Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease
Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contri...
Ausführliche Beschreibung
Autor*in: |
Rosenberger, Albert [verfasserIn] |
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E-Artikel |
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Englisch |
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2007 |
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Anmerkung: |
© Rosenberger et al; licensee BioMed Central Ltd. 2007 |
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Übergeordnetes Werk: |
Enthalten in: BMC genetics - London : BioMed Central, 2000, 8(2007), 1 vom: 02. Juli |
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Übergeordnetes Werk: |
volume:8 ; year:2007 ; number:1 ; day:02 ; month:07 |
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DOI / URN: |
10.1186/1471-2156-8-44 |
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SPR027003213 |
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520 | |a Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. | ||
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700 | 1 | |a Gasser, Thomas |4 aut | |
700 | 1 | |a Bickeböller, Heike |4 aut | |
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10.1186/1471-2156-8-44 doi (DE-627)SPR027003213 (SPR)1471-2156-8-44-e DE-627 ger DE-627 rakwb eng Rosenberger, Albert verfasserin aut Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Rosenberger et al; licensee BioMed Central Ltd. 2007 Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. Meta Analysis (dpeaa)DE-He213 Marker Position (dpeaa)DE-He213 Genome Wide Linkage (dpeaa)DE-He213 Phet (dpeaa)DE-He213 Genome Scan (dpeaa)DE-He213 Sharma, Manu aut Müller-Myhsok, Bertram aut Gasser, Thomas aut Bickeböller, Heike aut Enthalten in BMC genetics London : BioMed Central, 2000 8(2007), 1 vom: 02. Juli (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:8 year:2007 number:1 day:02 month:07 https://dx.doi.org/10.1186/1471-2156-8-44 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1 02 07 |
spelling |
10.1186/1471-2156-8-44 doi (DE-627)SPR027003213 (SPR)1471-2156-8-44-e DE-627 ger DE-627 rakwb eng Rosenberger, Albert verfasserin aut Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Rosenberger et al; licensee BioMed Central Ltd. 2007 Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. Meta Analysis (dpeaa)DE-He213 Marker Position (dpeaa)DE-He213 Genome Wide Linkage (dpeaa)DE-He213 Phet (dpeaa)DE-He213 Genome Scan (dpeaa)DE-He213 Sharma, Manu aut Müller-Myhsok, Bertram aut Gasser, Thomas aut Bickeböller, Heike aut Enthalten in BMC genetics London : BioMed Central, 2000 8(2007), 1 vom: 02. Juli (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:8 year:2007 number:1 day:02 month:07 https://dx.doi.org/10.1186/1471-2156-8-44 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1 02 07 |
allfields_unstemmed |
10.1186/1471-2156-8-44 doi (DE-627)SPR027003213 (SPR)1471-2156-8-44-e DE-627 ger DE-627 rakwb eng Rosenberger, Albert verfasserin aut Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Rosenberger et al; licensee BioMed Central Ltd. 2007 Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. Meta Analysis (dpeaa)DE-He213 Marker Position (dpeaa)DE-He213 Genome Wide Linkage (dpeaa)DE-He213 Phet (dpeaa)DE-He213 Genome Scan (dpeaa)DE-He213 Sharma, Manu aut Müller-Myhsok, Bertram aut Gasser, Thomas aut Bickeböller, Heike aut Enthalten in BMC genetics London : BioMed Central, 2000 8(2007), 1 vom: 02. Juli (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:8 year:2007 number:1 day:02 month:07 https://dx.doi.org/10.1186/1471-2156-8-44 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1 02 07 |
allfieldsGer |
10.1186/1471-2156-8-44 doi (DE-627)SPR027003213 (SPR)1471-2156-8-44-e DE-627 ger DE-627 rakwb eng Rosenberger, Albert verfasserin aut Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Rosenberger et al; licensee BioMed Central Ltd. 2007 Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. Meta Analysis (dpeaa)DE-He213 Marker Position (dpeaa)DE-He213 Genome Wide Linkage (dpeaa)DE-He213 Phet (dpeaa)DE-He213 Genome Scan (dpeaa)DE-He213 Sharma, Manu aut Müller-Myhsok, Bertram aut Gasser, Thomas aut Bickeböller, Heike aut Enthalten in BMC genetics London : BioMed Central, 2000 8(2007), 1 vom: 02. Juli (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:8 year:2007 number:1 day:02 month:07 https://dx.doi.org/10.1186/1471-2156-8-44 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1 02 07 |
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10.1186/1471-2156-8-44 doi (DE-627)SPR027003213 (SPR)1471-2156-8-44-e DE-627 ger DE-627 rakwb eng Rosenberger, Albert verfasserin aut Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Rosenberger et al; licensee BioMed Central Ltd. 2007 Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. Meta Analysis (dpeaa)DE-He213 Marker Position (dpeaa)DE-He213 Genome Wide Linkage (dpeaa)DE-He213 Phet (dpeaa)DE-He213 Genome Scan (dpeaa)DE-He213 Sharma, Manu aut Müller-Myhsok, Bertram aut Gasser, Thomas aut Bickeböller, Heike aut Enthalten in BMC genetics London : BioMed Central, 2000 8(2007), 1 vom: 02. Juli (DE-627)326644938 (DE-600)2041497-3 1471-2156 nnns volume:8 year:2007 number:1 day:02 month:07 https://dx.doi.org/10.1186/1471-2156-8-44 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2007 1 02 07 |
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Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease |
abstract |
Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. © Rosenberger et al; licensee BioMed Central Ltd. 2007 |
abstractGer |
Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. © Rosenberger et al; licensee BioMed Central Ltd. 2007 |
abstract_unstemmed |
Background Genome wide linkage scans have often been successful in the identification of genetic regions containing susceptibility genes for a disease. Meta analysis is used to synthesize information and can even deliver evidence for findings missed by original studies. If researchers are not contributing their data, extracting valid information from publications is technically challenging, but worth the effort. We propose an approach to include data extracted from published figures of genome wide linkage scans. The validity of the extraction was examined on the basis of those 25 markers, for which sufficient information was reported. Monte Carlo simulations were used to take into account the uncertainty in marker position and in linkage test statistic. For the final meta analysis we compared the Genome Search Meta Analysis method (GSMA) and the Corrected p-value Meta analysis Method (CPMM). An application to Parkinson's disease is given. Because we had to use secondary data a meta analysis based on original summary values would be desirable. Results Data uncertainty by replicated extraction of marker position is shown to be much smaller than 30 cM, a distance up to which a maximum LOD score may usually be found away from the true locus. The main findings are not impaired by data uncertainty. Conclusion Applying the proposed method a novel linked region for Parkinson's disease was identified on chromosome 14 (p = 0.036). Comparing the two meta analysis methods we found in this analysis more regions of interest being identified by GSMA, whereas CPMM provides stronger evidence for linkage. For further validation of the extraction method comparisons with raw data would be required. © Rosenberger et al; licensee BioMed Central Ltd. 2007 |
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title_short |
Meta analysis of whole-genome linkage scans with data uncertainty: an application to Parkinson's disease |
url |
https://dx.doi.org/10.1186/1471-2156-8-44 |
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author2 |
Sharma, Manu Müller-Myhsok, Bertram Gasser, Thomas Bickeböller, Heike |
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Sharma, Manu Müller-Myhsok, Bertram Gasser, Thomas Bickeböller, Heike |
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doi_str |
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up_date |
2024-07-03T23:53:06.550Z |
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