Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study
It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic varian...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Noordam, Raymond [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
7 |
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| Übergeordnetes Werk: |
Enthalten in: Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters - Kaya, S. Irem ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:62 ; year:2015 ; pages:31-37 ; extent:7 |
| Links: |
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| DOI / URN: |
10.1016/j.jpsychires.2015.01.005 |
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| 520 | |a It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. | ||
| 520 | |a It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. | ||
| 650 | 7 | |a Drug response biomarkers |2 Elsevier | |
| 650 | 7 | |a Pharmacogenetics |2 Elsevier | |
| 650 | 7 | |a Genome-wide association study |2 Elsevier | |
| 650 | 7 | |a Serotonin uptake inhibitors |2 Elsevier | |
| 650 | 7 | |a Gene–environment interaction |2 Elsevier | |
| 700 | 1 | |a Direk, Nese |4 oth | |
| 700 | 1 | |a Sitlani, Colleen M. |4 oth | |
| 700 | 1 | |a Aarts, Nikkie |4 oth | |
| 700 | 1 | |a Tiemeier, Henning |4 oth | |
| 700 | 1 | |a Hofman, Albert |4 oth | |
| 700 | 1 | |a Uitterlinden, André G. |4 oth | |
| 700 | 1 | |a Psaty, Bruce M. |4 oth | |
| 700 | 1 | |a Stricker, Bruno H. |4 oth | |
| 700 | 1 | |a Visser, Loes E. |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Kaya, S. Irem ELSEVIER |t Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters |d 2022 |g Amsterdam [u.a.] |w (DE-627)ELV007548370 |
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10.1016/j.jpsychires.2015.01.005 doi GBVA2015023000004.pica (DE-627)ELV01896785X (ELSEVIER)S0022-3956(15)00006-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 540 VZ 35.23 bkl Noordam, Raymond verfasserin aut Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. Drug response biomarkers Elsevier Pharmacogenetics Elsevier Genome-wide association study Elsevier Serotonin uptake inhibitors Elsevier Gene–environment interaction Elsevier Direk, Nese oth Sitlani, Colleen M. oth Aarts, Nikkie oth Tiemeier, Henning oth Hofman, Albert oth Uitterlinden, André G. oth Psaty, Bruce M. oth Stricker, Bruno H. oth Visser, Loes E. oth Enthalten in Elsevier Science Kaya, S. Irem ELSEVIER Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters 2022 Amsterdam [u.a.] (DE-627)ELV007548370 volume:62 year:2015 pages:31-37 extent:7 https://doi.org/10.1016/j.jpsychires.2015.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.23 Analytische Chemie: Allgemeines VZ AR 62 2015 31-37 7 045F 610 |
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10.1016/j.jpsychires.2015.01.005 doi GBVA2015023000004.pica (DE-627)ELV01896785X (ELSEVIER)S0022-3956(15)00006-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 540 VZ 35.23 bkl Noordam, Raymond verfasserin aut Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. Drug response biomarkers Elsevier Pharmacogenetics Elsevier Genome-wide association study Elsevier Serotonin uptake inhibitors Elsevier Gene–environment interaction Elsevier Direk, Nese oth Sitlani, Colleen M. oth Aarts, Nikkie oth Tiemeier, Henning oth Hofman, Albert oth Uitterlinden, André G. oth Psaty, Bruce M. oth Stricker, Bruno H. oth Visser, Loes E. oth Enthalten in Elsevier Science Kaya, S. Irem ELSEVIER Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters 2022 Amsterdam [u.a.] (DE-627)ELV007548370 volume:62 year:2015 pages:31-37 extent:7 https://doi.org/10.1016/j.jpsychires.2015.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.23 Analytische Chemie: Allgemeines VZ AR 62 2015 31-37 7 045F 610 |
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10.1016/j.jpsychires.2015.01.005 doi GBVA2015023000004.pica (DE-627)ELV01896785X (ELSEVIER)S0022-3956(15)00006-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 540 VZ 35.23 bkl Noordam, Raymond verfasserin aut Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. Drug response biomarkers Elsevier Pharmacogenetics Elsevier Genome-wide association study Elsevier Serotonin uptake inhibitors Elsevier Gene–environment interaction Elsevier Direk, Nese oth Sitlani, Colleen M. oth Aarts, Nikkie oth Tiemeier, Henning oth Hofman, Albert oth Uitterlinden, André G. oth Psaty, Bruce M. oth Stricker, Bruno H. oth Visser, Loes E. oth Enthalten in Elsevier Science Kaya, S. Irem ELSEVIER Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters 2022 Amsterdam [u.a.] (DE-627)ELV007548370 volume:62 year:2015 pages:31-37 extent:7 https://doi.org/10.1016/j.jpsychires.2015.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.23 Analytische Chemie: Allgemeines VZ AR 62 2015 31-37 7 045F 610 |
