The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping

Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about th...
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Autor*in:	Swartz, Michael D [verfasserIn] Shete, Sanjay

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2007

Schlagwörter:	Prior Probability Dependence Structure Conditional Logistic Regression Bayesian Variable Selection Bayesian Prior

Anmerkung:	© Swartz and Shete; 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-S113

Katalog-ID:	SPR028427157

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spelling	10.1186/1753-6561-1-S1-S113 doi (DE-627)SPR028427157 (SPR)1753-6561-1-S1-S113-e DE-627 ger DE-627 rakwb eng Swartz, Michael D verfasserin aut The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Swartz and Shete; licensee BioMed Central Ltd. 2007 Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about the disease outside the specific data analyzed. It is well known that the larger the sample size, the more the Bayesian method with uninformative priors can be approximated by its Frequentist analogue. However, what is not known is how much impact the priors have on a Bayesian method when analyzing a null region of the chromosome. Here, we look at the impact of various prior values on stochastic search gene suggestion (SSGS) when analyzing a region of simulated chromosome 6 known to be unassociated with the simulated disease. SSGS is a recently developed Bayesian variable selection method tailored to investigate disease-gene association using case-parent triads. Our findings indicate that the prior probability values do affect false positives, and this study suggests values to calibrate the prior. Also, the sensitivity of the results to the prior probability values depends on two factors: the linkage disequilibrium between the marker loci examined, and whether this dependence is included in the model. In order to assess the null distribution we used the simulated data with the "answers" known. Prior Probability (dpeaa)DE-He213 Dependence Structure (dpeaa)DE-He213 Conditional Logistic Regression (dpeaa)DE-He213 Bayesian Variable Selection (dpeaa)DE-He213 Bayesian Prior (dpeaa)DE-He213 Shete, Sanjay 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-S113 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-S113 doi (DE-627)SPR028427157 (SPR)1753-6561-1-S1-S113-e DE-627 ger DE-627 rakwb eng Swartz, Michael D verfasserin aut The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Swartz and Shete; licensee BioMed Central Ltd. 2007 Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about the disease outside the specific data analyzed. It is well known that the larger the sample size, the more the Bayesian method with uninformative priors can be approximated by its Frequentist analogue. However, what is not known is how much impact the priors have on a Bayesian method when analyzing a null region of the chromosome. Here, we look at the impact of various prior values on stochastic search gene suggestion (SSGS) when analyzing a region of simulated chromosome 6 known to be unassociated with the simulated disease. SSGS is a recently developed Bayesian variable selection method tailored to investigate disease-gene association using case-parent triads. Our findings indicate that the prior probability values do affect false positives, and this study suggests values to calibrate the prior. Also, the sensitivity of the results to the prior probability values depends on two factors: the linkage disequilibrium between the marker loci examined, and whether this dependence is included in the model. In order to assess the null distribution we used the simulated data with the "answers" known. Prior Probability (dpeaa)DE-He213 Dependence Structure (dpeaa)DE-He213 Conditional Logistic Regression (dpeaa)DE-He213 Bayesian Variable Selection (dpeaa)DE-He213 Bayesian Prior (dpeaa)DE-He213 Shete, Sanjay 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-S113 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-S113 doi (DE-627)SPR028427157 (SPR)1753-6561-1-S1-S113-e DE-627 ger DE-627 rakwb eng Swartz, Michael D verfasserin aut The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Swartz and Shete; licensee BioMed Central Ltd. 2007 Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about the disease outside the specific data analyzed. It is well known that the larger the sample size, the more the Bayesian method with uninformative priors can be approximated by its Frequentist analogue. However, what is not known is how much impact the priors have on a Bayesian method when analyzing a null region of the chromosome. Here, we look at the impact of various prior values on stochastic search gene suggestion (SSGS) when analyzing a region of simulated chromosome 6 known to be unassociated with the simulated disease. SSGS is a recently developed Bayesian variable selection method tailored to investigate disease-gene association using case-parent triads. Our findings indicate that the prior probability values do affect false positives, and this study suggests values to calibrate the prior. Also, the sensitivity of the results to the prior probability values depends on two factors: the linkage disequilibrium between the marker loci examined, and whether this dependence is included in the model. In order to assess the null distribution we used the simulated data with the "answers" known. Prior Probability (dpeaa)DE-He213 Dependence Structure (dpeaa)DE-He213 Conditional Logistic Regression (dpeaa)DE-He213 Bayesian Variable Selection (dpeaa)DE-He213 Bayesian Prior (dpeaa)DE-He213 Shete, Sanjay 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-S113 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-S113 doi (DE-627)SPR028427157 (SPR)1753-6561-1-S1-S113-e DE-627 ger DE-627 rakwb eng Swartz, Michael D verfasserin aut The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Swartz and Shete; licensee BioMed Central Ltd. 2007 Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about the disease outside the specific data analyzed. It is well known that the larger the sample size, the more the Bayesian method with uninformative priors can be approximated by its Frequentist analogue. However, what is not known is how much impact the priors have on a Bayesian method when analyzing a null region of the chromosome. Here, we look at the impact of various prior values on stochastic search gene suggestion (SSGS) when analyzing a region of simulated chromosome 6 known to be unassociated with the simulated disease. SSGS is a recently developed Bayesian variable selection method tailored to investigate disease-gene association using case-parent triads. Our findings indicate that the prior probability values do affect false positives, and this study suggests values to calibrate the prior. Also, the sensitivity of the results to the prior probability values depends on two factors: the linkage disequilibrium between the marker loci examined, and whether this dependence is included in the model. In order to assess the null distribution we used the simulated data with the "answers" known. Prior Probability (dpeaa)DE-He213 Dependence Structure (dpeaa)DE-He213 Conditional Logistic Regression (dpeaa)DE-He213 Bayesian Variable Selection (dpeaa)DE-He213 Bayesian Prior (dpeaa)DE-He213 Shete, Sanjay 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-S113 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
