ArticleFrontiers in genetics2014
An alternative hypothesis testing strategy for secondary phenotype data in case-control genetic association studies.
Article in Frontiers in genetics, 2014. 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.
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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.
- A novel association test for multiple secondary phenotypes from a case-control GWAS.Genetic epidemiology · 2017Trial
- A genome-wide association study identifies risk loci for spirometric measures among smokers of European and African ancestry.BMC genetics · 2015Trial
- Genetic Advances in Chronic Obstructive Pulmonary Disease. Insights from COPDGene.American journal of respiratory and critical care medicine · 2019Review
- Common and Rare Variants Genetic Association Analysis of Cigarettes per Day Among Ever-Smokers in Chronic Obstructive Pulmonary Disease Cases and Controls.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2019Article
- A general approach to testing for pleiotropy with rare and common variants.Genetic epidemiology · 2017Article
- Article
- Novel approaches to the analysis of family data in genetic epidemiology.Frontiers in genetics · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Motivated by the challenges associated with accounting for the ascertainment when analyzing secondary phenotypes that are correlated with case-control status, Lin and Zeng have proposed a method that properly reflects the case-control sampling (Lin and Zeng, 2009). The Lin and Zeng method has the advantage of accurately estimating effect sizes for secondary phenotypes that are normally distributed or dichotomous. This method can be computationally intensive in practice under the null hypothesis when the likelihood surface that needs to be maximized can be relatively flat. We propose an extension of the Lin and Zeng method for hypothesis testing that uses proportional odds logistic regression to circumvent these computational issues. Through simulation studies, we compare the power and type-1 error rate of our method to standard approaches and Lin and Zeng's approach.
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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.