Exploring epistasis in candidate genes for rheumatoid arthritis

Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW)...
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Autor*in:	Ritchie, Marylyn D [verfasserIn] Bartlett, Jacquelaine Bush, William S Edwards, Todd L Motsinger, Alison A Torstenson, Eric S

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2007

Schlagwörter:	Rheumatoid Arthritis Normalize Mutual Information Multifactor Dimensionality Reduction Unrelated Control Grammatical Evolution

Anmerkung:	© Ritchie et al; licensee BioMed Central Ltd. 2007

Übergeordnetes Werk:	Enthalten in: BMC proceedings - London : BioMed Central, 2007, 1(2007), Suppl 1 vom: 18. Dez.
Übergeordnetes Werk:	volume:1 ; year:2007 ; number:Suppl 1 ; day:18 ; month:12

Links:	Volltext

DOI / URN:	10.1186/1753-6561-1-S1-S70

Katalog-ID:	SPR028426681

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520			\|a Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data.
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allfields	10.1186/1753-6561-1-S1-S70 doi (DE-627)SPR028426681 (SPR)1753-6561-1-S1-S70-e DE-627 ger DE-627 rakwb eng Ritchie, Marylyn D verfasserin aut Exploring epistasis in candidate genes for rheumatoid arthritis 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ritchie et al; licensee BioMed Central Ltd. 2007 Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. Rheumatoid Arthritis (dpeaa)DE-He213 Normalize Mutual Information (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Unrelated Control (dpeaa)DE-He213 Grammatical Evolution (dpeaa)DE-He213 Bartlett, Jacquelaine aut Bush, William S aut Edwards, Todd L aut Motsinger, Alison A aut Torstenson, Eric S aut Enthalten in BMC proceedings London : BioMed Central, 2007 1(2007), Suppl 1 vom: 18. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:1 year:2007 number:Suppl 1 day:18 month:12 https://dx.doi.org/10.1186/1753-6561-1-S1-S70 kostenfrei 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_2014 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 1 2007 Suppl 1 18 12
spelling	10.1186/1753-6561-1-S1-S70 doi (DE-627)SPR028426681 (SPR)1753-6561-1-S1-S70-e DE-627 ger DE-627 rakwb eng Ritchie, Marylyn D verfasserin aut Exploring epistasis in candidate genes for rheumatoid arthritis 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ritchie et al; licensee BioMed Central Ltd. 2007 Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. Rheumatoid Arthritis (dpeaa)DE-He213 Normalize Mutual Information (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Unrelated Control (dpeaa)DE-He213 Grammatical Evolution (dpeaa)DE-He213 Bartlett, Jacquelaine aut Bush, William S aut Edwards, Todd L aut Motsinger, Alison A aut Torstenson, Eric S aut Enthalten in BMC proceedings London : BioMed Central, 2007 1(2007), Suppl 1 vom: 18. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:1 year:2007 number:Suppl 1 day:18 month:12 https://dx.doi.org/10.1186/1753-6561-1-S1-S70 kostenfrei 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_2014 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 1 2007 Suppl 1 18 12
allfields_unstemmed	10.1186/1753-6561-1-S1-S70 doi (DE-627)SPR028426681 (SPR)1753-6561-1-S1-S70-e DE-627 ger DE-627 rakwb eng Ritchie, Marylyn D verfasserin aut Exploring epistasis in candidate genes for rheumatoid arthritis 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ritchie et al; licensee BioMed Central Ltd. 2007 Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. Rheumatoid Arthritis (dpeaa)DE-He213 Normalize Mutual Information (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Unrelated Control (dpeaa)DE-He213 Grammatical Evolution (dpeaa)DE-He213 Bartlett, Jacquelaine aut Bush, William S aut Edwards, Todd L aut Motsinger, Alison A aut Torstenson, Eric S aut Enthalten in BMC proceedings London : BioMed Central, 2007 1(2007), Suppl 1 vom: 18. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:1 year:2007 number:Suppl 1 day:18 month:12 https://dx.doi.org/10.1186/1753-6561-1-S1-S70 kostenfrei 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_2014 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 1 2007 Suppl 1 18 12
allfieldsGer	10.1186/1753-6561-1-S1-S70 doi (DE-627)SPR028426681 (SPR)1753-6561-1-S1-S70-e DE-627 ger DE-627 rakwb eng Ritchie, Marylyn D verfasserin aut Exploring epistasis in candidate genes for rheumatoid arthritis 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ritchie et al; licensee BioMed Central Ltd. 2007 Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. Rheumatoid Arthritis (dpeaa)DE-He213 Normalize Mutual Information (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Unrelated Control (dpeaa)DE-He213 Grammatical Evolution (dpeaa)DE-He213 Bartlett, Jacquelaine aut Bush, William S aut Edwards, Todd L aut Motsinger, Alison A aut Torstenson, Eric S aut Enthalten in BMC proceedings London : BioMed Central, 2007 1(2007), Suppl 1 vom: 18. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:1 year:2007 number:Suppl 1 day:18 month:12 https://dx.doi.org/10.1186/1753-6561-1-S1-S70 kostenfrei 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_2014 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 1 2007 Suppl 1 18 12
