ArticleGenetic epidemiology2009
Analysis of multiple phenotypes.
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 9 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.
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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
9 citing papers in PubMed, 14 citations in OpenAlex.
- Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies using multi-layer network.Bioinformatics (Oxford, England) · 2023Article
- The Genomic Architecture of Hidradenitis Suppurativa-A Systematic Review.Frontiers in genetics · 2022Review
- A Blueprint for Identifying Phenotypes and Drug Targets in Complex Disorders with Empirical Dynamics.Patterns (New York, N.Y.) · 2020Article
- Article
- Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19.BMC genetics · 2016Article
- An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.Human heredity · 2016Article
- Biobanking across the phenome - at the center of chronic disease research.BMC public health · 2013Article
- A common functional promoter variant links CNR1 gene expression to HDL cholesterol level.Nature communications · 2013Article
- Impact of phenotype definition on genome-wide association signals: empirical evaluation in human immunodeficiency virus type 1 infection.American journal of epidemiology · 2011Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
The complex etiology of common diseases like cardiovascular disease, diabetes, hypertension, and rheumatoid arthritis has led investigators to focus on the genetics of correlated phenotypes and risk factors. Joint analysis of multiple disease-related phenotypes may reveal genes of pleiotropic effect and increase analytical power, but at the cost of increased analytical and computational complexity. All three data sets provided for analysis at the Genetic Analysis Workshop 16 offered multiple quantitative measures of phenotypes related to underlying disease processes as well as discrete measures of affection status. Participants in Group 6 addressed the challenges and possibilities of association analysis of these data sets on multiple levels, including phenotype definition and data reduction, multivariate approaches to gene discovery, analysis of causality and data structure, and development of predictive models. These approaches included combinations of continuous and discrete phenotypes, use of repeated measures in longitudinal data, and models that included multiple phenotypic measures and multiple single-nucleotide polymorphism variants. Most research teams regarded the use of multiple related phenotypes as a tool for increasing analytical power, as well as for clarifying the underlying biology of complex diseases.
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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.