Evidence map›Paper›PMID 28479870›Full record

ArticleCurrent genomics2016

A Genome-wide Association Analysis in Four Populations Reveals Strong Genetic Heterogeneity For Birth Weight.

Tiane Luo, Xu Liu, Yuehua Cui

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 94% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Association of theInternational journal of molecular sciences · 2022
    Article
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 1 institution in 1 country.

Tiane LuoDivision of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi, 030001, China.
Xu LiuDepartment of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA.
Yuehua CuiDivision of Health Statistics, School of Public Health, Shanxi Medical University, Shanxi, 030001, China.
Michigan State University · US

Funding

A Center for GEI Association StudiesU01HG004424 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI GABRIEL, STACEY · 2007 to 2010
$21.4M
Genome Wide Association Coordinating CenterU01HG004446 · NHGRI · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2007 to 2011
$6.6M
GWA Mapping: Maternal Metabolism-Birth Weight InteractionsU01HG004415 · NHGRI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LOWE, WILLIAM L · 2007 to 2009
$1.1M
NHGRI NIH HHS U01 HG004415NHGRI NIH HHS U01 HG004424NHGRI NIH HHS U01 HG004446
6 · The paper itself

Abstract

Low or high birth weight is one of the main causes for neonatal morbidity and mortality. They are also associated with adulthood chronic illness. Birth weight is a complex trait which is affected by baby's genes, maternal environments as well as the complex interactions between them. To understand the genetic basis of birth weight, we reanalyzed a genome-wide association study data set which consists of four populations, namely Thai, Afro-Caribbean, European, and Hispanic population with regular linear models. In addition to fit the data with parametric linear models, we fitted the data with a nonparametric varying-coefficient model to identify variants that are nonlinearly modulated by mother's condition to affect birth weight. For this purpose, we used baby's cord glucose level as the mother's environmental variable. At the 10

Indexed as

Birth weightFour populationGene-environment interactionGenetic associationNonlinear modulationVarying-coefficient model

Identifiers

PMID28479870
PMCPMC5320544
OpenAlexW2507343590

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.