A genotype imputation method for de-identified haplotype reference information by using recurrent neural network
Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a la...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kaname Kojima [verfasserIn] Shu Tadaka [verfasserIn] Fumiki Katsuoka [verfasserIn] Gen Tamiya [verfasserIn] Masayuki Yamamoto [verfasserIn] Kengo Kinoshita [verfasserIn] Ferhat Ay [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Übergeordnetes Werk: |
In: PLoS Computational Biology - Public Library of Science (PLoS), 2005, 16(2020), 10 |
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| Übergeordnetes Werk: |
volume:16 ; year:2020 ; number:10 |
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| 520 | |a Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. | ||
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(DE-627)DOAJ002226049 (DE-599)DOAJce0a795815d64e808f8ab1ec1ee99693 DE-627 ger DE-627 rakwb eng QH301-705.5 Kaname Kojima verfasserin aut A genotype imputation method for de-identified haplotype reference information by using recurrent neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. Biology (General) Shu Tadaka verfasserin aut Fumiki Katsuoka verfasserin aut Gen Tamiya verfasserin aut Masayuki Yamamoto verfasserin aut Kengo Kinoshita verfasserin aut Ferhat Ay verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 16(2020), 10 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:16 year:2020 number:10 https://doaj.org/article/ce0a795815d64e808f8ab1ec1ee99693 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529210/?tool=EBI kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 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_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_206 GBV_ILN_213 GBV_ILN_230 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 10 |
| spelling |
(DE-627)DOAJ002226049 (DE-599)DOAJce0a795815d64e808f8ab1ec1ee99693 DE-627 ger DE-627 rakwb eng QH301-705.5 Kaname Kojima verfasserin aut A genotype imputation method for de-identified haplotype reference information by using recurrent neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. Biology (General) Shu Tadaka verfasserin aut Fumiki Katsuoka verfasserin aut Gen Tamiya verfasserin aut Masayuki Yamamoto verfasserin aut Kengo Kinoshita verfasserin aut Ferhat Ay verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 16(2020), 10 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:16 year:2020 number:10 https://doaj.org/article/ce0a795815d64e808f8ab1ec1ee99693 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529210/?tool=EBI kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 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_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_206 GBV_ILN_213 GBV_ILN_230 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 10 |
| allfields_unstemmed |
(DE-627)DOAJ002226049 (DE-599)DOAJce0a795815d64e808f8ab1ec1ee99693 DE-627 ger DE-627 rakwb eng QH301-705.5 Kaname Kojima verfasserin aut A genotype imputation method for de-identified haplotype reference information by using recurrent neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. Biology (General) Shu Tadaka verfasserin aut Fumiki Katsuoka verfasserin aut Gen Tamiya verfasserin aut Masayuki Yamamoto verfasserin aut Kengo Kinoshita verfasserin aut Ferhat Ay verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 16(2020), 10 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:16 year:2020 number:10 https://doaj.org/article/ce0a795815d64e808f8ab1ec1ee99693 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529210/?tool=EBI kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 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_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_206 GBV_ILN_213 GBV_ILN_230 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 10 |
| allfieldsGer |
(DE-627)DOAJ002226049 (DE-599)DOAJce0a795815d64e808f8ab1ec1ee99693 DE-627 ger DE-627 rakwb eng QH301-705.5 Kaname Kojima verfasserin aut A genotype imputation method for de-identified haplotype reference information by using recurrent neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. Biology (General) Shu Tadaka verfasserin aut Fumiki Katsuoka verfasserin aut Gen Tamiya verfasserin aut Masayuki Yamamoto verfasserin aut Kengo Kinoshita verfasserin aut Ferhat Ay verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 16(2020), 10 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:16 year:2020 number:10 https://doaj.org/article/ce0a795815d64e808f8ab1ec1ee99693 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529210/?tool=EBI kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 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_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_206 GBV_ILN_213 GBV_ILN_230 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 10 |
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(DE-627)DOAJ002226049 (DE-599)DOAJce0a795815d64e808f8ab1ec1ee99693 DE-627 ger DE-627 rakwb eng QH301-705.5 Kaname Kojima verfasserin aut A genotype imputation method for de-identified haplotype reference information by using recurrent neural network 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. Biology (General) Shu Tadaka verfasserin aut Fumiki Katsuoka verfasserin aut Gen Tamiya verfasserin aut Masayuki Yamamoto verfasserin aut Kengo Kinoshita verfasserin aut Ferhat Ay verfasserin aut In PLoS Computational Biology Public Library of Science (PLoS), 2005 16(2020), 10 (DE-627)491436017 (DE-600)2193340-6 15537358 nnns volume:16 year:2020 number:10 https://doaj.org/article/ce0a795815d64e808f8ab1ec1ee99693 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529210/?tool=EBI kostenfrei https://doaj.org/toc/1553-734X Journal toc kostenfrei https://doaj.org/toc/1553-7358 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_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_206 GBV_ILN_213 GBV_ILN_230 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2020 10 |
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Kaname Kojima @@aut@@ Shu Tadaka @@aut@@ Fumiki Katsuoka @@aut@@ Gen Tamiya @@aut@@ Masayuki Yamamoto @@aut@@ Kengo Kinoshita @@aut@@ Ferhat Ay @@aut@@ |
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QH301-705.5 A genotype imputation method for de-identified haplotype reference information by using recurrent neural network |
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A genotype imputation method for de-identified haplotype reference information by using recurrent neural network |
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genotype imputation method for de-identified haplotype reference information by using recurrent neural network |
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A genotype imputation method for de-identified haplotype reference information by using recurrent neural network |
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Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. |
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Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. |
| abstract_unstemmed |
Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel. In general, more accurate imputation results were obtained using a larger size of haplotype reference panel. Most of the existing genotype imputation methods explicitly require the haplotype reference panel in precise form, but the accessibility of haplotype data is often limited, due to the requirement of agreements from the donors. Since de-identified information such as summary statistics or model parameters can be used publicly, imputation methods using de-identified haplotype reference information might be useful to enhance the quality of imputation results under the condition where the access of the haplotype data is limited. In this study, we proposed a novel imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network (RNN). The model parameters are presented in the form of de-identified information from which the restoration of the genotype data at the individual-level is almost impossible. We demonstrated that the proposed method provides comparable imputation accuracy when compared with the existing imputation methods using haplotype datasets from the 1000 Genomes Project (1KGP) and the Haplotype Reference Consortium. We also considered a scenario where a subset of haplotypes is made available only in de-identified form for the haplotype reference panel. In the evaluation using the 1KGP dataset under the scenario, the imputation accuracy of the proposed method is much higher than that of the existing imputation methods. We therefore conclude that our RNN-based method is quite promising to further promote the data-sharing of sensitive genome data under the recent movement for the protection of individuals’ privacy. Author summary Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of genome data of a large number of individuals called a reference panel. In general, more accurate imputation results are obtained using a larger size of the reference panel. Although most of the existing imputation methods use the reference panel in an explicit form, the accessibility of genome data is often limited due to the requirement of agreements from the donors. We thus proposed a new imputation method that handles the reference panel as its model parameters by using bidirectional recurrent neural network. Since it is almost impossible to restore genome data at the individual-level from the model parameters, they can be shared publicly as the de-identified information even when the accessibility of the original reference panel is limited. We demonstrate that the proposed method provides comparable imputation accuracy with the existing methods. We also considered a scenario where a part of the genome data is made available only in de-identified form for the reference panel and have shown that the imputation accuracy of the proposed method is much higher than that of the existing methods under the scenario. |
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| score |
7.3988514 |