to human disease. Numerous statistical methods have been proposed to aggregate association signals across multiple rare variant sites in an effort to increase statistical power; however, the precise relation between the tests is often not well understood. We present a geometric representation for rare variant data in which rare allele counts in case and control samples are treated as vectors in Euclidean space. The geometric framework facilitates a rigorous classification of existing rare variant tests into two broad categories: tests for a difference in the lengths of the case and control vectors, and joint tests for a difference in either the lengths or angles of the two vectors. We demonstrate that genetic architecture of a trait, including the number and frequency of risk alleles, directly relates to the behavior of the length and joint tests. Hence, the geometric framework allows prediction of which tests will perform best under different disease models. Furthermore, the structure of the geometric framework immediately suggests additional classes and types of rare variant tests. We consider two general classes of tests which show robustness to noncausal and protective variants. The geometric framework introduces a novel and unique method to assess current rare variant methodology and provides guidelines for both applied and theoretical researchers.
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.