Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols.
Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the D...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yuh Shiwa [verfasserIn] Tsuyoshi Hachiya [verfasserIn] Ryohei Furukawa [verfasserIn] Hideki Ohmomo [verfasserIn] Kanako Ono [verfasserIn] Hisaaki Kudo [verfasserIn] Jun Hata [verfasserIn] Atsushi Hozawa [verfasserIn] Motoki Iwasaki [verfasserIn] Koichi Matsuda [verfasserIn] Naoko Minegishi [verfasserIn] Mamoru Satoh [verfasserIn] Kozo Tanno [verfasserIn] Taiki Yamaji [verfasserIn] Kenji Wakai [verfasserIn] Jiro Hitomi [verfasserIn] Yutaka Kiyohara [verfasserIn] Michiaki Kubo [verfasserIn] Hideo Tanaka [verfasserIn] Shoichiro Tsugane [verfasserIn] Masayuki Yamamoto [verfasserIn] Kenji Sobue [verfasserIn] Atsushi Shimizu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Übergeordnetes Werk: |
In: PLoS ONE - Public Library of Science (PLoS), 2007, 11(2016), 1, p e0147519 |
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| Übergeordnetes Werk: |
volume:11 ; year:2016 ; number:1, p e0147519 |
| Links: |
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| DOI / URN: |
10.1371/journal.pone.0147519 |
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| Katalog-ID: |
DOAJ04402858X |
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| 520 | |a Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. | ||
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10.1371/journal.pone.0147519 doi (DE-627)DOAJ04402858X (DE-599)DOAJ08433ed3488446f796d58a75d5827c0c DE-627 ger DE-627 rakwb eng Yuh Shiwa verfasserin aut Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. Medicine R Science Q Tsuyoshi Hachiya verfasserin aut Ryohei Furukawa verfasserin aut Hideki Ohmomo verfasserin aut Kanako Ono verfasserin aut Hisaaki Kudo verfasserin aut Jun Hata verfasserin aut Atsushi Hozawa verfasserin aut Motoki Iwasaki verfasserin aut Koichi Matsuda verfasserin aut Naoko Minegishi verfasserin aut Mamoru Satoh verfasserin aut Kozo Tanno verfasserin aut Taiki Yamaji verfasserin aut Kenji Wakai verfasserin aut Jiro Hitomi verfasserin aut Yutaka Kiyohara verfasserin aut Michiaki Kubo verfasserin aut Hideo Tanaka verfasserin aut Shoichiro Tsugane verfasserin aut Masayuki Yamamoto verfasserin aut Kenji Sobue verfasserin aut Atsushi Shimizu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0147519 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0147519 https://doi.org/10.1371/journal.pone.0147519 kostenfrei https://doaj.org/article/08433ed3488446f796d58a75d5827c0c kostenfrei http://europepmc.org/articles/PMC4723336?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0147519 |
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10.1371/journal.pone.0147519 doi (DE-627)DOAJ04402858X (DE-599)DOAJ08433ed3488446f796d58a75d5827c0c DE-627 ger DE-627 rakwb eng Yuh Shiwa verfasserin aut Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. Medicine R Science Q Tsuyoshi Hachiya verfasserin aut Ryohei Furukawa verfasserin aut Hideki Ohmomo verfasserin aut Kanako Ono verfasserin aut Hisaaki Kudo verfasserin aut Jun Hata verfasserin aut Atsushi Hozawa verfasserin aut Motoki Iwasaki verfasserin aut Koichi Matsuda verfasserin aut Naoko Minegishi verfasserin aut Mamoru Satoh verfasserin aut Kozo Tanno verfasserin aut Taiki Yamaji verfasserin aut Kenji Wakai verfasserin aut Jiro Hitomi verfasserin aut Yutaka Kiyohara verfasserin aut Michiaki Kubo verfasserin aut Hideo Tanaka verfasserin aut Shoichiro Tsugane verfasserin aut Masayuki Yamamoto verfasserin aut Kenji Sobue verfasserin aut Atsushi Shimizu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0147519 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0147519 https://doi.org/10.1371/journal.pone.0147519 kostenfrei https://doaj.org/article/08433ed3488446f796d58a75d5827c0c kostenfrei http://europepmc.org/articles/PMC4723336?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0147519 |
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10.1371/journal.pone.0147519 doi (DE-627)DOAJ04402858X (DE-599)DOAJ08433ed3488446f796d58a75d5827c0c DE-627 ger DE-627 rakwb eng Yuh Shiwa verfasserin aut Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. Medicine R Science Q Tsuyoshi Hachiya verfasserin aut Ryohei Furukawa verfasserin aut Hideki Ohmomo verfasserin aut Kanako Ono verfasserin aut Hisaaki Kudo verfasserin aut Jun Hata verfasserin aut Atsushi Hozawa verfasserin aut Motoki Iwasaki verfasserin aut Koichi Matsuda verfasserin aut Naoko Minegishi verfasserin aut Mamoru Satoh verfasserin aut Kozo Tanno verfasserin aut Taiki Yamaji verfasserin aut Kenji Wakai verfasserin aut Jiro Hitomi verfasserin aut Yutaka Kiyohara verfasserin aut Michiaki Kubo verfasserin aut Hideo Tanaka verfasserin aut Shoichiro Tsugane verfasserin aut Masayuki Yamamoto verfasserin aut Kenji Sobue verfasserin aut Atsushi Shimizu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0147519 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0147519 https://doi.org/10.1371/journal.pone.0147519 kostenfrei https://doaj.org/article/08433ed3488446f796d58a75d5827c0c kostenfrei http://europepmc.org/articles/PMC4723336?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0147519 |
