Evidence map›Paper›PMID 30255768›Full record

ArticleBMC genetics2018

Indirect effect inference and application to GAW20 data.

Liming Li, Chan Wang, Tianyuan Lu, Shili Lin, Yue-Qing Hu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Liming LiState Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
Chan WangState Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
Tianyuan LuState Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, China.
Shili LinDepartment of Statistics, The Ohio State University, 1958 Neil Avenue, 404 Cokins Hall, Columbus, OH, 43210, USA.
Yue-Qing HuState Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai, 200438, China. yuehu@fudan.edu.cn.

Funding

Genetic and Environmental Determinants of TriglyceridesU01HL072524 · NHLBI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI ARNETT, DONNA K · 2002 to 2008
$11.9M
GENETIC ANALYSIS OF COMMON DISEASES: AN EVALUATIONR01GM031575 · NIGMS · UNIVERSITY OF TEXAS RIO GRANDE VALLEY · PI ALMASY, LAURA A. · 1985 to 2016
$7.8M
Genomewide Association Study of Lipid Response to Fenofibrate and Dietary FatR01HL091357 · NHLBI · UNIVERSITY OF KENTUCKY · PI ARNETT, DONNA K · 2008 to 2018
$5.8M
Epigenetic Determinants of Lipid Response to Dietary Fat and FenofibrateR01HL104135 · NHLBI · UNIVERSITY OF KENTUCKY · PI ARNETT, DONNA K · 2010 to 2015
$5.5M
NHLBI NIH HHS R01 HL091357NHLBI NIH HHS R01 HL104135NHLBI NIH HHS U01 HL072524NIGMS NIH HHS R01 GM031575
6 · The paper itself

Abstract

backgroundAssociation studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed.

resultsWe propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied.

conclusionsThe proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses.

Indexed as

Genome-Wide Association StudyDNA MethylationGenomicsHumansHypertriglyceridemiaHypoglycemic AgentsPolymorphism, Single NucleotideHypoglycemic AgentsDifferentially methylated regionsDNA methylationEpigenetics

Identifiers

PMID30255768
PMCPMC6157197

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Registered trials

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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.