Evidence map›Paper›PMID 31138268›Full record

ArticleGenome biology2019

OSCA: a tool for omic-data-based complex trait analysis.

Futao Zhang, Wenhan Chen, Zhihong Zhu, Qian Zhang, Marta F Nabais, Ting Qi, Ian J Deary, Naomi R Wray, Peter M Visscher, Allan F McRae and 1 more

Abstract readEvaluation Study
In one paragraph

Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
101citing papers in PubMed, 7 pooled it
–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

101 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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41 more citing papers are in PubMed but not listed here.

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

11 authors.

Futao ZhangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Wenhan ChenInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Zhihong ZhuInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Qian ZhangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Marta F NabaisInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Ting QiInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Ian J DearyCentre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
Naomi R WrayInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Peter M VisscherInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Allan F McRaeInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia.
Jian YangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, 4072, Australia. jian.yang.qt@gmail.com.ORCID 0000-0003-2001-2474

Funding

Biotechnology and Biological Sciences Research Council BB/F019394/1Medical Research Council MR/K026992/1Wellcome Trust
6 · The paper itself

Abstract

The rapid increase of omic data has greatly facilitated the investigation of associations between omic profiles such as DNA methylation (DNAm) and complex traits in large cohorts. Here, we propose a mixed-linear-model-based method called MOMENT that tests for association between a DNAm probe and trait with all other distal probes fitted in multiple random-effect components to account for unobserved confounders. We demonstrate by simulations that MOMENT shows a lower false positive rate and more robustness than existing methods. MOMENT has been implemented in a versatile software package called OSCA together with a number of other implementations for omic-data-based analyses.

Indexed as

Genetic TechniquesSoftwareAgedComputer SimulationDNA MethylationHumansLinear ModelsMetabolomicsPhenotype

Identifiers

PMID31138268
PMCPMC6537380

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.