ArticleGenetics2016
Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases.
Article in Genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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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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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 63 citations in OpenAlex.
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- Early complement genes are associated with visual system degeneration in multiple sclerosis.Brain : a journal of neurology · 2019Pooled it
- Impact of polymorphisms within genes involved in regulating DNA methylation in patients with metastatic colorectal cancer enrolled in three independent, randomised, open-label clinical trials: a meta-analysis from TRIBE, MAVERICC and FIRE-3.European journal of cancer (Oxford, England : 1990) · 2019Pooled it
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- Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries.American journal of human genetics · 2022Article
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- Genetic underpinnings of affective temperaments: a pilot GWAS investigation identifies a new genome-wide significant SNP for anxious temperament in ADGRB3 gene.Translational psychiatry · 2021Article
- Boosting GWAS using biological networks: A study on susceptibility to familial breast cancer.PLoS computational biology · 2021Article
- Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila.PLoS genetics · 2021Article
- Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities.PLoS computational biology · 2021Article
- Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.PeerJ. Computer science · 2021Article
- DOMINO: a network-based active module identification algorithm with reduced rate of false calls.Molecular systems biology · 2021Article
- Network and pathway expansion of genetic disease associations identifies successful drug targets.Scientific reports · 2020Article
- Learning gene networks underlying clinical phenotypes using SNP perturbation.PLoS computational biology · 2020Article
- Estimation of non-null SNP effect size distributions enables the detection of enriched genes underlying complex traits.PLoS genetics · 2020Article
- Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics.Genetics · 2020Article
- Footprints of natural selection at the mannose-6-phosphate isomerase locus in barnacles.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also useful when generating hypotheses for experimental validation. However, commonly used methods for generating GWA gene scores are computationally inefficient, biased by gene length, imprecise, or have low true positive rate (TPR) at low false positive rates (FPR), leading to erroneous hypotheses for functional validation. Here we introduce a new method, PEGASUS, for analytically calculating gene scores. PEGASUS produces gene scores with as much as 10 orders of magnitude higher numerical precision than competing methods. In simulation, PEGASUS outperforms existing methods, achieving up to 30% higher TPR when the FPR is fixed at 1%. We use gene scores from PEGASUS as input to HotNet2 to identify networks of interacting genes associated with multiple complex diseases and traits; this is the first application of HotNet2 to common variation. In ulcerative colitis and waist-hip ratio, we discover networks that include genes previously associated with these phenotypes, as well as novel candidate genes. In contrast, existing methods fail to identify these networks. We also identify networks for attention-deficit/hyperactivity disorder, in which GWA studies have yet to identify any significant SNPs.
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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.