Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects

Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presenc...
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Autor*in:	Kim, Yoonhee [verfasserIn] Wojciechowski, Robert Sung, Heejong Mathias, Rasika A Wang, Li Klein, Alison P Lenroot, Rhoshel K Malley, James Bailey-Wilson, Joan E

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Mean Square Error Random Forest Environmental Risk Factor Genetic Analysis Workshop Random Forest Analysis

Anmerkung:	© Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC proceedings - London : BioMed Central, 2007, 3(2009), Suppl 7 vom: 15. Dez.
Übergeordnetes Werk:	volume:3 ; year:2009 ; number:Suppl 7 ; day:15 ; month:12

Links:	Volltext

DOI / URN:	10.1186/1753-6561-3-S7-S64

Katalog-ID:	SPR028430883

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allfields	10.1186/1753-6561-3-S7-S64 doi (DE-627)SPR028430883 (SPR)1753-6561-3-S7-S64-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. Mean Square Error (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Environmental Risk Factor (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Wojciechowski, Robert aut Sung, Heejong aut Mathias, Rasika A aut Wang, Li aut Klein, Alison P aut Lenroot, Rhoshel K aut Malley, James aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 7 vom: 15. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 7 day:15 month:12 https://dx.doi.org/10.1186/1753-6561-3-S7-S64 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 7 15 12
spelling	10.1186/1753-6561-3-S7-S64 doi (DE-627)SPR028430883 (SPR)1753-6561-3-S7-S64-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. Mean Square Error (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Environmental Risk Factor (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Wojciechowski, Robert aut Sung, Heejong aut Mathias, Rasika A aut Wang, Li aut Klein, Alison P aut Lenroot, Rhoshel K aut Malley, James aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 7 vom: 15. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 7 day:15 month:12 https://dx.doi.org/10.1186/1753-6561-3-S7-S64 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 7 15 12
allfields_unstemmed	10.1186/1753-6561-3-S7-S64 doi (DE-627)SPR028430883 (SPR)1753-6561-3-S7-S64-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. Mean Square Error (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Environmental Risk Factor (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Wojciechowski, Robert aut Sung, Heejong aut Mathias, Rasika A aut Wang, Li aut Klein, Alison P aut Lenroot, Rhoshel K aut Malley, James aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 7 vom: 15. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 7 day:15 month:12 https://dx.doi.org/10.1186/1753-6561-3-S7-S64 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 7 15 12
allfieldsGer	10.1186/1753-6561-3-S7-S64 doi (DE-627)SPR028430883 (SPR)1753-6561-3-S7-S64-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. Mean Square Error (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Environmental Risk Factor (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Wojciechowski, Robert aut Sung, Heejong aut Mathias, Rasika A aut Wang, Li aut Klein, Alison P aut Lenroot, Rhoshel K aut Malley, James aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 7 vom: 15. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 7 day:15 month:12 https://dx.doi.org/10.1186/1753-6561-3-S7-S64 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 7 15 12
allfieldsSound	10.1186/1753-6561-3-S7-S64 doi (DE-627)SPR028430883 (SPR)1753-6561-3-S7-S64-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. Mean Square Error (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Environmental Risk Factor (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Wojciechowski, Robert aut Sung, Heejong aut Mathias, Rasika A aut Wang, Li aut Klein, Alison P aut Lenroot, Rhoshel K aut Malley, James aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 3(2009), Suppl 7 vom: 15. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:3 year:2009 number:Suppl 7 day:15 month:12 https://dx.doi.org/10.1186/1753-6561-3-S7-S64 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2009 Suppl 7 15 12
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source	Enthalten in BMC proceedings 3(2009), Suppl 7 vom: 15. Dez. volume:3 year:2009 number:Suppl 7 day:15 month:12
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author	Kim, Yoonhee
spellingShingle	Kim, Yoonhee misc Mean Square Error misc Random Forest misc Environmental Risk Factor misc Genetic Analysis Workshop misc Random Forest Analysis Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects
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topic_title	Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects Mean Square Error (dpeaa)DE-He213 Random Forest (dpeaa)DE-He213 Environmental Risk Factor (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213
topic	misc Mean Square Error misc Random Forest misc Environmental Risk Factor misc Genetic Analysis Workshop misc Random Forest Analysis
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title	Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects
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title_full	Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects
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title_sort	evaluation of random forests performance for genome-wide association studies in the presence of interaction effects
title_auth	Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects
abstract	Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Abstract Random forests (RF) is one of a broad class of machine learning methods that are able to deal with large-scale data without model specification, which makes it an attractive method for genome-wide association studies (GWAS). The performance of RF and other association methods in the presence of interactions was evaluated using the simulated data from Genetic Analysis Workshop 16 Problem 3, with knowledge of the major causative markers, risk factors, and their interactions in the simulated traits. There was good power to detect the environmental risk factors using RF, trend tests, or regression analyses but the power to detect the effects of the causal markers was poor for all methods. The causal marker that had an interactive effect with smoking did show moderate evidence of association in the RF and regression analyses, suggesting that RF may perform well at detecting such interactions in larger, more highly powered datasets. © Kim et al; licensee BioMed Central Ltd. 2009. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
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title_short	Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects
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Evaluation of random forests performance for genome-wide association studies in the presence of interaction effects

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