ArticleHuman heredity2011
Relative efficiency of trend tests with misspecified genetic models in stratified analyses of case-control or cohort data.
Article in Human heredity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.
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Who cites it
2 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Genome-wide association meta-analysis of circulating odd-numbered chain saturated fatty acids: Results from the CHARGE Consortium.PloS one · 2018Pooled it
- Genetic loci associated with circulating levels of very long-chain saturated fatty acids.Journal of lipid research · 2015Pooled it
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
BACKGROUND/
aimsGenetic single-nucleotide polymorphism (SNP) data are often analyzed using trend tests that rely on a specific assumption about the way that disease frequency varies across genotypes, but the validity of this assumption is not typically known. We explore the relative efficiency of trend tests in which the assumed model may or may not correspond to the true genetic model.
methodsWe derive formulae for the asymptotic relative efficiencies (AREs) comparing tests that assume different genetic models. We consider both unstratified and stratified tests, using both case-control and cohort data. We illustrate these formulae using realistic parameters and compare the calculated AREs to simulated relative efficiencies in finite samples.
resultsThe AREs are identical for unstratified tests using case-control and cohort data, but differ for stratified tests. Loss of efficiency can be substantial, given specific combinations of high-risk allele frequencies, disease frequencies, and assumed versus actual genetic models. Given reasonably large sample sizes, asymptotic calculations align well with finite sample simulations of relative efficiency.
conclusionsARE is a useful estimate of the relative efficiency of statistics using different underlying genetic models. ARE calculations reveal that additive gene doses, which are most commonly used, lead to large losses in power in some settings.
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