Evidence map›Paper›PMID 28495956›Full record

ArticleGenetics2017

Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis.

Hongsheng Gui, Johnny S Kwan, Pak C Sham, Stacey S Cherny, Miaoxin Li

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hongsheng GuiCenter for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China.ORCID 0000-0002-0460-7446
Johnny S KwanDepartment of Psychiatry, University of Hong Kong, Hong Kong SAR, China.
Pak C ShamCenter for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China.
Stacey S ChernyCenter for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China cherny@hku.hk limx54@yahoo.com.
Miaoxin LiCenter for Genomic Sciences, University of Hong Kong, Hong Kong SAR, China cherny@hku.hk limx54@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Genetic PleiotropyGenome, HumanDatasets as TopicGenetic LinkageGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPolymorphism, Single Nucleotidecomplex diseasesgene-basedgenetic sharingGWASpleiotropy

Identifiers

PMID28495956
PMCPMC5500153

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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.