Association analysis of multiple traits by an approach of combining %$P%$ values
Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statist...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chen, Lili [verfasserIn] Wang, Yong [verfasserIn] Zhou, Yajing [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), 1 vom: 06. Feb., Seite 79-85 |
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| Übergeordnetes Werk: |
volume:97 ; year:2018 ; number:1 ; day:06 ; month:02 ; pages:79-85 |
| Links: |
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| DOI / URN: |
10.1007/s12041-018-0885-0 |
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| Katalog-ID: |
SPR024083186 |
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| 520 | |a Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. | ||
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10.1007/s12041-018-0885-0 doi (DE-627)SPR024083186 (SPR)s12041-018-0885-0-e DE-627 ger DE-627 rakwb eng Chen, Lili verfasserin aut Association analysis of multiple traits by an approach of combining %$P%$ values 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. association analysis (dpeaa)DE-He213 rare variant (dpeaa)DE-He213 common variant (dpeaa)DE-He213 multiple traits (dpeaa)DE-He213 Wang, Yong verfasserin aut Zhou, Yajing verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 1 vom: 06. Feb., Seite 79-85 (DE-627)SPR024069582 nnns volume:97 year:2018 number:1 day:06 month:02 pages:79-85 https://dx.doi.org/10.1007/s12041-018-0885-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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 1 06 02 79-85 |
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10.1007/s12041-018-0885-0 doi (DE-627)SPR024083186 (SPR)s12041-018-0885-0-e DE-627 ger DE-627 rakwb eng Chen, Lili verfasserin aut Association analysis of multiple traits by an approach of combining %$P%$ values 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. association analysis (dpeaa)DE-He213 rare variant (dpeaa)DE-He213 common variant (dpeaa)DE-He213 multiple traits (dpeaa)DE-He213 Wang, Yong verfasserin aut Zhou, Yajing verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 1 vom: 06. Feb., Seite 79-85 (DE-627)SPR024069582 nnns volume:97 year:2018 number:1 day:06 month:02 pages:79-85 https://dx.doi.org/10.1007/s12041-018-0885-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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 1 06 02 79-85 |
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10.1007/s12041-018-0885-0 doi (DE-627)SPR024083186 (SPR)s12041-018-0885-0-e DE-627 ger DE-627 rakwb eng Chen, Lili verfasserin aut Association analysis of multiple traits by an approach of combining %$P%$ values 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. association analysis (dpeaa)DE-He213 rare variant (dpeaa)DE-He213 common variant (dpeaa)DE-He213 multiple traits (dpeaa)DE-He213 Wang, Yong verfasserin aut Zhou, Yajing verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 1 vom: 06. Feb., Seite 79-85 (DE-627)SPR024069582 nnns volume:97 year:2018 number:1 day:06 month:02 pages:79-85 https://dx.doi.org/10.1007/s12041-018-0885-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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 1 06 02 79-85 |
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10.1007/s12041-018-0885-0 doi (DE-627)SPR024083186 (SPR)s12041-018-0885-0-e DE-627 ger DE-627 rakwb eng Chen, Lili verfasserin aut Association analysis of multiple traits by an approach of combining %$P%$ values 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. association analysis (dpeaa)DE-He213 rare variant (dpeaa)DE-He213 common variant (dpeaa)DE-He213 multiple traits (dpeaa)DE-He213 Wang, Yong verfasserin aut Zhou, Yajing verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 1 vom: 06. Feb., Seite 79-85 (DE-627)SPR024069582 nnns volume:97 year:2018 number:1 day:06 month:02 pages:79-85 https://dx.doi.org/10.1007/s12041-018-0885-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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 1 06 02 79-85 |
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10.1007/s12041-018-0885-0 doi (DE-627)SPR024083186 (SPR)s12041-018-0885-0-e DE-627 ger DE-627 rakwb eng Chen, Lili verfasserin aut Association analysis of multiple traits by an approach of combining %$P%$ values 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. association analysis (dpeaa)DE-He213 rare variant (dpeaa)DE-He213 common variant (dpeaa)DE-He213 multiple traits (dpeaa)DE-He213 Wang, Yong verfasserin aut Zhou, Yajing verfasserin aut Enthalten in Journal of Genetics Springer India, 1955 97(2018), 1 vom: 06. Feb., Seite 79-85 (DE-627)SPR024069582 nnns volume:97 year:2018 number:1 day:06 month:02 pages:79-85 https://dx.doi.org/10.1007/s12041-018-0885-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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 1 06 02 79-85 |
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Enthalten in Journal of Genetics 97(2018), 1 vom: 06. Feb., Seite 79-85 volume:97 year:2018 number:1 day:06 month:02 pages:79-85 |
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Association analysis of multiple traits by an approach of combining %$P%$ values |
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Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. |
| abstractGer |
Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. |
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Abstract Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants. |
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