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Methodology
Fentaw Abegaz
Rijksuniversiteit Groningen Center for Language and Cognition
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6294-4360
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
In genome-wide association studies the extent and impact of confounding due population structure has been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one being based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies is much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework.
Methods
In order to identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on model-based multifactor dimensionality reduction approach for structured populations namely: MBMDR-PC, MBMDR-PG and MBMDR-GC.
Results
Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power.
Conclusion
We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.
Keywords
Epistasis, Population Structure, Confounding, GWAS, GWAIS, MB-MDR, Gene-Gene Interaction, Population Stratification, Principal Components
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Peer Review Timeline
CURRENT STATUS: Published
View the published article at BioData Mining.
No community comments so far
Editorial decision: Accept
On 06 Feb, 2021
Review #1 received
Received 31 Jan, 2021
Reviewer #2 agreed
On 17 Jan, 2021
Review #2 received
Received 17 Jan, 2021
Reviewers invited
Invitations sent on 14 Jan, 2021
Reviewer #1 agreed
On 14 Jan, 2021
Editor assigned
On 13 Jan, 2021
Submission checks complete
On 13 Jan, 2021
Editor invited
On 13 Jan, 2021
Version 1
Posted 02 Jul, 2020
No comments provided
Editorial decision: Major revision
On 05 Nov, 2020
Review #2 received
Received 31 Oct, 2020
Reviewer #2 agreed
On 13 Oct, 2020
Review #1 received
Received 10 Aug, 2020
Reviewer #1 agreed
On 17 Jul, 2020
Reviewers invited
Invitations sent on 14 Jul, 2020
Editor assigned
On 01 Jul, 2020
Submission checks complete
On 30 Jun, 2020
Editor invited
On 30 Jun, 2020
First submitted
On 29 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
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A new preprint design is coming!
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See the published version of this preprint at BioData Mining.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Methodology
Fentaw Abegaz
Rijksuniversiteit Groningen Center for Language and Cognition
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6294-4360
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BioData Mining.
No community comments so far
Editorial decision: Accept
On 06 Feb, 2021
Review #1 received
Received 31 Jan, 2021
Reviewer #2 agreed
On 17 Jan, 2021
Review #2 received
Received 17 Jan, 2021
Reviewers invited
Invitations sent on 14 Jan, 2021
Reviewer #1 agreed
On 14 Jan, 2021
Editor assigned
On 13 Jan, 2021
Submission checks complete
On 13 Jan, 2021
Editor invited
On 13 Jan, 2021
Version 1
Posted 02 Jul, 2020
No comments provided
Editorial decision: Major revision
On 05 Nov, 2020
Review #2 received
Received 31 Oct, 2020
Reviewer #2 agreed
On 13 Oct, 2020
Review #1 received
Received 10 Aug, 2020
Reviewer #1 agreed
On 17 Jul, 2020
Reviewers invited
Invitations sent on 14 Jul, 2020
Editor assigned
On 01 Jul, 2020
Submission checks complete
On 30 Jun, 2020
Editor invited
On 30 Jun, 2020
First submitted
On 29 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BioData Mining.
Background
In genome-wide association studies the extent and impact of confounding due population structure has been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one being based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies is much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework.
Methods
In order to identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on model-based multifactor dimensionality reduction approach for structured populations namely: MBMDR-PC, MBMDR-PG and MBMDR-GC.
Results
Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power.
Conclusion
We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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