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10.1016/j.jpsychires.2015.01.005 doi GBVA2015023000004.pica (DE-627)ELV01896785X (ELSEVIER)S0022-3956(15)00006-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 540 VZ 35.23 bkl Noordam, Raymond verfasserin aut Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. Drug response biomarkers Elsevier Pharmacogenetics Elsevier Genome-wide association study Elsevier Serotonin uptake inhibitors Elsevier Gene–environment interaction Elsevier Direk, Nese oth Sitlani, Colleen M. oth Aarts, Nikkie oth Tiemeier, Henning oth Hofman, Albert oth Uitterlinden, André G. oth Psaty, Bruce M. oth Stricker, Bruno H. oth Visser, Loes E. oth Enthalten in Elsevier Science Kaya, S. Irem ELSEVIER Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters 2022 Amsterdam [u.a.] (DE-627)ELV007548370 volume:62 year:2015 pages:31-37 extent:7 https://doi.org/10.1016/j.jpsychires.2015.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.23 Analytische Chemie: Allgemeines VZ AR 62 2015 31-37 7 045F 610 |
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10.1016/j.jpsychires.2015.01.005 doi GBVA2015023000004.pica (DE-627)ELV01896785X (ELSEVIER)S0022-3956(15)00006-0 DE-627 ger DE-627 rakwb eng 610 610 DE-600 540 VZ 35.23 bkl Noordam, Raymond verfasserin aut Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study 2015transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. Drug response biomarkers Elsevier Pharmacogenetics Elsevier Genome-wide association study Elsevier Serotonin uptake inhibitors Elsevier Gene–environment interaction Elsevier Direk, Nese oth Sitlani, Colleen M. oth Aarts, Nikkie oth Tiemeier, Henning oth Hofman, Albert oth Uitterlinden, André G. oth Psaty, Bruce M. oth Stricker, Bruno H. oth Visser, Loes E. oth Enthalten in Elsevier Science Kaya, S. Irem ELSEVIER Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters 2022 Amsterdam [u.a.] (DE-627)ELV007548370 volume:62 year:2015 pages:31-37 extent:7 https://doi.org/10.1016/j.jpsychires.2015.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 35.23 Analytische Chemie: Allgemeines VZ AR 62 2015 31-37 7 045F 610 |
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identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study |
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Identifying genetic loci associated with antidepressant drug response with drug–gene interaction models in a population-based study |
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It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. |
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It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. |
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It has been difficult to identify genes affecting drug response to Selective Serotonin Reuptake Inhibitors (SSRIs). We used multiple cross-sectional assessments of depressive symptoms in a population-based study to identify potential genetic interactions with SSRIs as a model to study genetic variants associated with SSRI response. This study, embedded in the prospective Rotterdam Study, included all successfully genotyped participants with data on depressive symptoms (CES-D scores). We used repeated measurement models to test multiplicative interaction between genetic variants and use of SSRIs on repeated CESD scores. Besides a genome-wide analysis, we also performed an analysis which was restricted to genes related to the serotonergic signaling pathway. A total of 273 out of 14 937 assessments of depressive symptoms in 6443 participants, use of an SSRI was recorded. After correction for multiple testing, no plausible loci were identified in the genome-wide analysis. However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. Therefore, the present study suggests that drug–gene interaction models on (repeated) cross-sectional assessments of depressive symptoms in a population-based study can identify potential loci that may influence SSRI response. |
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However, among the top 10 independent loci with the lowest p-values, findings within two genes (FSHR and HMGB4) might be of interest. Among 26 genes related to the serotonergic signaling pathway, the rs6108160 polymorphism in the PLCB1 gene reached statistical significance after Bonferroni correction (p-value = 8.1e-5). Also, the widely replicated 102C > T polymorphism in the HTR2A gene showed a statistically significant drug–gene interaction with SSRI use. 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Irem ELSEVIER</subfield><subfield code="t">Trends in on-site removal, treatment, and sensitive assay of common pharmaceuticals in surface waters</subfield><subfield code="d">2022</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV007548370</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:62</subfield><subfield code="g">year:2015</subfield><subfield code="g">pages:31-37</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jpsychires.2015.01.005</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">35.23</subfield><subfield code="j">Analytische Chemie: Allgemeines</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">62</subfield><subfield code="j">2015</subfield><subfield code="h">31-37</subfield><subfield code="g">7</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">610</subfield></datafield></record></collection>
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