Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model
Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including gene...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kato, Bernet S [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Schlagwörter: |
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| Anmerkung: |
© Kato et al; licensee BioMed Central Ltd. 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Proteome science - London : BioMed Central, 2003, 9(2011), 1 vom: 17. Nov. |
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| Übergeordnetes Werk: |
volume:9 ; year:2011 ; number:1 ; day:17 ; month:11 |
| Links: |
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| DOI / URN: |
10.1186/1477-5956-9-73 |
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| Katalog-ID: |
SPR029468701 |
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| 520 | |a Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. | ||
| 650 | 4 | |a Variance decomposition |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a linear mixed-effects model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a longitudinal twin study |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a suspension bead arrays |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a antibodies |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a protein profiling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nicholson, George |4 aut | |
| 700 | 1 | |a Neiman, Maja |4 aut | |
| 700 | 1 | |a Rantalainen, Mattias |4 aut | |
| 700 | 1 | |a Holmes, Chris C |4 aut | |
| 700 | 1 | |a Barrett, Amy |4 aut | |
| 700 | 1 | |a Uhlén, Mathias |4 aut | |
| 700 | 1 | |a Nilsson, Peter |4 aut | |
| 700 | 1 | |a Spector, Tim D |4 aut | |
| 700 | 1 | |a Schwenk, Jochen M |4 aut | |
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10.1186/1477-5956-9-73 doi (DE-627)SPR029468701 (SPR)1477-5956-9-73-e DE-627 ger DE-627 rakwb eng Kato, Bernet S verfasserin aut Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kato et al; licensee BioMed Central Ltd. 2011 Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. Variance decomposition (dpeaa)DE-He213 linear mixed-effects model (dpeaa)DE-He213 longitudinal twin study (dpeaa)DE-He213 suspension bead arrays (dpeaa)DE-He213 antibodies (dpeaa)DE-He213 protein profiling (dpeaa)DE-He213 Nicholson, George aut Neiman, Maja aut Rantalainen, Mattias aut Holmes, Chris C aut Barrett, Amy aut Uhlén, Mathias aut Nilsson, Peter aut Spector, Tim D aut Schwenk, Jochen M aut Enthalten in Proteome science London : BioMed Central, 2003 9(2011), 1 vom: 17. Nov. (DE-627)383961440 (DE-600)2141087-2 1477-5956 nnns volume:9 year:2011 number:1 day:17 month:11 https://dx.doi.org/10.1186/1477-5956-9-73 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 9 2011 1 17 11 |
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10.1186/1477-5956-9-73 doi (DE-627)SPR029468701 (SPR)1477-5956-9-73-e DE-627 ger DE-627 rakwb eng Kato, Bernet S verfasserin aut Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kato et al; licensee BioMed Central Ltd. 2011 Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. Variance decomposition (dpeaa)DE-He213 linear mixed-effects model (dpeaa)DE-He213 longitudinal twin study (dpeaa)DE-He213 suspension bead arrays (dpeaa)DE-He213 antibodies (dpeaa)DE-He213 protein profiling (dpeaa)DE-He213 Nicholson, George aut Neiman, Maja aut Rantalainen, Mattias aut Holmes, Chris C aut Barrett, Amy aut Uhlén, Mathias aut Nilsson, Peter aut Spector, Tim D aut Schwenk, Jochen M aut Enthalten in Proteome science London : BioMed Central, 2003 9(2011), 1 vom: 17. Nov. (DE-627)383961440 (DE-600)2141087-2 1477-5956 nnns volume:9 year:2011 number:1 day:17 month:11 https://dx.doi.org/10.1186/1477-5956-9-73 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 9 2011 1 17 11 |
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10.1186/1477-5956-9-73 doi (DE-627)SPR029468701 (SPR)1477-5956-9-73-e DE-627 ger DE-627 rakwb eng Kato, Bernet S verfasserin aut Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kato et al; licensee BioMed Central Ltd. 2011 Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. Variance decomposition (dpeaa)DE-He213 linear mixed-effects model (dpeaa)DE-He213 longitudinal twin study (dpeaa)DE-He213 suspension bead arrays (dpeaa)DE-He213 antibodies (dpeaa)DE-He213 protein profiling (dpeaa)DE-He213 Nicholson, George aut Neiman, Maja aut Rantalainen, Mattias aut Holmes, Chris C aut Barrett, Amy aut Uhlén, Mathias aut Nilsson, Peter aut Spector, Tim D aut Schwenk, Jochen M aut Enthalten in Proteome science London : BioMed Central, 2003 9(2011), 1 vom: 17. Nov. (DE-627)383961440 (DE-600)2141087-2 1477-5956 nnns volume:9 year:2011 number:1 day:17 month:11 https://dx.doi.org/10.1186/1477-5956-9-73 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 9 2011 1 17 11 |
| allfieldsGer |
10.1186/1477-5956-9-73 doi (DE-627)SPR029468701 (SPR)1477-5956-9-73-e DE-627 ger DE-627 rakwb eng Kato, Bernet S verfasserin aut Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kato et al; licensee BioMed Central Ltd. 2011 Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. Variance decomposition (dpeaa)DE-He213 linear mixed-effects model (dpeaa)DE-He213 longitudinal twin study (dpeaa)DE-He213 suspension bead arrays (dpeaa)DE-He213 antibodies (dpeaa)DE-He213 protein profiling (dpeaa)DE-He213 Nicholson, George aut Neiman, Maja aut Rantalainen, Mattias aut Holmes, Chris C aut Barrett, Amy aut Uhlén, Mathias aut Nilsson, Peter aut Spector, Tim D aut Schwenk, Jochen M aut Enthalten in Proteome science London : BioMed Central, 2003 9(2011), 1 vom: 17. Nov. (DE-627)383961440 (DE-600)2141087-2 1477-5956 nnns volume:9 year:2011 number:1 day:17 month:11 https://dx.doi.org/10.1186/1477-5956-9-73 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 9 2011 1 17 11 |
