Gene analysis for longitudinal family data using random-effects models

Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a ge...
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Autor*in:	Houwing-Duistermaat, Jeanine J [verfasserIn] Helmer, Quinta Balliu, Bruna van den Akker, Erik Tsonaka, Roula Uh, Hae-Won

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Linear Mixed Model Rare Variant Functional Locus Gene Summary Variant Call Format

Anmerkung:	© Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: BMC proceedings - London : BioMed Central, 2007, 8(2014), Suppl 1 vom: 17. Juni
Übergeordnetes Werk:	volume:8 ; year:2014 ; number:Suppl 1 ; day:17 ; month:06

Links:	Volltext

DOI / URN:	10.1186/1753-6561-8-S1-S88

Katalog-ID:	SPR028454626

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520			\|a Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $).
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allfields	10.1186/1753-6561-8-S1-S88 doi (DE-627)SPR028454626 (SPR)1753-6561-8-S1-S88-e DE-627 ger DE-627 rakwb eng Houwing-Duistermaat, Jeanine J verfasserin aut Gene analysis for longitudinal family data using random-effects models 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014 Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). Linear Mixed Model (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Functional Locus (dpeaa)DE-He213 Gene Summary (dpeaa)DE-He213 Variant Call Format (dpeaa)DE-He213 Helmer, Quinta aut Balliu, Bruna aut van den Akker, Erik aut Tsonaka, Roula aut Uh, Hae-Won aut Enthalten in BMC proceedings London : BioMed Central, 2007 8(2014), Suppl 1 vom: 17. Juni (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:8 year:2014 number:Suppl 1 day:17 month:06 https://dx.doi.org/10.1186/1753-6561-8-S1-S88 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 1 17 06
spelling	10.1186/1753-6561-8-S1-S88 doi (DE-627)SPR028454626 (SPR)1753-6561-8-S1-S88-e DE-627 ger DE-627 rakwb eng Houwing-Duistermaat, Jeanine J verfasserin aut Gene analysis for longitudinal family data using random-effects models 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014 Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). Linear Mixed Model (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Functional Locus (dpeaa)DE-He213 Gene Summary (dpeaa)DE-He213 Variant Call Format (dpeaa)DE-He213 Helmer, Quinta aut Balliu, Bruna aut van den Akker, Erik aut Tsonaka, Roula aut Uh, Hae-Won aut Enthalten in BMC proceedings London : BioMed Central, 2007 8(2014), Suppl 1 vom: 17. Juni (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:8 year:2014 number:Suppl 1 day:17 month:06 https://dx.doi.org/10.1186/1753-6561-8-S1-S88 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 1 17 06
allfields_unstemmed	10.1186/1753-6561-8-S1-S88 doi (DE-627)SPR028454626 (SPR)1753-6561-8-S1-S88-e DE-627 ger DE-627 rakwb eng Houwing-Duistermaat, Jeanine J verfasserin aut Gene analysis for longitudinal family data using random-effects models 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014 Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). Linear Mixed Model (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Functional Locus (dpeaa)DE-He213 Gene Summary (dpeaa)DE-He213 Variant Call Format (dpeaa)DE-He213 Helmer, Quinta aut Balliu, Bruna aut van den Akker, Erik aut Tsonaka, Roula aut Uh, Hae-Won aut Enthalten in BMC proceedings London : BioMed Central, 2007 8(2014), Suppl 1 vom: 17. Juni (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:8 year:2014 number:Suppl 1 day:17 month:06 https://dx.doi.org/10.1186/1753-6561-8-S1-S88 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 1 17 06
allfieldsGer	10.1186/1753-6561-8-S1-S88 doi (DE-627)SPR028454626 (SPR)1753-6561-8-S1-S88-e DE-627 ger DE-627 rakwb eng Houwing-Duistermaat, Jeanine J verfasserin aut Gene analysis for longitudinal family data using random-effects models 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014 Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). Linear Mixed Model (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Functional Locus (dpeaa)DE-He213 Gene Summary (dpeaa)DE-He213 Variant Call Format (dpeaa)DE-He213 Helmer, Quinta aut Balliu, Bruna aut van den Akker, Erik aut Tsonaka, Roula aut Uh, Hae-Won aut Enthalten in BMC proceedings London : BioMed Central, 2007 8(2014), Suppl 1 vom: 17. Juni (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:8 year:2014 number:Suppl 1 day:17 month:06 https://dx.doi.org/10.1186/1753-6561-8-S1-S88 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 1 17 06
allfieldsSound	10.1186/1753-6561-8-S1-S88 doi (DE-627)SPR028454626 (SPR)1753-6561-8-S1-S88-e DE-627 ger DE-627 rakwb eng Houwing-Duistermaat, Jeanine J verfasserin aut Gene analysis for longitudinal family data using random-effects models 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014 Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). Linear Mixed Model (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Functional Locus (dpeaa)DE-He213 Gene Summary (dpeaa)DE-He213 Variant Call Format (dpeaa)DE-He213 Helmer, Quinta aut Balliu, Bruna aut van den Akker, Erik aut Tsonaka, Roula aut Uh, Hae-Won aut Enthalten in BMC proceedings London : BioMed Central, 2007 8(2014), Suppl 1 vom: 17. Juni (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:8 year:2014 number:Suppl 1 day:17 month:06 https://dx.doi.org/10.1186/1753-6561-8-S1-S88 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_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 1 17 06
language	English
source	Enthalten in BMC proceedings 8(2014), Suppl 1 vom: 17. Juni volume:8 year:2014 number:Suppl 1 day:17 month:06
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authorswithroles_txt_mv	Houwing-Duistermaat, Jeanine J @@aut@@ Helmer, Quinta @@aut@@ Balliu, Bruna @@aut@@ van den Akker, Erik @@aut@@ Tsonaka, Roula @@aut@@ Uh, Hae-Won @@aut@@
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author	Houwing-Duistermaat, Jeanine J
spellingShingle	Houwing-Duistermaat, Jeanine J misc Linear Mixed Model misc Rare Variant misc Functional Locus misc Gene Summary misc Variant Call Format Gene analysis for longitudinal family data using random-effects models
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title	Gene analysis for longitudinal family data using random-effects models
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title_full	Gene analysis for longitudinal family data using random-effects models
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title_sort	gene analysis for longitudinal family data using random-effects models
title_auth	Gene analysis for longitudinal family data using random-effects models
abstract	Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014
abstractGer	Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	Abstract We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × $ 10^{-12} $). © Houwing-Duistermaat et al.; licensee BioMed Central Ltd. 2014
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title_short	Gene analysis for longitudinal family data using random-effects models
url	https://dx.doi.org/10.1186/1753-6561-8-S1-S88
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author2	Helmer, Quinta Balliu, Bruna van den Akker, Erik Tsonaka, Roula Uh, Hae-Won
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Gene analysis for longitudinal family data using random-effects models

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