Evidence map›Paper›PMID 33931127›Full record

ArticleGenome biology2021

MARS: leveraging allelic heterogeneity to increase power of association testing.

Farhad Hormozdiari, Junghyun Jung, Eleazar Eskin, Jong Wha J Joo

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

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 30% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

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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

4 authors at 3 institutions in 2 countries.

Farhad Hormozdiari *Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA.
Junghyun Jung *Department of Life Science, Dongguk University-Seoul, Seoul, 04620, South Korea.
Eleazar EskinDepartment of Computer Science, University of California, Los Angeles, Los Angeles, 90095, CA, USA.
Jong Wha J JooDepartment of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea. jwjoo1204@gmail.com.
Dongguk University · KRBroad Institute · USUniversity of California, Los Angeles · US

Funding

The pursuit of genetic causal mechanismsR56HG010812 · NHGRI · STANFORD UNIVERSITY · PI SABATTI, CHIARA · 2021 to 2022
$875k
NHGRI NIH HHS R56 HG010812
6 · The paper itself

Abstract

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Indexed as

AllelesGenetic HeterogeneityModels, GeneticModels, StatisticalAlgorithmsGenetic Association StudiesGenome-Wide Association StudyHumansAssociation studiesCausal variantsSet-based association analysis

Identifiers

PMID33931127
PMCPMC8086090
OpenAlexW3159591964

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.