ArticleGenetics2017
Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis.
Article in Genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Large-Scale Analyses of GWAS Identify Five Key Pleiotropic Genes Involved in Complex Diseases.Genes · 2026Article
- Childhood household dysfunction and psychiatric, criminal, and social outcomes in emerging adulthood. A cousin comparison study.International journal of epidemiology · 2025Article
- Shared Genetic Architecture of Obstructive Sleep Apnea and Neck Pain: A Large-Scale Genome-Wide Association Study.Journal of pain research · 2025Article
- MicroRNA sequences modulating inflammation and lipid accumulation in macrophage "foam" cells: Implications for atherosclerosis.World journal of cardiology · 2020Review
- Mutations in sphingolipid metabolism genes are associated with ADHD.Translational psychiatry · 2020Article
- Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma.BMC cancer · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evidence from genome-wide association studies (GWAS) suggest that pleiotropic effects on human complex phenotypes are very common. Recently, an atlas of genetic correlations among complex phenotypes has broadened our understanding of human diseases and traits. Here, we examine genetic overlap, from a gene-centric perspective, among the same 24 phenotypes previously investigated for genetic correlations. After adopting the multilevel pipeline (freely available at http://grass.cgs.hku.hk/limx/kgg/), which includes intragenic single nucleotide polymorphisms (SNPs), genes, and gene-sets, to estimate genetic similarities across phenotypes, a large amount of sharing of several biologically related phenotypes was confirmed. In addition, significant genetic overlaps were also found among phenotype pairs that were previously unidentified by SNP-level approaches. All these pairs with new genetic links are supported by earlier epidemiological evidence, although only a few of them have pleiotropic genes in the GWAS Catalog. Hence, our gene and gene-set analyses are able to provide new insights into cross-phenotype connections. The investigation on genetic sharing at three different levels presents a complementary picture of how common DNA sequence variations contribute to disease comorbidities and trait manifestations.
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Registered trials
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