A multiple regression method for genomewide association studies using only linkage information
Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mei, Bujun [verfasserIn] Wang, Zhihua [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of Genetics - Springer India, 1955, 97(2018), 2 vom: Juni, Seite 477-482 |
|---|---|
| Übergeordnetes Werk: |
volume:97 ; year:2018 ; number:2 ; month:06 ; pages:477-482 |
| Links: |
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| DOI / URN: |
10.1007/s12041-018-0936-6 |
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| Katalog-ID: |
SPR024083674 |
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| 520 | |a Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. | ||
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10.1007/s12041-018-0936-6 doi (DE-627)SPR024083674 (SPR)s12041-018-0936-6-e DE-627 ger DE-627 rakwb eng Mei, Bujun verfasserin aut A multiple regression method for genomewide association studies using only linkage information 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. QTL mapping (dpeaa)DE-He213 linkage mapping (dpeaa)DE-He213 multiple regression (dpeaa)DE-He213 pedigree-free linkage analysis (dpeaa)DE-He213 Wang, Zhihua verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 2 vom: Juni, Seite 477-482 (DE-627)SPR024069582 nnns volume:97 year:2018 number:2 month:06 pages:477-482 https://dx.doi.org/10.1007/s12041-018-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2190 GBV_ILN_2244 AR 97 2018 2 06 477-482 |
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10.1007/s12041-018-0936-6 doi (DE-627)SPR024083674 (SPR)s12041-018-0936-6-e DE-627 ger DE-627 rakwb eng Mei, Bujun verfasserin aut A multiple regression method for genomewide association studies using only linkage information 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. QTL mapping (dpeaa)DE-He213 linkage mapping (dpeaa)DE-He213 multiple regression (dpeaa)DE-He213 pedigree-free linkage analysis (dpeaa)DE-He213 Wang, Zhihua verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 2 vom: Juni, Seite 477-482 (DE-627)SPR024069582 nnns volume:97 year:2018 number:2 month:06 pages:477-482 https://dx.doi.org/10.1007/s12041-018-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2190 GBV_ILN_2244 AR 97 2018 2 06 477-482 |
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10.1007/s12041-018-0936-6 doi (DE-627)SPR024083674 (SPR)s12041-018-0936-6-e DE-627 ger DE-627 rakwb eng Mei, Bujun verfasserin aut A multiple regression method for genomewide association studies using only linkage information 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. QTL mapping (dpeaa)DE-He213 linkage mapping (dpeaa)DE-He213 multiple regression (dpeaa)DE-He213 pedigree-free linkage analysis (dpeaa)DE-He213 Wang, Zhihua verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 2 vom: Juni, Seite 477-482 (DE-627)SPR024069582 nnns volume:97 year:2018 number:2 month:06 pages:477-482 https://dx.doi.org/10.1007/s12041-018-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2190 GBV_ILN_2244 AR 97 2018 2 06 477-482 |
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10.1007/s12041-018-0936-6 doi (DE-627)SPR024083674 (SPR)s12041-018-0936-6-e DE-627 ger DE-627 rakwb eng Mei, Bujun verfasserin aut A multiple regression method for genomewide association studies using only linkage information 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. QTL mapping (dpeaa)DE-He213 linkage mapping (dpeaa)DE-He213 multiple regression (dpeaa)DE-He213 pedigree-free linkage analysis (dpeaa)DE-He213 Wang, Zhihua verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 2 vom: Juni, Seite 477-482 (DE-627)SPR024069582 nnns volume:97 year:2018 number:2 month:06 pages:477-482 https://dx.doi.org/10.1007/s12041-018-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2190 GBV_ILN_2244 AR 97 2018 2 06 477-482 |
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10.1007/s12041-018-0936-6 doi (DE-627)SPR024083674 (SPR)s12041-018-0936-6-e DE-627 ger DE-627 rakwb eng Mei, Bujun verfasserin aut A multiple regression method for genomewide association studies using only linkage information 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. QTL mapping (dpeaa)DE-He213 linkage mapping (dpeaa)DE-He213 multiple regression (dpeaa)DE-He213 pedigree-free linkage analysis (dpeaa)DE-He213 Wang, Zhihua verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 2 vom: Juni, Seite 477-482 (DE-627)SPR024069582 nnns volume:97 year:2018 number:2 month:06 pages:477-482 https://dx.doi.org/10.1007/s12041-018-0936-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_65 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2004 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2012 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_2088 GBV_ILN_2190 GBV_ILN_2244 AR 97 2018 2 06 477-482 |
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A multiple regression method for genomewide association studies using only linkage information QTL mapping (dpeaa)DE-He213 linkage mapping (dpeaa)DE-He213 multiple regression (dpeaa)DE-He213 pedigree-free linkage analysis (dpeaa)DE-He213 |
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A multiple regression method for genomewide association studies using only linkage information |
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Mei, Bujun |
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Mei, Bujun Wang, Zhihua |
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10.1007/s12041-018-0936-6 |
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multiple regression method for genomewide association studies using only linkage information |
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A multiple regression method for genomewide association studies using only linkage information |
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Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. |
| abstractGer |
Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. |
| abstract_unstemmed |
Abstract Genomewide association studies (GWASs) typically require a base of linkage disequilibrium (LD) to capture quantitative trait locus (QTL) signals. In this study, we tested whether identifying QTLs in the framework of GWAS can be based only on linkage information. Our study sought to validate a method to replace LD with linkage in association studies, and we investigated the statistical power of different heritabilities and the number of QTLs using simulation data. We found that it is entirely feasible to exploit the multiple regression method for GWASs using only linkage information. Similar to the typical genomewide association tests using LD information, our new approach performed validly when the multiple regression based on linkage method was employed. However, the performance improved slightly when the linkage was used alone, which was much closer to the traditional GWAS model using single marker regression. Meanwhile, the statistical power of the new method decreased with increasing number of QTLs, and its power was sensitive to heritability. In summary, these results suggest that this method can identify QTLs, although the power is relatively weak. The cause of this phenomenon remains unknown. |
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A multiple regression method for genomewide association studies using only linkage information |
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