Evidence map›Paper›PMID 27061195›Full record

ArticleGenetic epidemiology2016

Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations.

Jingya Qiu, Jason H Moore, Christian Darabos

Open access · hybridAbstract read
In one paragraph

Article in Genetic epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.0field-weighted citation impact, top 9% of its field
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

9 citing papers in PubMed, 18 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Jingya QiuGeisel School of Medicine, Dartmouth College, Lebanon, New Hampshire, United States of America.
Jason H MooreInstitute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Christian DarabosInstitute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
University of Pennsylvania · USDartmouth Psychiatric Research Center · US

Funding

Ultrastructural analysis of axonal Kv2-mediated endoplasmic reticulum/plasma membrane (ER/PM) junctionsP20GM103506 · NIGMS · DARTMOUTH COLLEGE · PI STEVEN FIERING · 2012 to 2026
$60.1M
Translational Research Support CoreP30ES013508 · NIEHS · UNIVERSITY OF PENNSYLVANIA · PI Rebecca A Simmons · 2006 to 2026
$35.3M
Quantitative Biology Research InstituteP20GM103534 · NIGMS · DARTMOUTH COLLEGE · PI AMOS, CHRISTOPHER I. · 2012 to 2016
$8.6M
Bioinformatics Strategies for Genome-Wide Association StudiesR01LM010098 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H., WILLIAMS, SCOTT MATTHEW · 2009 to 2023
$5.1M
Bioinformatics Strategies: Biodefense Vaccine ResearchR01AI059694 · NIAID · DARTMOUTH COLLEGE · PI MOORE, JASON H. · 2004 to 2012
$4.5M
Machine Learning Prediction of Cancer SusceptibilityR01LM009012 · NLM · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2006 to 2014
$3.3M
Bioinformatics Approaches to Visual Disease GeneticsR01EY022300 · NEI · UNIVERSITY OF PENNSYLVANIA · PI MOORE, JASON H. · 2012 to 2014
$947k
NEI NIH HHS R01 EY022300NIAID NIH HHS AI59694NIAID NIH HHS R01 AI059694NIEHS NIH HHS P30 ES013508NIGMS NIH HHS GM103506NIGMS NIH HHS GM103534NIGMS NIH HHS P20 GM103506NIGMS NIH HHS P20 GM103534NLM NIH HHS LM009012NLM NIH HHS LM010098NLM NIH HHS R01 LM009012NLM NIH HHS R01 LM010098
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have led to the discovery of over 200 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM). Additionally, East Asians develop T2DM at a higher rate, younger age, and lower body mass index than their European ancestry counterparts. The reason behind this occurrence remains elusive. With comprehensive searches through the National Human Genome Research Institute (NHGRI) GWAS catalog literature, we compiled a database of 2,800 ancestry-specific SNPs associated with T2DM and 70 other related traits. Manual data extraction was necessary because the GWAS catalog reports statistics such as odds ratio and P-value, but does not consistently include ancestry information. Currently, many statistics are derived by combining initial and replication samples from study populations of mixed ancestry. Analysis of all-inclusive data can be misleading, as not all SNPs are transferable across diverse populations. We used ancestry data to construct ancestry-specific human phenotype networks (HPN) centered on T2DM. Quantitative and visual analysis of network models reveal the genetic disparities between ancestry groups. Of the 27 phenotypes in the East Asian HPN, six phenotypes were unique to the network, revealing the underlying ancestry-specific nature of some SNPs associated with T2DM. We studied the relationship between T2DM and five phenotypes unique to the East Asian HPN to generate new interaction hypotheses in a clinical context. The genetic differences found in our ancestry-specific HPNs suggest different pathways are involved in the pathogenesis of T2DM among different populations. Our study underlines the importance of ancestry in the development of T2DM and its implications in pharmocogenetics and personalized medicine.

Indexed as

Genetic Predisposition to DiseaseAsia, EasternAsian PeopleDiabetes Mellitus, Type 2FemaleGenome-Wide Association StudyHumansModels, GeneticMyocardial InfarctionOdds RatioOvarian NeoplasmsPhenotypePolymorphism, Single NucleotideSchizophreniacomplex diseaseEast Asian populationsGWAShuman phenotype networktype 2 diabetes

Identifiers

PMID27061195
PMCPMC5071667
OpenAlexW2340549150

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.