Evidence map›Paper›PMID 33034635›Full record

ArticleGigaScience2020

Gene-set Enrichment with Mathematical Biology (GEMB).

Amy L Cochran, Kenneth J Nieser, Daniel B Forger, Sebastian Zöllner, Melvin G McInnis

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

Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.1field-weighted citation impact, top 54% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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

5 authors at 3 institutions in 1 country.

Amy L CochranDepartment of Math, University of Wisconsin-Madison, 480 Lincoln Drive, Madison, WI, 53706, USA.
Kenneth J NieserDepartment of Population Health Sciences, University of Wisconsin-Madison, 610 Walnut Street, Madison, WI, 53726, USA.
Daniel B ForgerDepartment of Mathematics, University of Michigan, 530 Church Street, Ann Arbor, MI, 48109, USA.
Sebastian ZöllnerDepartment of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
Melvin G McInnisDepartment of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
University of Michigan · USUniversity of Wisconsin–Madison · USWashtenaw Community College · US

Funding

Modeling mood course to detect markers for effective adaptive interventionsK01MH112876 · NIMH · UNIVERSITY OF WISCONSIN-MADISON · PI COCHRAN, AMY LOUISE · 2017 to 2020
$511k
NIMH NIH HHS K01 MH112876
6 · The paper itself

Abstract

backgroundGene-set analyses measure the association between a disease of interest and a "set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further-defining gene contributions based on biophysical properties-by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function.

resultsWe present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10-4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199).

conclusionsTogether, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.

Indexed as

Bipolar DisorderGenome-Wide Association StudyBiologyGenetic Predisposition to DiseaseHumansMultifactorial Inheritancebipolar disordercalcium signalinggene ontologygene-set analysisgenetic enrichmentmathematical biology

Identifiers

PMID33034635
PMCPMC7546080
OpenAlexW3092122470

What OpenQuestion holds

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LicenceCC BY
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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.