Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods

Abstract Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, b...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gu, Fangyi [verfasserIn] Monsees, Genevieve Kraft, Peter

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2007

Schlagwörter:	Akaike Information Criterion Bayesian Information Criterion Small Akaike Information Criterion Disease Susceptibility Locus Sensitive Rule

Anmerkung:	© Gu et al; licensee BioMed Central Ltd. 2007. 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, 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-S25

Katalog-ID:	SPR028426185

Internformat


LEADER	01000caa a22002652 4500
001	SPR028426185
003	DE-627
005	20230519105354.0
007	cr uuu---uuuuu
008	201007s2007 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/1753-6561-1-S1-S25 \|2 doi
035			\|a (DE-627)SPR028426185
035			\|a (SPR)1753-6561-1-S1-S25-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	1		\|a Gu, Fangyi \|e verfasserin \|4 aut
245	1	0	\|a Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods
264		1	\|c 2007
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © Gu et al; licensee BioMed Central Ltd. 2007. 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 (
520			\|a Abstract Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development.
650		4	\|a Akaike Information Criterion \|7 (dpeaa)DE-He213
650		4	\|a Bayesian Information Criterion \|7 (dpeaa)DE-He213
650		4	\|a Small Akaike Information Criterion \|7 (dpeaa)DE-He213
650		4	\|a Disease Susceptibility Locus \|7 (dpeaa)DE-He213
650		4	\|a Sensitive Rule \|7 (dpeaa)DE-He213
700	1		\|a Monsees, Genevieve \|4 aut
700	1		\|a Kraft, Peter \|4 aut
773	0	8	\|i Enthalten in \|t BMC proceedings \|d London : BioMed Central, 2007 \|g 1(2007), Suppl 1 vom: 18. Dez. \|w (DE-627)559080840 \|w (DE-600)2411867-9 \|x 1753-6561 \|7 nnns
773	1	8	\|g volume:1 \|g year:2007 \|g number:Suppl 1 \|g day:18 \|g month:12
856	4	0	\|u https://dx.doi.org/10.1186/1753-6561-1-S1-S25 \|z lizenzpflichtig \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 1 \|j 2007 \|e Suppl 1 \|b 18 \|c 12