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10.1186/1477-5956-9-73 doi (DE-627)SPR029468701 (SPR)1477-5956-9-73-e DE-627 ger DE-627 rakwb eng Kato, Bernet S verfasserin aut Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kato et al; licensee BioMed Central Ltd. 2011 Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. Variance decomposition (dpeaa)DE-He213 linear mixed-effects model (dpeaa)DE-He213 longitudinal twin study (dpeaa)DE-He213 suspension bead arrays (dpeaa)DE-He213 antibodies (dpeaa)DE-He213 protein profiling (dpeaa)DE-He213 Nicholson, George aut Neiman, Maja aut Rantalainen, Mattias aut Holmes, Chris C aut Barrett, Amy aut Uhlén, Mathias aut Nilsson, Peter aut Spector, Tim D aut Schwenk, Jochen M aut Enthalten in Proteome science London : BioMed Central, 2003 9(2011), 1 vom: 17. Nov. (DE-627)383961440 (DE-600)2141087-2 1477-5956 nnns volume:9 year:2011 number:1 day:17 month:11 https://dx.doi.org/10.1186/1477-5956-9-73 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 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_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 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 9 2011 1 17 11 |
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variance decomposition of protein profiles from antibody arrays using a longitudinal twin model |
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Variance decomposition of protein profiles from antibody arrays using a longitudinal twin model |
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Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. © Kato et al; licensee BioMed Central Ltd. 2011 |
| abstractGer |
Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. © Kato et al; licensee BioMed Central Ltd. 2011 |
| abstract_unstemmed |
Background The advent of affinity-based proteomics technologies for global protein profiling provides the prospect of finding new molecular biomarkers for common, multifactorial disorders. The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods. © Kato et al; licensee BioMed Central Ltd. 2011 |
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The molecular phenotypes obtained from studies on such platforms are driven by multiple sources, including genetic, environmental, and experimental components. In characterizing the contribution of different sources of variation to the measured phenotypes, the aim is to facilitate the design and interpretation of future biomedical studies employing exploratory and multiplexed technologies. Thus, biometrical genetic modelling of twin or other family data can be used to decompose the variation underlying a phenotype into biological and experimental components. Results Using antibody suspension bead arrays and antibodies from the Human Protein Atlas, we study unfractionated serum from a longitudinal study on 154 twins. In this study, we provide a detailed description of how the variation in a molecular phenotype in terms of protein profile can be decomposed into familial i.e. genetic and common environmental; individual environmental, short-term biological and experimental components. The results show that across 69 antibodies analyzed in the study, the median proportion of the total variation explained by familial sources is 12% (IQR 1-22%), and the median proportion of the total variation attributable to experimental sources is 63% (IQR 53-72%). Conclusion The variability analysis of antibody arrays highlights the importance to consider variability components and their relative contributions when designing and evaluating studies for biomarker discoveries with exploratory, high-throughput and multiplexed methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variance decomposition</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">linear mixed-effects model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">longitudinal twin study</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">suspension bead arrays</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">antibodies</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">protein profiling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nicholson, George</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Neiman, Maja</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rantalainen, Mattias</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Holmes, Chris C</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Barrett, Amy</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Uhlén, Mathias</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nilsson, Peter</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Spector, Tim D</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schwenk, Jochen M</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Proteome science</subfield><subfield code="d">London : BioMed Central, 2003</subfield><subfield code="g">9(2011), 1 vom: 17. 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