allfieldsSound	10.1186/1753-6561-1-S1-S70 doi (DE-627)SPR028426681 (SPR)1753-6561-1-S1-S70-e DE-627 ger DE-627 rakwb eng Ritchie, Marylyn D verfasserin aut Exploring epistasis in candidate genes for rheumatoid arthritis 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Ritchie et al; licensee BioMed Central Ltd. 2007 Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. Rheumatoid Arthritis (dpeaa)DE-He213 Normalize Mutual Information (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Unrelated Control (dpeaa)DE-He213 Grammatical Evolution (dpeaa)DE-He213 Bartlett, Jacquelaine aut Bush, William S aut Edwards, Todd L aut Motsinger, Alison A aut Torstenson, Eric S aut Enthalten in BMC proceedings London : BioMed Central, 2007 1(2007), Suppl 1 vom: 18. Dez. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:1 year:2007 number:Suppl 1 day:18 month:12 https://dx.doi.org/10.1186/1753-6561-1-S1-S70 kostenfrei 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_2014 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 1 2007 Suppl 1 18 12
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author	Ritchie, Marylyn D
spellingShingle	Ritchie, Marylyn D misc Rheumatoid Arthritis misc Normalize Mutual Information misc Multifactor Dimensionality Reduction misc Unrelated Control misc Grammatical Evolution Exploring epistasis in candidate genes for rheumatoid arthritis
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topic_title	Exploring epistasis in candidate genes for rheumatoid arthritis Rheumatoid Arthritis (dpeaa)DE-He213 Normalize Mutual Information (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Unrelated Control (dpeaa)DE-He213 Grammatical Evolution (dpeaa)DE-He213
topic	misc Rheumatoid Arthritis misc Normalize Mutual Information misc Multifactor Dimensionality Reduction misc Unrelated Control misc Grammatical Evolution
topic_unstemmed	misc Rheumatoid Arthritis misc Normalize Mutual Information misc Multifactor Dimensionality Reduction misc Unrelated Control misc Grammatical Evolution
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title	Exploring epistasis in candidate genes for rheumatoid arthritis
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title_full	Exploring epistasis in candidate genes for rheumatoid arthritis
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title_sort	exploring epistasis in candidate genes for rheumatoid arthritis
title_auth	Exploring epistasis in candidate genes for rheumatoid arthritis
abstract	Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. © Ritchie et al; licensee BioMed Central Ltd. 2007
abstractGer	Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. © Ritchie et al; licensee BioMed Central Ltd. 2007
abstract_unstemmed	Abstract The identification of susceptibility genes for common, chronic disease presents great challenges. The development of novel statistical and computational methodologies to help identify these genes is an area of great necessity. Much research is ongoing and the Genetic Analysis Workshop (GAW) is a venue for the dissemination and comparison of many of these methods. GAW15 included real data sets to look for disease susceptibility genes for rheumatoid arthritis (RA). RA is a complex, chronic inflammatory disease with several replicated disease genes, but much of the genetic variation in the phenotype remains unexplained. We applied two computational methods, namely multifactor dimensionality reduction (MDR) and grammatical evolution neural networks (GENN), to three data sets from GAW15. While these analytic methods were applied with the intention of detecting of multilocus models of association, both methods identified a strong single locus effect of a single-nucleotide polymorphism (SNP) in PTPN22 that is significantly associated with RA. This SNP has previously been associated with RA in several other published studies. These results demonstrate that both MDR and GENN are capable of identifying a single-locus main effect, in addition to multilocus models of association. This is the first published comparison of the two methods. Because GENN employs an evolutionary computation search strategy in comparison to the exhaustive search strategy of MDR, it is encouraging that the two methods produced similar results. This comparison should be extended in future studies with both simulated and real data. © Ritchie et al; licensee BioMed Central Ltd. 2007
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title_short	Exploring epistasis in candidate genes for rheumatoid arthritis
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author2	Bartlett, Jacquelaine Bush, William S Edwards, Todd L Motsinger, Alison A Torstenson, Eric S
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up_date	2024-07-03T19:25:13.818Z
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