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
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topic_facet	Prior Probability Dependence Structure Conditional Logistic Regression Bayesian Variable Selection Bayesian Prior
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container_title	BMC proceedings
authorswithroles_txt_mv	Swartz, Michael D @@aut@@ Shete, Sanjay @@aut@@
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author	Swartz, Michael D
spellingShingle	Swartz, Michael D misc Prior Probability misc Dependence Structure misc Conditional Logistic Regression misc Bayesian Variable Selection misc Bayesian Prior The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping
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topic_title	The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping Prior Probability (dpeaa)DE-He213 Dependence Structure (dpeaa)DE-He213 Conditional Logistic Regression (dpeaa)DE-He213 Bayesian Variable Selection (dpeaa)DE-He213 Bayesian Prior (dpeaa)DE-He213
topic	misc Prior Probability misc Dependence Structure misc Conditional Logistic Regression misc Bayesian Variable Selection misc Bayesian Prior
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title	The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping
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title_full	The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping
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title_sort	null distribution of stochastic search gene suggestion: a bayesian approach to gene mapping
title_auth	The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping
abstract	Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about the disease outside the specific data analyzed. It is well known that the larger the sample size, the more the Bayesian method with uninformative priors can be approximated by its Frequentist analogue. However, what is not known is how much impact the priors have on a Bayesian method when analyzing a null region of the chromosome. Here, we look at the impact of various prior values on stochastic search gene suggestion (SSGS) when analyzing a region of simulated chromosome 6 known to be unassociated with the simulated disease. SSGS is a recently developed Bayesian variable selection method tailored to investigate disease-gene association using case-parent triads. Our findings indicate that the prior probability values do affect false positives, and this study suggests values to calibrate the prior. Also, the sensitivity of the results to the prior probability values depends on two factors: the linkage disequilibrium between the marker loci examined, and whether this dependence is included in the model. In order to assess the null distribution we used the simulated data with the "answers" known. © Swartz and Shete; licensee BioMed Central Ltd. 2007
abstractGer	Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about the disease outside the specific data analyzed. It is well known that the larger the sample size, the more the Bayesian method with uninformative priors can be approximated by its Frequentist analogue. However, what is not known is how much impact the priors have on a Bayesian method when analyzing a null region of the chromosome. Here, we look at the impact of various prior values on stochastic search gene suggestion (SSGS) when analyzing a region of simulated chromosome 6 known to be unassociated with the simulated disease. SSGS is a recently developed Bayesian variable selection method tailored to investigate disease-gene association using case-parent triads. Our findings indicate that the prior probability values do affect false positives, and this study suggests values to calibrate the prior. Also, the sensitivity of the results to the prior probability values depends on two factors: the linkage disequilibrium between the marker loci examined, and whether this dependence is included in the model. In order to assess the null distribution we used the simulated data with the "answers" known. © Swartz and Shete; licensee BioMed Central Ltd. 2007
abstract_unstemmed	Abstract Bayesian methods continue to permeate genetic epidemiology investigations of genetic markers associated with or linked to causal genes for complex diseases. The attraction of these methods is an ability to capitalize on Bayesian priors to model additional complexity and information about the disease outside the specific data analyzed. It is well known that the larger the sample size, the more the Bayesian method with uninformative priors can be approximated by its Frequentist analogue. However, what is not known is how much impact the priors have on a Bayesian method when analyzing a null region of the chromosome. Here, we look at the impact of various prior values on stochastic search gene suggestion (SSGS) when analyzing a region of simulated chromosome 6 known to be unassociated with the simulated disease. SSGS is a recently developed Bayesian variable selection method tailored to investigate disease-gene association using case-parent triads. Our findings indicate that the prior probability values do affect false positives, and this study suggests values to calibrate the prior. Also, the sensitivity of the results to the prior probability values depends on two factors: the linkage disequilibrium between the marker loci examined, and whether this dependence is included in the model. In order to assess the null distribution we used the simulated data with the "answers" known. © Swartz and Shete; licensee BioMed Central Ltd. 2007
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title_short	The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping
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The null distribution of stochastic search gene suggestion: a Bayesian approach to gene mapping

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