SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis

Background Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capabili...
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Autor*in:	Yoo, Jinho [verfasserIn] Lee, Youngbok Kim, Yujung Rha, Sun Young Kim, Yangseok

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Linkage Disequilibrium Linkage Disequilibrium Block Linkage Disequilibrium Analysis Genetic Association Analysis Haplotype Estimation

Anmerkung:	© Yoo et al; licensee BioMed Central Ltd. 2008. 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, 9(2008), 1 vom: 23. Juni
Übergeordnetes Werk:	volume:9 ; year:2008 ; number:1 ; day:23 ; month:06

Links:	Volltext

DOI / URN:	10.1186/1471-2105-9-290

Katalog-ID:	SPR026844818

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520			\|a Background Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use.
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650		4	\|a Genetic Association Analysis \|7 (dpeaa)DE-He213
650		4	\|a Haplotype Estimation \|7 (dpeaa)DE-He213
700	1		\|a Lee, Youngbok \|4 aut
700	1		\|a Kim, Yujung \|4 aut
700	1		\|a Rha, Sun Young \|4 aut
700	1		\|a Kim, Yangseok \|4 aut
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allfields	10.1186/1471-2105-9-290 doi (DE-627)SPR026844818 (SPR)1471-2105-9-290-e DE-627 ger DE-627 rakwb eng Yoo, Jinho verfasserin aut SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yoo et al; licensee BioMed Central Ltd. 2008. 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 Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 Linkage Disequilibrium Analysis (dpeaa)DE-He213 Genetic Association Analysis (dpeaa)DE-He213 Haplotype Estimation (dpeaa)DE-He213 Lee, Youngbok aut Kim, Yujung aut Rha, Sun Young aut Kim, Yangseok aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:06 https://dx.doi.org/10.1186/1471-2105-9-290 lizenzpflichtig 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 9 2008 1 23 06
spelling	10.1186/1471-2105-9-290 doi (DE-627)SPR026844818 (SPR)1471-2105-9-290-e DE-627 ger DE-627 rakwb eng Yoo, Jinho verfasserin aut SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yoo et al; licensee BioMed Central Ltd. 2008. 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 Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 Linkage Disequilibrium Analysis (dpeaa)DE-He213 Genetic Association Analysis (dpeaa)DE-He213 Haplotype Estimation (dpeaa)DE-He213 Lee, Youngbok aut Kim, Yujung aut Rha, Sun Young aut Kim, Yangseok aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:06 https://dx.doi.org/10.1186/1471-2105-9-290 lizenzpflichtig 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 9 2008 1 23 06
allfields_unstemmed	10.1186/1471-2105-9-290 doi (DE-627)SPR026844818 (SPR)1471-2105-9-290-e DE-627 ger DE-627 rakwb eng Yoo, Jinho verfasserin aut SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yoo et al; licensee BioMed Central Ltd. 2008. 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 Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 Linkage Disequilibrium Analysis (dpeaa)DE-He213 Genetic Association Analysis (dpeaa)DE-He213 Haplotype Estimation (dpeaa)DE-He213 Lee, Youngbok aut Kim, Yujung aut Rha, Sun Young aut Kim, Yangseok aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:06 https://dx.doi.org/10.1186/1471-2105-9-290 lizenzpflichtig 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 9 2008 1 23 06
allfieldsGer	10.1186/1471-2105-9-290 doi (DE-627)SPR026844818 (SPR)1471-2105-9-290-e DE-627 ger DE-627 rakwb eng Yoo, Jinho verfasserin aut SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yoo et al; licensee BioMed Central Ltd. 2008. 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 Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 Linkage Disequilibrium Analysis (dpeaa)DE-He213 Genetic Association Analysis (dpeaa)DE-He213 Haplotype Estimation (dpeaa)DE-He213 Lee, Youngbok aut Kim, Yujung aut Rha, Sun Young aut Kim, Yangseok aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:06 https://dx.doi.org/10.1186/1471-2105-9-290 lizenzpflichtig 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 9 2008 1 23 06
allfieldsSound	10.1186/1471-2105-9-290 doi (DE-627)SPR026844818 (SPR)1471-2105-9-290-e DE-627 ger DE-627 rakwb eng Yoo, Jinho verfasserin aut SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Yoo et al; licensee BioMed Central Ltd. 2008. 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 Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 Linkage Disequilibrium Analysis (dpeaa)DE-He213 Genetic Association Analysis (dpeaa)DE-He213 Haplotype Estimation (dpeaa)DE-He213 Lee, Youngbok aut Kim, Yujung aut Rha, Sun Young aut Kim, Yangseok aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), 1 vom: 23. Juni (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:1 day:23 month:06 https://dx.doi.org/10.1186/1471-2105-9-290 lizenzpflichtig 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 9 2008 1 23 06
language	English
source	Enthalten in BMC bioinformatics 9(2008), 1 vom: 23. Juni volume:9 year:2008 number:1 day:23 month:06
