Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads

Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus stati...
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Autor*in:	Duitama, Jorge [verfasserIn] Kennedy, Justin Dinakar, Sanjiv Hernández, Yözen Wu, Yufeng Măndoiu, Ion I

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Hide Markov Model Multilocus Genotype Reference Panel Hide Markov Model Model Genotype Calling

Anmerkung:	© Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 12(2011), Suppl 1 vom: 15. Feb.
Übergeordnetes Werk:	volume:12 ; year:2011 ; number:Suppl 1 ; day:15 ; month:02

Links:	Volltext

DOI / URN:	10.1186/1471-2105-12-S1-S53

Katalog-ID:	SPR026873656

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520			\|a Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies.
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allfields	10.1186/1471-2105-12-S1-S53 doi (DE-627)SPR026873656 (SPR)1471-2105-12-S1-S53-e DE-627 ger DE-627 rakwb eng Duitama, Jorge verfasserin aut Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. Hide Markov Model (dpeaa)DE-He213 Multilocus Genotype (dpeaa)DE-He213 Reference Panel (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Genotype Calling (dpeaa)DE-He213 Kennedy, Justin aut Dinakar, Sanjiv aut Hernández, Yözen aut Wu, Yufeng aut Măndoiu, Ion I aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), Suppl 1 vom: 15. Feb. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:Suppl 1 day:15 month:02 https://dx.doi.org/10.1186/1471-2105-12-S1-S53 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_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_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 12 2011 Suppl 1 15 02
spelling	10.1186/1471-2105-12-S1-S53 doi (DE-627)SPR026873656 (SPR)1471-2105-12-S1-S53-e DE-627 ger DE-627 rakwb eng Duitama, Jorge verfasserin aut Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. Hide Markov Model (dpeaa)DE-He213 Multilocus Genotype (dpeaa)DE-He213 Reference Panel (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Genotype Calling (dpeaa)DE-He213 Kennedy, Justin aut Dinakar, Sanjiv aut Hernández, Yözen aut Wu, Yufeng aut Măndoiu, Ion I aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), Suppl 1 vom: 15. Feb. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:Suppl 1 day:15 month:02 https://dx.doi.org/10.1186/1471-2105-12-S1-S53 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_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_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 12 2011 Suppl 1 15 02
allfields_unstemmed	10.1186/1471-2105-12-S1-S53 doi (DE-627)SPR026873656 (SPR)1471-2105-12-S1-S53-e DE-627 ger DE-627 rakwb eng Duitama, Jorge verfasserin aut Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. Hide Markov Model (dpeaa)DE-He213 Multilocus Genotype (dpeaa)DE-He213 Reference Panel (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Genotype Calling (dpeaa)DE-He213 Kennedy, Justin aut Dinakar, Sanjiv aut Hernández, Yözen aut Wu, Yufeng aut Măndoiu, Ion I aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), Suppl 1 vom: 15. Feb. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:Suppl 1 day:15 month:02 https://dx.doi.org/10.1186/1471-2105-12-S1-S53 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_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_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 12 2011 Suppl 1 15 02
allfieldsGer	10.1186/1471-2105-12-S1-S53 doi (DE-627)SPR026873656 (SPR)1471-2105-12-S1-S53-e DE-627 ger DE-627 rakwb eng Duitama, Jorge verfasserin aut Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. Hide Markov Model (dpeaa)DE-He213 Multilocus Genotype (dpeaa)DE-He213 Reference Panel (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Genotype Calling (dpeaa)DE-He213 Kennedy, Justin aut Dinakar, Sanjiv aut Hernández, Yözen aut Wu, Yufeng aut Măndoiu, Ion I aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), Suppl 1 vom: 15. Feb. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:Suppl 1 day:15 month:02 https://dx.doi.org/10.1186/1471-2105-12-S1-S53 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_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_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 12 2011 Suppl 1 15 02
allfieldsSound	10.1186/1471-2105-12-S1-S53 doi (DE-627)SPR026873656 (SPR)1471-2105-12-S1-S53-e DE-627 ger DE-627 rakwb eng Duitama, Jorge verfasserin aut Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. Hide Markov Model (dpeaa)DE-He213 Multilocus Genotype (dpeaa)DE-He213 Reference Panel (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Genotype Calling (dpeaa)DE-He213 Kennedy, Justin aut Dinakar, Sanjiv aut Hernández, Yözen aut Wu, Yufeng aut Măndoiu, Ion I aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 12(2011), Suppl 1 vom: 15. Feb. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:12 year:2011 number:Suppl 1 day:15 month:02 https://dx.doi.org/10.1186/1471-2105-12-S1-S53 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_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_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 12 2011 Suppl 1 15 02
language	English
source	Enthalten in BMC bioinformatics 12(2011), Suppl 1 vom: 15. Feb. volume:12 year:2011 number:Suppl 1 day:15 month:02
sourceStr	Enthalten in BMC bioinformatics 12(2011), Suppl 1 vom: 15. Feb. volume:12 year:2011 number:Suppl 1 day:15 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Hide Markov Model Multilocus Genotype Reference Panel Hide Markov Model Model Genotype Calling
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Duitama, Jorge @@aut@@ Kennedy, Justin @@aut@@ Dinakar, Sanjiv @@aut@@ Hernández, Yözen @@aut@@ Wu, Yufeng @@aut@@ Măndoiu, Ion I @@aut@@
publishDateDaySort_date	2011-02-15T00:00:00Z
hierarchy_top_id	326644814
id	SPR026873656
language_de	englisch
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topic_title	Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads Hide Markov Model (dpeaa)DE-He213 Multilocus Genotype (dpeaa)DE-He213 Reference Panel (dpeaa)DE-He213 Hide Markov Model Model (dpeaa)DE-He213 Genotype Calling (dpeaa)DE-He213
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title	Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads
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title_sort	linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads
title_auth	Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads
abstract	Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies. © Duitama et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
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Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads

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