The advantages of dense marker sets for linkage analysis with very large families
Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigree...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Thomson, Russell [verfasserIn] Quinn, Stephen [verfasserIn] McKay, James [verfasserIn] Silver, Jeremy [verfasserIn] Bahlo, Melanie [verfasserIn] FitzGerald, Liesel [verfasserIn] Foote, Simon [verfasserIn] Dickinson, Jo [verfasserIn] Stankovich, Jim [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Human genetics |
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| Übergeordnetes Werk: |
volume:121 ; year:2007 ; number:3-4 ; day:25 ; month:01 ; pages:459-468 |
| Links: |
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| DOI / URN: |
10.1007/s00439-007-0323-5 |
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| Katalog-ID: |
SPR006284922 |
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| 100 | 1 | |a Thomson, Russell |e verfasserin |4 aut | |
| 245 | 1 | 4 | |a The advantages of dense marker sets for linkage analysis with very large families |
| 264 | 1 | |c 2007 | |
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| 520 | |a Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. | ||
| 650 | 4 | |a Markov Chain Monte Carlo |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Variance Component Analysis |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a International HapMap Project |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Large Pedigree |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Markov Chain Monte Carlo Technique |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Quinn, Stephen |e verfasserin |4 aut | |
| 700 | 1 | |a McKay, James |e verfasserin |4 aut | |
| 700 | 1 | |a Silver, Jeremy |e verfasserin |4 aut | |
| 700 | 1 | |a Bahlo, Melanie |e verfasserin |4 aut | |
| 700 | 1 | |a FitzGerald, Liesel |e verfasserin |4 aut | |
| 700 | 1 | |a Foote, Simon |e verfasserin |4 aut | |
| 700 | 1 | |a Dickinson, Jo |e verfasserin |4 aut | |
| 700 | 1 | |a Stankovich, Jim |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Human genetics <Berlin> |d Berlin : Springer, 1964 |g 121(2007), 3-4 vom: 25. Jan., Seite 459-468 |w (DE-627)253723973 |w (DE-600)1459188-1 |x 1432-1203 |7 nnns |
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10.1007/s00439-007-0323-5 doi (DE-627)SPR006284922 (SPR)s00439-007-0323-5-e DE-627 ger DE-627 rakwb eng 610 ASE 44.48 bkl Thomson, Russell verfasserin aut The advantages of dense marker sets for linkage analysis with very large families 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. Markov Chain Monte Carlo (dpeaa)DE-He213 Variance Component Analysis (dpeaa)DE-He213 International HapMap Project (dpeaa)DE-He213 Large Pedigree (dpeaa)DE-He213 Markov Chain Monte Carlo Technique (dpeaa)DE-He213 Quinn, Stephen verfasserin aut McKay, James verfasserin aut Silver, Jeremy verfasserin aut Bahlo, Melanie verfasserin aut FitzGerald, Liesel verfasserin aut Foote, Simon verfasserin aut Dickinson, Jo verfasserin aut Stankovich, Jim verfasserin aut Enthalten in Human genetics <Berlin> Berlin : Springer, 1964 121(2007), 3-4 vom: 25. Jan., Seite 459-468 (DE-627)253723973 (DE-600)1459188-1 1432-1203 nnns volume:121 year:2007 number:3-4 day:25 month:01 pages:459-468 https://dx.doi.org/10.1007/s00439-007-0323-5 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.48 ASE AR 121 2007 3-4 25 01 459-468 |
| spelling |
10.1007/s00439-007-0323-5 doi (DE-627)SPR006284922 (SPR)s00439-007-0323-5-e DE-627 ger DE-627 rakwb eng 610 ASE 44.48 bkl Thomson, Russell verfasserin aut The advantages of dense marker sets for linkage analysis with very large families 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. Markov Chain Monte Carlo (dpeaa)DE-He213 Variance Component Analysis (dpeaa)DE-He213 International HapMap Project (dpeaa)DE-He213 Large Pedigree (dpeaa)DE-He213 Markov Chain Monte Carlo Technique (dpeaa)DE-He213 Quinn, Stephen verfasserin aut McKay, James verfasserin aut Silver, Jeremy verfasserin aut Bahlo, Melanie verfasserin aut FitzGerald, Liesel verfasserin aut Foote, Simon verfasserin aut Dickinson, Jo verfasserin aut Stankovich, Jim verfasserin aut Enthalten in Human genetics <Berlin> Berlin : Springer, 1964 121(2007), 3-4 vom: 25. Jan., Seite 459-468 (DE-627)253723973 (DE-600)1459188-1 1432-1203 nnns volume:121 year:2007 number:3-4 day:25 month:01 pages:459-468 https://dx.doi.org/10.1007/s00439-007-0323-5 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.48 ASE AR 121 2007 3-4 25 01 459-468 |
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10.1007/s00439-007-0323-5 doi (DE-627)SPR006284922 (SPR)s00439-007-0323-5-e DE-627 ger DE-627 rakwb eng 610 ASE 44.48 bkl Thomson, Russell verfasserin aut The advantages of dense marker sets for linkage analysis with very large families 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. Markov Chain Monte Carlo (dpeaa)DE-He213 Variance Component Analysis (dpeaa)DE-He213 International HapMap Project (dpeaa)DE-He213 Large Pedigree (dpeaa)DE-He213 Markov Chain Monte Carlo Technique (dpeaa)DE-He213 Quinn, Stephen verfasserin aut McKay, James verfasserin aut Silver, Jeremy verfasserin aut Bahlo, Melanie verfasserin aut FitzGerald, Liesel verfasserin aut Foote, Simon verfasserin aut Dickinson, Jo verfasserin aut Stankovich, Jim verfasserin aut Enthalten in Human genetics <Berlin> Berlin : Springer, 1964 121(2007), 3-4 vom: 25. Jan., Seite 459-468 (DE-627)253723973 (DE-600)1459188-1 1432-1203 nnns volume:121 year:2007 number:3-4 day:25 month:01 pages:459-468 https://dx.doi.org/10.1007/s00439-007-0323-5 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.48 ASE AR 121 2007 3-4 25 01 459-468 |