sourceStr	Enthalten in BMC bioinformatics 9(2008), 1 vom: 23. Juni volume:9 year:2008 number:1 day:23 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Linkage Disequilibrium Linkage Disequilibrium Block Linkage Disequilibrium Analysis Genetic Association Analysis Haplotype Estimation
isfreeaccess_bool	false
container_title	BMC bioinformatics
authorswithroles_txt_mv	Yoo, Jinho @@aut@@ Lee, Youngbok @@aut@@ Kim, Yujung @@aut@@ Rha, Sun Young @@aut@@ Kim, Yangseok @@aut@@
publishDateDaySort_date	2008-06-23T00:00:00Z
hierarchy_top_id	326644814
id	SPR026844818
language_de	englisch
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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 (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. 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author	Yoo, Jinho
spellingShingle	Yoo, Jinho misc Linkage Disequilibrium misc Linkage Disequilibrium Block misc Linkage Disequilibrium Analysis misc Genetic Association Analysis misc Haplotype Estimation SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis
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topic_title	SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis Linkage Disequilibrium (dpeaa)DE-He213 Linkage Disequilibrium Block (dpeaa)DE-He213 Linkage Disequilibrium Analysis (dpeaa)DE-He213 Genetic Association Analysis (dpeaa)DE-He213 Haplotype Estimation (dpeaa)DE-He213
topic	misc Linkage Disequilibrium misc Linkage Disequilibrium Block misc Linkage Disequilibrium Analysis misc Genetic Association Analysis misc Haplotype Estimation
topic_unstemmed	misc Linkage Disequilibrium misc Linkage Disequilibrium Block misc Linkage Disequilibrium Analysis misc Genetic Association Analysis misc Haplotype Estimation
topic_browse	misc Linkage Disequilibrium misc Linkage Disequilibrium Block misc Linkage Disequilibrium Analysis misc Genetic Association Analysis misc Haplotype Estimation
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title	SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis
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title_full	SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis
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author_browse	Yoo, Jinho Lee, Youngbok Kim, Yujung Rha, Sun Young Kim, Yangseok
container_volume	9
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author-letter	Yoo, Jinho
doi_str_mv	10.1186/1471-2105-9-290
title_sort	snpanalyzer 2.0: a web-based integrated workbench for linkage disequilibrium analysis and association analysis
title_auth	SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis
abstract	Background Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. © Yoo et al; licensee BioMed Central Ltd. 2008. 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 Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. © Yoo et al; licensee BioMed Central Ltd. 2008. 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 Since the completion of the HapMap project, huge numbers of individual genotypes have been generated from many kinds of laboratories. The efforts of finding or interpreting genetic association between disease and SNPs/haplotypes have been on-going widely. So, the necessity of the capability to analyze huge data and diverse interpretation of the results are growing rapidly. Results We have developed an advanced tool to perform linkage disequilibrium analysis, and genetic association analysis between disease and SNPs/haplotypes in an integrated web interface. It comprises of four main analysis modules: (i) data import and preprocessing, (ii) haplotype estimation, (iii) LD blocking and (iv) association analysis. Hardy-Weinberg Equilibrium test is implemented for each SNPs in the data preprocessing. Haplotypes are reconstructed from unphased diploid genotype data, and linkage disequilibrium between pairwise SNPs is computed and represented by D', $ r^{2} $ and LOD score. Tagging SNPs are determined by using the square of Pearson's correlation coefficient ($ r^{2} $). If genotypes from two different sample groups are available, diverse genetic association analyses are implemented using additive, codominant, dominant and recessive models. Multiple verified algorithms and statistics are implemented in parallel for the reliability of the analysis. Conclusion SNPAnalyzer 2.0 performs linkage disequilibrium analysis and genetic association analysis in an integrated web interface using multiple verified algorithms and statistics. Diverse analysis methods, capability of handling huge data and visual comparison of analysis results are very comprehensive and easy-to-use. © Yoo et al; licensee BioMed Central Ltd. 2008. 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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title_short	SNPAnalyzer 2.0: A web-based integrated workbench for linkage disequilibrium analysis and association analysis
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author2	Lee, Youngbok Kim, Yujung Rha, Sun Young Kim, Yangseok
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score	7.398225

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