Comparing baseline and longitudinal measures in association studies

Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of addit...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Shuai [verfasserIn] Gao, Wei Ngwa, Julius Allard, Catherine Liu, Ching-Ti Cupples, L Adrienne

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Diastolic Blood Pressure Genetic Analysis Workshop Baseline Approach Longitudinal Method Gene CACNA2D3

Anmerkung:	© Wang 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-S84

Katalog-ID:	SPR028454588

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520			\|a Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values.
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allfields	10.1186/1753-6561-8-S1-S84 doi (DE-627)SPR028454588 (SPR)1753-6561-8-S1-S84-e DE-627 ger DE-627 rakwb eng Wang, Shuai verfasserin aut Comparing baseline and longitudinal measures in association studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wang et al.; licensee BioMed Central Ltd. 2014 Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. Diastolic Blood Pressure (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Baseline Approach (dpeaa)DE-He213 Longitudinal Method (dpeaa)DE-He213 Gene CACNA2D3 (dpeaa)DE-He213 Gao, Wei aut Ngwa, Julius aut Allard, Catherine aut Liu, Ching-Ti aut Cupples, L Adrienne 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-S84 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-S84 doi (DE-627)SPR028454588 (SPR)1753-6561-8-S1-S84-e DE-627 ger DE-627 rakwb eng Wang, Shuai verfasserin aut Comparing baseline and longitudinal measures in association studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wang et al.; licensee BioMed Central Ltd. 2014 Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. Diastolic Blood Pressure (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Baseline Approach (dpeaa)DE-He213 Longitudinal Method (dpeaa)DE-He213 Gene CACNA2D3 (dpeaa)DE-He213 Gao, Wei aut Ngwa, Julius aut Allard, Catherine aut Liu, Ching-Ti aut Cupples, L Adrienne 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-S84 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-S84 doi (DE-627)SPR028454588 (SPR)1753-6561-8-S1-S84-e DE-627 ger DE-627 rakwb eng Wang, Shuai verfasserin aut Comparing baseline and longitudinal measures in association studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wang et al.; licensee BioMed Central Ltd. 2014 Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. Diastolic Blood Pressure (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Baseline Approach (dpeaa)DE-He213 Longitudinal Method (dpeaa)DE-He213 Gene CACNA2D3 (dpeaa)DE-He213 Gao, Wei aut Ngwa, Julius aut Allard, Catherine aut Liu, Ching-Ti aut Cupples, L Adrienne 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-S84 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-S84 doi (DE-627)SPR028454588 (SPR)1753-6561-8-S1-S84-e DE-627 ger DE-627 rakwb eng Wang, Shuai verfasserin aut Comparing baseline and longitudinal measures in association studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wang et al.; licensee BioMed Central Ltd. 2014 Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. Diastolic Blood Pressure (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Baseline Approach (dpeaa)DE-He213 Longitudinal Method (dpeaa)DE-He213 Gene CACNA2D3 (dpeaa)DE-He213 Gao, Wei aut Ngwa, Julius aut Allard, Catherine aut Liu, Ching-Ti aut Cupples, L Adrienne 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-S84 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-S84 doi (DE-627)SPR028454588 (SPR)1753-6561-8-S1-S84-e DE-627 ger DE-627 rakwb eng Wang, Shuai verfasserin aut Comparing baseline and longitudinal measures in association studies 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wang et al.; licensee BioMed Central Ltd. 2014 Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. Diastolic Blood Pressure (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Baseline Approach (dpeaa)DE-He213 Longitudinal Method (dpeaa)DE-He213 Gene CACNA2D3 (dpeaa)DE-He213 Gao, Wei aut Ngwa, Julius aut Allard, Catherine aut Liu, Ching-Ti aut Cupples, L Adrienne 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-S84 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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topic_facet	Diastolic Blood Pressure Genetic Analysis Workshop Baseline Approach Longitudinal Method Gene CACNA2D3
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authorswithroles_txt_mv	Wang, Shuai @@aut@@ Gao, Wei @@aut@@ Ngwa, Julius @@aut@@ Allard, Catherine @@aut@@ Liu, Ching-Ti @@aut@@ Cupples, L Adrienne @@aut@@
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author	Wang, Shuai
spellingShingle	Wang, Shuai misc Diastolic Blood Pressure misc Genetic Analysis Workshop misc Baseline Approach misc Longitudinal Method misc Gene CACNA2D3 Comparing baseline and longitudinal measures in association studies
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topic_title	Comparing baseline and longitudinal measures in association studies Diastolic Blood Pressure (dpeaa)DE-He213 Genetic Analysis Workshop (dpeaa)DE-He213 Baseline Approach (dpeaa)DE-He213 Longitudinal Method (dpeaa)DE-He213 Gene CACNA2D3 (dpeaa)DE-He213
topic	misc Diastolic Blood Pressure misc Genetic Analysis Workshop misc Baseline Approach misc Longitudinal Method misc Gene CACNA2D3
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title	Comparing baseline and longitudinal measures in association studies
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title_full	Comparing baseline and longitudinal measures in association studies
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title_sort	comparing baseline and longitudinal measures in association studies
title_auth	Comparing baseline and longitudinal measures in association studies
abstract	Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. © Wang et al.; licensee BioMed Central Ltd. 2014
abstractGer	Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. © Wang et al.; licensee BioMed Central Ltd. 2014
abstract_unstemmed	Abstract In recent years, longitudinal family-based studies have had success in identifying genetic variants that influence complex traits in genome-wide association studies. In this paper, we suggest that longitudinal analyses may contain valuable information that can enable identification of additional associations compared to baseline analyses. Using Genetic Analysis Workshop 18 data, consisting of whole genome sequence data in a pedigree-based sample, we compared 3 methods for the genetic analysis of longitudinal data to an analysis that used baseline data only. These longitudinal methods were (a) longitudinal mixed-effects model; (b) analysis of the mean trait over time; and (c) a 2-stage analysis, with estimation of a random intercept in the first stage and regression of the random intercept on a single-nucleotide polymorphism at the second stage. All methods accounted for the familial correlation among subjects within a pedigree. The analyses considered common variants with minor allele frequency above 5% on chromosome 3. Analyses were performed without knowledge of the simulation model. The 3 longitudinal methods showed consistent results, which were generally different from those found by using only the baseline observation. The gene CACNA2D3, identified by both longitudinal and baseline approaches, had a stronger signal in the longitudinal analysis (p = 2.65 × $ 10^{−7} $) compared to that in the baseline analysis (p = 2.48 × $ 10^{−5} $). The effect size of the longitudinal mixed-effects model and mean trait were higher compared to the 2-stage approach. The longitudinal results provided stable results different from that using 1 observation at baseline and generally had lower p values. © Wang et al.; licensee BioMed Central Ltd. 2014
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title_short	Comparing baseline and longitudinal measures in association studies
url	https://dx.doi.org/10.1186/1753-6561-8-S1-S84
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author2	Gao, Wei Ngwa, Julius Allard, Catherine Liu, Ching-Ti Cupples, L Adrienne
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up_date	2024-07-03T19:32:47.921Z
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Comparing baseline and longitudinal measures in association studies

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