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10.1007/s00439-007-0323-5 doi (DE-627)SPR006284922 (SPR)s00439-007-0323-5-e DE-627 ger DE-627 rakwb eng 610 ASE 44.48 bkl Thomson, Russell verfasserin aut The advantages of dense marker sets for linkage analysis with very large families 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. Markov Chain Monte Carlo (dpeaa)DE-He213 Variance Component Analysis (dpeaa)DE-He213 International HapMap Project (dpeaa)DE-He213 Large Pedigree (dpeaa)DE-He213 Markov Chain Monte Carlo Technique (dpeaa)DE-He213 Quinn, Stephen verfasserin aut McKay, James verfasserin aut Silver, Jeremy verfasserin aut Bahlo, Melanie verfasserin aut FitzGerald, Liesel verfasserin aut Foote, Simon verfasserin aut Dickinson, Jo verfasserin aut Stankovich, Jim verfasserin aut Enthalten in Human genetics <Berlin> Berlin : Springer, 1964 121(2007), 3-4 vom: 25. Jan., Seite 459-468 (DE-627)253723973 (DE-600)1459188-1 1432-1203 nnns volume:121 year:2007 number:3-4 day:25 month:01 pages:459-468 https://dx.doi.org/10.1007/s00439-007-0323-5 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.48 ASE AR 121 2007 3-4 25 01 459-468 |
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10.1007/s00439-007-0323-5 doi (DE-627)SPR006284922 (SPR)s00439-007-0323-5-e DE-627 ger DE-627 rakwb eng 610 ASE 44.48 bkl Thomson, Russell verfasserin aut The advantages of dense marker sets for linkage analysis with very large families 2007 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. Markov Chain Monte Carlo (dpeaa)DE-He213 Variance Component Analysis (dpeaa)DE-He213 International HapMap Project (dpeaa)DE-He213 Large Pedigree (dpeaa)DE-He213 Markov Chain Monte Carlo Technique (dpeaa)DE-He213 Quinn, Stephen verfasserin aut McKay, James verfasserin aut Silver, Jeremy verfasserin aut Bahlo, Melanie verfasserin aut FitzGerald, Liesel verfasserin aut Foote, Simon verfasserin aut Dickinson, Jo verfasserin aut Stankovich, Jim verfasserin aut Enthalten in Human genetics <Berlin> Berlin : Springer, 1964 121(2007), 3-4 vom: 25. Jan., Seite 459-468 (DE-627)253723973 (DE-600)1459188-1 1432-1203 nnns volume:121 year:2007 number:3-4 day:25 month:01 pages:459-468 https://dx.doi.org/10.1007/s00439-007-0323-5 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.48 ASE AR 121 2007 3-4 25 01 459-468 |
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Enthalten in Human genetics <Berlin> 121(2007), 3-4 vom: 25. Jan., Seite 459-468 volume:121 year:2007 number:3-4 day:25 month:01 pages:459-468 |
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Thomson, Russell @@aut@@ Quinn, Stephen @@aut@@ McKay, James @@aut@@ Silver, Jeremy @@aut@@ Bahlo, Melanie @@aut@@ FitzGerald, Liesel @@aut@@ Foote, Simon @@aut@@ Dickinson, Jo @@aut@@ Stankovich, Jim @@aut@@ |
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Thomson, Russell |
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Thomson, Russell ddc 610 bkl 44.48 misc Markov Chain Monte Carlo misc Variance Component Analysis misc International HapMap Project misc Large Pedigree misc Markov Chain Monte Carlo Technique The advantages of dense marker sets for linkage analysis with very large families |
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610 ASE 44.48 bkl The advantages of dense marker sets for linkage analysis with very large families Markov Chain Monte Carlo (dpeaa)DE-He213 Variance Component Analysis (dpeaa)DE-He213 International HapMap Project (dpeaa)DE-He213 Large Pedigree (dpeaa)DE-He213 Markov Chain Monte Carlo Technique (dpeaa)DE-He213 |
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advantages of dense marker sets for linkage analysis with very large families |
| title_auth |
The advantages of dense marker sets for linkage analysis with very large families |
| abstract |
Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. |
| abstractGer |
Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. |
| abstract_unstemmed |
Abstract Dense sets of hundreds of thousands of markers have been developed for genome-wide association studies. These marker sets are also beneficial for linkage analysis of large, deep pedigrees containing distantly related cases. It is impossible to analyse jointly all genotypes in large pedigrees using the Lander–Green Algorithm, however, as marker density increases it becomes less crucial to analyse all individuals’ genotypes simultaneously. In this report, an approximate multipoint non-parametric technique is described, where large pedigrees are split into many small pedigrees, each containing just two cases. This technique is demonstrated, using phased data from the International Hapmap Project to simulate sets of 10,000, 50,000 and 250,000 markers, showing that it becomes increasingly accurate as more markers are genotyped. This method allows routine linkage analysis of large families with dense marker sets and represents a more easily applied alternative to Monte Carlo Markov Chain methods. |
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The advantages of dense marker sets for linkage analysis with very large families |
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https://dx.doi.org/10.1007/s00439-007-0323-5 |
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Quinn, Stephen McKay, James Silver, Jeremy Bahlo, Melanie FitzGerald, Liesel Foote, Simon Dickinson, Jo Stankovich, Jim |
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| score |
7.4004526 |