Indexfelder

author_variant	f g fg g m gm p k pk
matchkey_str	article:17536561:2007----::xasiecenfriesssetbltlcicroaigttsiaitrcinfeoyeaoprsnfieiodaibsdna
hierarchy_sort_str	2007
publishDate	2007
allfields	10.1186/1753-6561-1-S1-S25 doi (DE-627)SPR028426185 (SPR)1753-6561-1-S1-S25-e DE-627 ger DE-627 rakwb eng Gu, Fangyi verfasserin aut Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Gu et al; licensee BioMed Central Ltd. 2007. 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 Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. Akaike Information Criterion (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Small Akaike Information Criterion (dpeaa)DE-He213 Disease Susceptibility Locus (dpeaa)DE-He213 Sensitive Rule (dpeaa)DE-He213 Monsees, Genevieve aut Kraft, Peter 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-S25 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_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-S25 doi (DE-627)SPR028426185 (SPR)1753-6561-1-S1-S25-e DE-627 ger DE-627 rakwb eng Gu, Fangyi verfasserin aut Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Gu et al; licensee BioMed Central Ltd. 2007. 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 Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. Akaike Information Criterion (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Small Akaike Information Criterion (dpeaa)DE-He213 Disease Susceptibility Locus (dpeaa)DE-He213 Sensitive Rule (dpeaa)DE-He213 Monsees, Genevieve aut Kraft, Peter 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-S25 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_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-S25 doi (DE-627)SPR028426185 (SPR)1753-6561-1-S1-S25-e DE-627 ger DE-627 rakwb eng Gu, Fangyi verfasserin aut Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Gu et al; licensee BioMed Central Ltd. 2007. 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 Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. Akaike Information Criterion (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Small Akaike Information Criterion (dpeaa)DE-He213 Disease Susceptibility Locus (dpeaa)DE-He213 Sensitive Rule (dpeaa)DE-He213 Monsees, Genevieve aut Kraft, Peter 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-S25 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_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-S25 doi (DE-627)SPR028426185 (SPR)1753-6561-1-S1-S25-e DE-627 ger DE-627 rakwb eng Gu, Fangyi verfasserin aut Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Gu et al; licensee BioMed Central Ltd. 2007. 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 Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. Akaike Information Criterion (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Small Akaike Information Criterion (dpeaa)DE-He213 Disease Susceptibility Locus (dpeaa)DE-He213 Sensitive Rule (dpeaa)DE-He213 Monsees, Genevieve aut Kraft, Peter 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-S25 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_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-S25 doi (DE-627)SPR028426185 (SPR)1753-6561-1-S1-S25-e DE-627 ger DE-627 rakwb eng Gu, Fangyi verfasserin aut Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Gu et al; licensee BioMed Central Ltd. 2007. 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 Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. Akaike Information Criterion (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Small Akaike Information Criterion (dpeaa)DE-He213 Disease Susceptibility Locus (dpeaa)DE-He213 Sensitive Rule (dpeaa)DE-He213 Monsees, Genevieve aut Kraft, Peter 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-S25 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_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
language	English
source	Enthalten in BMC proceedings 1(2007), Suppl 1 vom: 18. Dez. volume:1 year:2007 number:Suppl 1 day:18 month:12
sourceStr	Enthalten in BMC proceedings 1(2007), Suppl 1 vom: 18. Dez. volume:1 year:2007 number:Suppl 1 day:18 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Akaike Information Criterion Bayesian Information Criterion Small Akaike Information Criterion Disease Susceptibility Locus Sensitive Rule
isfreeaccess_bool	false
container_title	BMC proceedings
authorswithroles_txt_mv	Gu, Fangyi @@aut@@ Monsees, Genevieve @@aut@@ Kraft, Peter @@aut@@
publishDateDaySort_date	2007-12-18T00:00:00Z
hierarchy_top_id	559080840
id	SPR028426185
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR028426185</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519105354.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2007 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/1753-6561-1-S1-S25</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR028426185</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)1753-6561-1-S1-S25-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gu, Fangyi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Gu et al; licensee BioMed Central Ltd. 2007. 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 (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Akaike Information Criterion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian Information Criterion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Small Akaike Information Criterion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disease Susceptibility Locus</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensitive Rule</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Monsees, Genevieve</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kraft, Peter</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC proceedings</subfield><subfield code="d">London : BioMed Central, 2007</subfield><subfield code="g">1(2007), Suppl 1 vom: 18. Dez.</subfield><subfield code="w">(DE-627)559080840</subfield><subfield code="w">(DE-600)2411867-9</subfield><subfield code="x">1753-6561</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:1</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:Suppl 1</subfield><subfield code="g">day:18</subfield><subfield code="g">month:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/1753-6561-1-S1-S25</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">1</subfield><subfield code="j">2007</subfield><subfield code="e">Suppl 1</subfield><subfield code="b">18</subfield><subfield code="c">12</subfield></datafield></record></collection>
author	Gu, Fangyi
spellingShingle	Gu, Fangyi misc Akaike Information Criterion misc Bayesian Information Criterion misc Small Akaike Information Criterion misc Disease Susceptibility Locus misc Sensitive Rule Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods
authorStr	Gu, Fangyi
ppnlink_with_tag_str_mv	@@773@@(DE-627)559080840
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1753-6561
topic_title	Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods Akaike Information Criterion (dpeaa)DE-He213 Bayesian Information Criterion (dpeaa)DE-He213 Small Akaike Information Criterion (dpeaa)DE-He213 Disease Susceptibility Locus (dpeaa)DE-He213 Sensitive Rule (dpeaa)DE-He213
topic	misc Akaike Information Criterion misc Bayesian Information Criterion misc Small Akaike Information Criterion misc Disease Susceptibility Locus misc Sensitive Rule
topic_unstemmed	misc Akaike Information Criterion misc Bayesian Information Criterion misc Small Akaike Information Criterion misc Disease Susceptibility Locus misc Sensitive Rule
topic_browse	misc Akaike Information Criterion misc Bayesian Information Criterion misc Small Akaike Information Criterion misc Disease Susceptibility Locus misc Sensitive Rule
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	BMC proceedings
hierarchy_parent_id	559080840
hierarchy_top_title	BMC proceedings
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)559080840 (DE-600)2411867-9
title	Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods
ctrlnum	(DE-627)SPR028426185 (SPR)1753-6561-1-S1-S25-e
title_full	Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods
author_sort	Gu, Fangyi
journal	BMC proceedings
journalStr	BMC proceedings
lang_code	eng
isOA_bool	false
recordtype	marc
publishDateSort	2007
contenttype_str_mv	txt
author_browse	Gu, Fangyi Monsees, Genevieve Kraft, Peter
container_volume	1
format_se	Elektronische Aufsätze
author-letter	Gu, Fangyi
doi_str_mv	10.1186/1753-6561-1-S1-S25
title_sort	exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and akaike and bayesian information criteria-based methods
title_auth	Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods
abstract	Abstract Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. © Gu et al; licensee BioMed Central Ltd. 2007. 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 Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. © Gu et al; licensee BioMed Central Ltd. 2007. 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 Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development. © Gu et al; licensee BioMed Central Ltd. 2007. 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 (
collection_details	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
container_issue	Suppl 1
title_short	Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods
url	https://dx.doi.org/10.1186/1753-6561-1-S1-S25
remote_bool	true
author2	Monsees, Genevieve Kraft, Peter
author2Str	Monsees, Genevieve Kraft, Peter
ppnlink	559080840
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1186/1753-6561-1-S1-S25
up_date	2024-07-03T19:25:03.298Z
_version_	1803587109045403648
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR028426185</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519105354.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2007 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/1753-6561-1-S1-S25</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR028426185</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)1753-6561-1-S1-S25-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Gu, Fangyi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Gu et al; licensee BioMed Central Ltd. 2007. 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 (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Akaike Information Criterion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian Information Criterion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Small Akaike Information Criterion</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Disease Susceptibility Locus</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensitive Rule</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Monsees, Genevieve</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kraft, Peter</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC proceedings</subfield><subfield code="d">London : BioMed Central, 2007</subfield><subfield code="g">1(2007), Suppl 1 vom: 18. Dez.</subfield><subfield code="w">(DE-627)559080840</subfield><subfield code="w">(DE-600)2411867-9</subfield><subfield code="x">1753-6561</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:1</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:Suppl 1</subfield><subfield code="g">day:18</subfield><subfield code="g">month:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/1753-6561-1-S1-S25</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">1</subfield><subfield code="j">2007</subfield><subfield code="e">Suppl 1</subfield><subfield code="b">18</subfield><subfield code="c">12</subfield></datafield></record></collection>
score	7.3996477

Nicht das Richtige dabei?

Schreiben Sie uns!

Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?