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10.1371/journal.pone.0147519 doi (DE-627)DOAJ04402858X (DE-599)DOAJ08433ed3488446f796d58a75d5827c0c DE-627 ger DE-627 rakwb eng Yuh Shiwa verfasserin aut Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. Medicine R Science Q Tsuyoshi Hachiya verfasserin aut Ryohei Furukawa verfasserin aut Hideki Ohmomo verfasserin aut Kanako Ono verfasserin aut Hisaaki Kudo verfasserin aut Jun Hata verfasserin aut Atsushi Hozawa verfasserin aut Motoki Iwasaki verfasserin aut Koichi Matsuda verfasserin aut Naoko Minegishi verfasserin aut Mamoru Satoh verfasserin aut Kozo Tanno verfasserin aut Taiki Yamaji verfasserin aut Kenji Wakai verfasserin aut Jiro Hitomi verfasserin aut Yutaka Kiyohara verfasserin aut Michiaki Kubo verfasserin aut Hideo Tanaka verfasserin aut Shoichiro Tsugane verfasserin aut Masayuki Yamamoto verfasserin aut Kenji Sobue verfasserin aut Atsushi Shimizu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0147519 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0147519 https://doi.org/10.1371/journal.pone.0147519 kostenfrei https://doaj.org/article/08433ed3488446f796d58a75d5827c0c kostenfrei http://europepmc.org/articles/PMC4723336?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0147519 |
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10.1371/journal.pone.0147519 doi (DE-627)DOAJ04402858X (DE-599)DOAJ08433ed3488446f796d58a75d5827c0c DE-627 ger DE-627 rakwb eng Yuh Shiwa verfasserin aut Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. Medicine R Science Q Tsuyoshi Hachiya verfasserin aut Ryohei Furukawa verfasserin aut Hideki Ohmomo verfasserin aut Kanako Ono verfasserin aut Hisaaki Kudo verfasserin aut Jun Hata verfasserin aut Atsushi Hozawa verfasserin aut Motoki Iwasaki verfasserin aut Koichi Matsuda verfasserin aut Naoko Minegishi verfasserin aut Mamoru Satoh verfasserin aut Kozo Tanno verfasserin aut Taiki Yamaji verfasserin aut Kenji Wakai verfasserin aut Jiro Hitomi verfasserin aut Yutaka Kiyohara verfasserin aut Michiaki Kubo verfasserin aut Hideo Tanaka verfasserin aut Shoichiro Tsugane verfasserin aut Masayuki Yamamoto verfasserin aut Kenji Sobue verfasserin aut Atsushi Shimizu verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0147519 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0147519 https://doi.org/10.1371/journal.pone.0147519 kostenfrei https://doaj.org/article/08433ed3488446f796d58a75d5827c0c kostenfrei http://europepmc.org/articles/PMC4723336?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 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_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0147519 |
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Yuh Shiwa @@aut@@ Tsuyoshi Hachiya @@aut@@ Ryohei Furukawa @@aut@@ Hideki Ohmomo @@aut@@ Kanako Ono @@aut@@ Hisaaki Kudo @@aut@@ Jun Hata @@aut@@ Atsushi Hozawa @@aut@@ Motoki Iwasaki @@aut@@ Koichi Matsuda @@aut@@ Naoko Minegishi @@aut@@ Mamoru Satoh @@aut@@ Kozo Tanno @@aut@@ Taiki Yamaji @@aut@@ Kenji Wakai @@aut@@ Jiro Hitomi @@aut@@ Yutaka Kiyohara @@aut@@ Michiaki Kubo @@aut@@ Hideo Tanaka @@aut@@ Shoichiro Tsugane @@aut@@ Masayuki Yamamoto @@aut@@ Kenji Sobue @@aut@@ Atsushi Shimizu @@aut@@ |
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Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols |
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Adjustment of Cell-Type Composition Minimizes Systematic Bias in Blood DNA Methylation Profiles Derived by DNA Collection Protocols. |
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Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. |
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Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. |
| abstract_unstemmed |
Differences in DNA collection protocols may be a potential confounder in epigenome-wide association studies (EWAS) using a large number of blood specimens from multiple biobanks and/or cohorts. Here we show that pre-analytical procedures involved in DNA collection can induce systematic bias in the DNA methylation profiles of blood cells that can be adjusted by cell-type composition variables. In Experiment 1, whole blood from 16 volunteers was collected to examine the effect of a 24 h storage period at 4°C on DNA methylation profiles as measured using the Infinium HumanMethylation450 BeadChip array. Our statistical analysis showed that the P-value distribution of more than 450,000 CpG sites was similar to the theoretical distribution (in quantile-quantile plot, λ = 1.03) when comparing two control replicates, which was remarkably deviated from the theoretical distribution (λ = 1.50) when comparing control and storage conditions. We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). These results revealed important implications for comparing DNA methylation profiles between blood specimens from different sources and may lead to discovery of disease-associated DNA methylation markers and the development of DNA methylation profile-based predictive risk models. |
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We then considered cell-type composition as a possible cause of the observed bias in DNA methylation profiles and found that the bias associated with the cold storage condition was largely decreased (λ adjusted = 1.14) by taking into account a cell-type composition variable. As such, we compared four respective sample collection protocols used in large-scale Japanese biobanks or cohorts as well as two control replicates. Systematic biases in DNA methylation profiles were observed between control and three of four protocols without adjustment of cell-type composition (λ = 1.12-1.45) and no remarkable biases were seen after adjusting for cell-type composition in all four protocols (λ adjusted = 1.00-1.17). 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