Evidence map›Paper›PMID 27496912›Full record

ArticleBiostatistics (Oxford, England)2017

Simultaneous detection and estimation of trait associations with genomic phenotypes.

Jean Morrison, Noah Simon, Daniela Witten

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biostatistics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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.

Jean MorrisonDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA jeanm5@uw.edu.
Noah SimonDepartment of Biostatistics, University of Washington, Seattle, WA 98195, USA jeanm5@uw.edu.
Daniela WittenDepartment of Statistics, University of Washington, Seattle, WA, 98195, USA and Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
University of Washington · US

Funding

High-dimensional unsupervised learning, with applications to genomicsDP5OD009145 · OD · UNIVERSITY OF WASHINGTON · PI WITTEN, DANIELA · 2011 to 2015
$1.8M
Data-Driven Statistical Learning with Applications to GenomicsDP5OD019820 · OD · UNIVERSITY OF WASHINGTON · PI SIMON, NOAH · 2014 to 2018
$1.7M
Penalized likelihood methods for estimation and testing with genomic dataF31HG008572 · NHGRI · UNIVERSITY OF WASHINGTON · PI MORRISON, JEAN V. · 2016 to 2016
$20k
NHGRI NIH HHS F31 HG008572NIH HHS DP5 OD009145NIH HHS DP5 OD019820
6 · The paper itself

Abstract

Genomic phenotypes, such as DNA methylation and chromatin accessibility, can be used to characterize the transcriptional and regulatory activity of DNA within a cell. Recent technological advances have made it possible to measure such phenotypes very densely. This density often results in spatial structure, in the sense that measurements at nearby sites are very similar. In this article, we consider the task of comparing genomic phenotypes across experimental conditions, cell types, or disease subgroups. We propose a new method, Joint Adaptive Differential Estimation (JADE), which leverages the spatial structure inherent to genomic phenotypes. JADE simultaneously estimates smooth underlying group average genomic phenotype profiles and detects regions in which the average profile differs between groups. We evaluate JADE's performance in several biologically plausible simulation settings. We also consider an application to the detection of regions with differential methylation between mature skeletal muscle cells, myotubes, and myoblasts.

Indexed as

Models, GeneticModels, StatisticalPhenotypeDNA MethylationGenomeHumansMuscle Fibers, SkeletalMyoblasts, SkeletalBioinformaticsComputer intensive methodsFunctional dataStatistical genetics

Identifiers

PMID27496912
PMCPMC6082590
OpenAlexW2486388385

What OpenQuestion holds

Textmetadata
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.