Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Dominic Russ; John A Williams; Victor Roth Cardoso; Laura Bravo-Merodio; Samantha C Pendleton; Furqan Aziz; Animesh Acharjee; Georgios V Gkoutos

doi:10.1371/journal.pone.0263390

Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Dominic Russ, John A Williams, Victor Roth Cardoso, Laura Bravo-Merodio, Samantha C Pendleton, Furqan Aziz, Animesh Acharjee, Georgios V Gkoutos

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Abstract

BACKGROUND: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.

METHODS: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.

RESULTS: For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.

Original language	English
Article number	e0263390
Number of pages	19
Journal	PLoS ONE
Volume	17
Issue number	2
DOIs	https://doi.org/10.1371/journal.pone.0263390
Publication status	Published - 18 Feb 2022

Bibliographical note

Funding Information:
The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2022 Russ et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

Algorithms
Alleles
Atrial Fibrillation/genetics
Data Mining/methods
Dystrophin-Associated Proteins/genetics
Epistasis, Genetic
Gene Frequency
Genetic Loci
Genome-Wide Association Study/methods
Genotype
Humans
Linear Models
Microfilament Proteins/genetics
Models, Genetic
Multifactor Dimensionality Reduction
Nerve Tissue Proteins/genetics
Neuropeptides/genetics
Penetrance
Polymorphism, Single Nucleotide
ROC Curve

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10.1371/journal.pone.0263390Licence: Creative Commons: Attribution (CC BY)

RussD2022EvaluaFinal published version, 2.02 MBLicence: Creative Commons: Attribution (CC BY)

H2020_RIA_NANOCOMMONS_CO-ORDINATOR
Valsami-Jones, E., Lynch, I. & Gkoutos, G.
European Commission
1/01/18 → 30/06/22
Project: EU

Cite this

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title = "Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models",

abstract = "BACKGROUND: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.METHODS: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.RESULTS: For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.",

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author = "Dominic Russ and Williams, {John A} and Cardoso, {Victor Roth} and Laura Bravo-Merodio and Pendleton, {Samantha C} and Furqan Aziz and Animesh Acharjee and Gkoutos, {Georgios V}",

note = "Funding Information: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 2022 Russ et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

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T1 - Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

AU - Russ, Dominic

AU - Williams, John A

AU - Cardoso, Victor Roth

AU - Bravo-Merodio, Laura

AU - Pendleton, Samantha C

AU - Aziz, Furqan

AU - Acharjee, Animesh

AU - Gkoutos, Georgios V

N1 - Funding Information: The authors acknowledge support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC, Nanocommons H2020-EU (731032) and the NIHR Birmingham Biomedical Research Centre and the MRC Heath Data Research UK (HDRUK/CFC/01), an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2022 Russ et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2022/2/18

Y1 - 2022/2/18

N2 - BACKGROUND: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.METHODS: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.RESULTS: For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.

AB - BACKGROUND: Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics.METHODS: A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation.RESULTS: For pure, two locus interactions, PLINK's implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e - 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e - 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e - 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e - 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.

KW - Algorithms

KW - Alleles

KW - Atrial Fibrillation/genetics

KW - Data Mining/methods

KW - Dystrophin-Associated Proteins/genetics

KW - Epistasis, Genetic

KW - Gene Frequency

KW - Genetic Loci

KW - Genome-Wide Association Study/methods

KW - Genotype

KW - Humans

KW - Linear Models

KW - Microfilament Proteins/genetics

KW - Models, Genetic

KW - Multifactor Dimensionality Reduction

KW - Nerve Tissue Proteins/genetics

KW - Neuropeptides/genetics

KW - Penetrance

KW - Polymorphism, Single Nucleotide

KW - ROC Curve

U2 - 10.1371/journal.pone.0263390

DO - 10.1371/journal.pone.0263390

M3 - Article

C2 - 35180244

SN - 1932-6203

VL - 17

JO - PLoS ONE

JF - PLoS ONE

IS - 2

M1 - e0263390

ER -

Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Abstract

Bibliographical note

Keywords

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H2020_RIA_NANOCOMMONS_CO-ORDINATOR

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