OSCA: A tool for omic-data-based complex trait analysis

Futao Zhang; Wenhan Chen; Zhihong Zhu; Qian Zhang; Marta F. Nabais; Ting Qi; Ian Deary; Naomi R Wray; Peter M. Visscher; Allan F. Mcrae; Jian Yang

doi:10.1186/s13059-019-1718-z

OSCA: A tool for omic-data-based complex trait analysis

Futao Zhang, Wenhan Chen, Zhihong Zhu, Qian Zhang, Marta F. Nabais, Ting Qi, Ian Deary, Naomi R Wray, Peter M. Visscher, Allan F. Mcrae, Jian Yang

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1-13
Journal	Genome Biology
Volume	20
Issue number	107
DOIs	https://doi.org/10.1186/s13059-019-1718-z
Publication status	Published - 28 May 2019

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10.1186/s13059-019-1718-zLicence: Creative Commons: Attribution (CC-BY)

ZhangEtalGB2019OSCAAToolForOmic-Data-basedAnalysisFinal published version, 1.54 MBLicence: Creative Commons: Attribution (CC-BY)

Disconnected Mind - Phase 4
Deary, I. (Principal Investigator), Bastin, M. (Co-investigator) & Wardlaw, J. (Co-investigator)
UK-based charities
1/04/19 → 31/03/23
Project: Research
The Disconnected Mind Phase 3
Cox, S. (Principal Investigator) & Deary, I. (Co-investigator)
1/04/16 → 31/03/23
Project: Research
RA2662 Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2.
Porteous, D. (Principal Investigator)
1/09/13 → 31/08/19
Project: Research

Cite this

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title = "OSCA: A tool for omic-data-based complex trait analysis",

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.",

author = "Futao Zhang and Wenhan Chen and Zhihong Zhu and Qian Zhang and Nabais, {Marta F.} and Ting Qi and Ian Deary and Wray, {Naomi R} and Visscher, {Peter M.} and Mcrae, {Allan F.} and Jian Yang",

year = "2019",

month = may,

day = "28",

doi = "10.1186/s13059-019-1718-z",

language = "English",

volume = "20",

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journal = "Genome Biology",

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T1 - OSCA

T2 - A tool for omic-data-based complex trait analysis

AU - Zhang, Futao

AU - Chen, Wenhan

AU - Zhu, Zhihong

AU - Zhang, Qian

AU - Nabais, Marta F.

AU - Qi, Ting

AU - Deary, Ian

AU - Wray, Naomi R

AU - Visscher, Peter M.

AU - Mcrae, Allan F.

AU - Yang, Jian

PY - 2019/5/28

Y1 - 2019/5/28

N2 - 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.

AB - 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.

U2 - 10.1186/s13059-019-1718-z

DO - 10.1186/s13059-019-1718-z

M3 - Article

SN - 1465-6906

VL - 20

SP - 1

EP - 13

JO - Genome Biology

JF - Genome Biology

IS - 107

ER -

OSCA: A tool for omic-data-based complex trait analysis

Abstract

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Projects

Disconnected Mind - Phase 4

The Disconnected Mind Phase 3

RA2662 Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2.

Cite this