Evidence map›Paper›PMID 30255777›Full record

ArticleBMC genetics2018

Application of novel and existing methods to identify genes with evidence of epigenetic association: results from GAW20.

Angga M Fuady, Samantha Lent, Chloé Sarnowski, Nathan L Tintle

Abstract read
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

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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

4 authors.

Angga M FuadyMedical Statistics, Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, 2333, Leiden, ZC, Netherlands.
Samantha LentDepartment of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA, 02118, USA.
Chloé SarnowskiDepartment of Biostatistics, Boston University School of Public Health, 801 Massachusetts Avenue, Boston, MA, 02118, USA.
Nathan L TintleDepartment of Mathematics and Statistics, Dordt College, Sioux Center, IA, 51250, USA. Nathan.Tintle@dordt.edu.

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
Epigenetic Determinants of Lipid Response to Dietary Fat and FenofibrateR01HL104135 · NHLBI · UNIVERSITY OF KENTUCKY · PI ARNETT, DONNA K · 2010 to 2015
$5.5M
Interdisciplinary Training for BiostatisticiansT32GM074905 · NIGMS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI WHITE, LAURA FORSBERG · 2005 to 2019
$3.0M
Analyzing the behavior and interpreting the results of gene based tests of rare variant associationR15HG006915 · NHGRI · DORDT COLLEGE · PI TINTLE, NATHAN L · 2012 to 2020
$1.3M
NHGRI NIH HHS R15 HG006915NHLBI NIH HHS R01 HL104135NHLBI NIH HHS U01 HL072524NIGMS NIH HHS R01 GM031575NIGMS NIH HHS T32 GM074905
6 · The paper itself

Abstract

backgroundThe rise in popularity and accessibility of DNA methylation data to evaluate epigenetic associations with disease has led to numerous methodological questions. As part of GAW20, our working group of 8 research groups focused on gene searching methods.

resultsAlthough the methods were varied, we identified 3 main themes within our group. First, many groups tackled the question of how best to use pedigree information in downstream analyses, finding that (a) the use of kinship matrices is common practice, (b) ascertainment corrections may be necessary, and (c) pedigree information may be useful for identifying parent-of-origin effects. Second, many groups also considered multimarker versus single-marker tests. Multimarker tests had modestly improved power versus single-marker methods on simulated data, and on real data identified additional associations that were not identified with single-marker methods, including identification of a gene with a strong biological interpretation. Finally, some of the groups explored methods to combine single-nucleotide polymorphism (SNP) and DNA methylation into a single association analysis.

conclusionsA causal inference method showed promise at discovering new mechanisms of SNP activity; gene-based methods of summarizing SNP and DNA methylation data also showed promise. Even though numerous questions still remain in the analysis of DNA methylation data, our discussions at GAW20 suggest some emerging best practices.

Indexed as

Epigenesis, GeneticGenome-Wide Association StudyDNA MethylationHumansHypertriglyceridemiaHypoglycemic AgentsPolymorphism, Single NucleotideHypoglycemic AgentsAscertainmentDNA methylationEpigeneticsFamily dataGAW20Multimarker testsSingle nucleotide polymorphism

Identifiers

PMID30255777
PMCPMC6157126

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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.