ReviewAmerican journal of epidemiology2018
Statistical Analysis of Multiple Phenotypes in Genetic Epidemiologic Studies: From Cross-Phenotype Associations to Pleiotropy.
Review in American journal of epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 24 citations in OpenAlex.
- Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits.Briefings in bioinformatics · 2024Article
- Multivariate genome-wide associations for immune traits in two maternal pig lines.BMC genomics · 2023Article
- Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood.Biometrical journal. Biometrische Zeitschrift · 2023Article
- Associating complex traits with genetic variants: polygenic risk scores, pleiotropy and endophenotypes.Genetica · 2022Article
- Discovery and Mediation Analysis of Cross-Phenotype Associations Between Asthma and Body Mass Index in 12q13.2.American journal of epidemiology · 2021Article
- Pleiotropy and Cross-Disorder Genetics Among Psychiatric Disorders.Biological psychiatry · 2021Review
- Genetic Pleiotropy of Bone-Related Phenotypes: Insights from Osteoporosis.Current osteoporosis reports · 2020Review
- How Well Can Multivariate and Univariate GWAS Distinguish Between True and Spurious Pleiotropy?Frontiers in genetics · 2020Article
- The E Is in the G: Gene-Environment-Trait Correlations and Findings From Genome-Wide Association Studies.Perspectives on psychological science : a journal of the Association for Psychological Science · 2020Article
- Assessing pleiotropy and mediation in genetic loci associated with chronic obstructive pulmonary disease.Genetic epidemiology · 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 at 1 institution in 1 country.
Funding
Abstract
In the context of genetics, pleiotropy refers to the phenomenon in which a single genetic locus affects more than 1 trait or disease. Genetic epidemiologic studies have identified loci associated with multiple phenotypes, and these cross-phenotype associations are often incorrectly interpreted as examples of pleiotropy. Pleiotropy is only one possible explanation for cross-phenotype associations. Cross-phenotype associations may also arise due to issues related to study design, confounder bias, or nongenetic causal links between the phenotypes under analysis. Therefore, it is necessary to dissect cross-phenotype associations carefully to uncover true pleiotropic loci. In this review, we describe statistical methods that can be used to identify robust statistical evidence of pleiotropy. First, we provide an overview of univariate and multivariate methods for discovery of cross-phenotype associations and highlight important considerations for choosing among available methods. Then, we describe how to dissect cross-phenotype associations by using mediation analysis. Pleiotropic loci provide insights into the mechanistic underpinnings of disease comorbidity, and they may serve as novel targets for interventions that simultaneously treat multiple diseases. Discerning between different types of cross-phenotype associations is necessary to realize the public health potential of pleiotropic loci.
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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.