Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Eleanor Wigmore; Jonathan Hafferty; Lynsey Hall; David M. Howard; Toni-Kim Clarke; Chiara Fabbri; Cathryn M. Lewis; Rudolf Uher; Lauren Navrady; Mark James Adams; Yanni Zeng; Archibald Campbell; Jude Gibson; Philippa Thomson; Caroline Hayward; Blair H Smith; Lynne J. Hocking; Sandosh Padmanabhan; Ian Deary; David Porteous; Ole Mors; Manuel Mattheisen; Kristin Nicodemus; Andrew McIntosh

doi:10.1038/s41397-019-0067-3

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Eleanor Wigmore (Lead Author), Jonathan Hafferty, Lynsey Hall, David M. Howard, Toni-Kim Clarke, Chiara Fabbri, Cathryn M. Lewis, Rudolf Uher, Lauren Navrady, Mark James Adams, Yanni Zeng, Archibald Campbell, Jude Gibson, Philippa Thomson, Caroline Hayward, Blair H Smith, Lynne J. Hocking, Sandosh Padmanabhan, Ian Deary, David PorteousOle Mors, Manuel Mattheisen, Kristin Nicodemus, Andrew McIntosh

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n=4,213, cases=358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n=3,452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene-sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population based observational studies may provide a tractable approach to achieving the necessary statistical power.

Original language	English
Journal	Pharmacogenomics Journal
DOIs	https://doi.org/10.1038/s41397-019-0067-3
Publication status	Published - 31 Jan 2019

Access to Document

10.1038/s41397-019-0067-3

s41397-019-0067-3Final published version, 1.99 MB

Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders
McIntosh, A., Crompton, C., Fletcher-Watson, S., Lawrie, S. & Whalley, H.
MRC
31/03/18 → 30/03/20
Project: Research
Stratifying Resilience and Depression Longitudinally
McIntosh, A., Deary, I., Evans, K., Haley, C. & Porteous, D.
UK-based charities
1/01/15 → 30/06/21
Project: Research
RA2661 Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2. Main Budget.
Deary, I., Gale, C., Holmes, M., Logie, P., Maclullich, A., Porteous, D., Seckl, J., Starr, J., Wardlaw, J. & Okely, J.
1/09/13 → 31/08/19
Project: Research

Genome-wide association summary statistics of antidepressant treatment resistance meta-analysis
Wigmore, E. (Creator), Hafferty, J. (Creator), Hall, L. (Creator), Howard, D. (Creator), Clarke, T. (Creator), Fabbri, C. (Creator), Lewis, C. (Creator), Navrady, L. (Creator), Adams, M. (Creator), Zeng, Y. (Creator), Campbell, A. (Creator), Gibson, J. (Creator), Thomson, P. (Creator), Hayward, C. (Creator), Smith, B. (Creator), Hocking, L. (Creator), Padmanabhan, S. (Creator), Deary, I. (Creator), Porteous, D. (Creator), Mors, O. (Creator), Mattheisen, M. (Creator), Uher, R. (Creator), Nicodemus, K. (Creator) & McIntosh, A. (Creator), Edinburgh DataShare, 31 Mar 2020
DOI: 10.7488/ds/2791
Dataset

Cite this

Wigmore, E., Hafferty, J., Hall, L., Howard, D. M., Clarke, T-K., Fabbri, C., Lewis, C. M., Uher, R., Navrady, L., Adams, M. J., Zeng, Y., Campbell, A., Gibson, J., Thomson, P., Hayward, C., Smith, B. H., Hocking, L. J., Padmanabhan, S., Deary, I., ... McIntosh, A. (2019). Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP. Pharmacogenomics Journal. https://doi.org/10.1038/s41397-019-0067-3

@article{d6035bcc6aaa468d999435e288c59007,

title = "Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP",

abstract = "Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n=4,213, cases=358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n=3,452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene-sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population based observational studies may provide a tractable approach to achieving the necessary statistical power.",

author = "Eleanor Wigmore and Jonathan Hafferty and Lynsey Hall and Howard, {David M.} and Toni-Kim Clarke and Chiara Fabbri and Lewis, {Cathryn M.} and Rudolf Uher and Lauren Navrady and Adams, {Mark James} and Yanni Zeng and Archibald Campbell and Jude Gibson and Philippa Thomson and Caroline Hayward and Smith, {Blair H} and Hocking, {Lynne J.} and Sandosh Padmanabhan and Ian Deary and David Porteous and Ole Mors and Manuel Mattheisen and Kristin Nicodemus and Andrew McIntosh",

year = "2019",

month = jan,

day = "31",

doi = "10.1038/s41397-019-0067-3",

language = "English",

journal = "Pharmacogenomics Journal",

issn = "1470-269X",

publisher = "Nature Publishing Group",

}

Wigmore, E, Hafferty, J, Hall, L, Howard, DM, Clarke, T-K, Fabbri, C, Lewis, CM, Uher, R, Navrady, L, Adams, MJ, Zeng, Y, Campbell, A, Gibson, J, Thomson, P , Hayward, C, Smith, BH, Hocking, LJ, Padmanabhan, S, Deary, I, Porteous, D, Mors, O, Mattheisen, M, Nicodemus, K & McIntosh, A 2019, 'Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP', Pharmacogenomics Journal. https://doi.org/10.1038/s41397-019-0067-3

TY - JOUR

T1 - Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

AU - Wigmore, Eleanor

AU - Hafferty, Jonathan

AU - Hall, Lynsey

AU - Howard, David M.

AU - Clarke, Toni-Kim

AU - Fabbri, Chiara

AU - Lewis, Cathryn M.

AU - Uher, Rudolf

AU - Navrady, Lauren

AU - Adams, Mark James

AU - Zeng, Yanni

AU - Campbell, Archibald

AU - Gibson, Jude

AU - Thomson, Philippa

AU - Hayward, Caroline

AU - Smith, Blair H

AU - Hocking, Lynne J.

AU - Padmanabhan, Sandosh

AU - Deary, Ian

AU - Porteous, David

AU - Mors, Ole

AU - Mattheisen, Manuel

AU - Nicodemus, Kristin

AU - McIntosh, Andrew

PY - 2019/1/31

Y1 - 2019/1/31

N2 - Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n=4,213, cases=358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n=3,452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene-sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population based observational studies may provide a tractable approach to achieving the necessary statistical power.

AB - Antidepressants demonstrate modest response rates in the treatment of major depressive disorder (MDD). Despite previous genome-wide association studies (GWAS) of antidepressant treatment response, the underlying genetic factors are unknown. Using prescription data in a population and family-based cohort (Generation Scotland: Scottish Family Health Study; GS:SFHS), we sought to define a measure of (a) antidepressant treatment resistance and (b) stages of antidepressant resistance by inferring antidepressant switching as non-response to treatment. GWAS were conducted separately for antidepressant treatment resistance in GS:SFHS and the Genome-based Therapeutic Drugs for Depression (GENDEP) study and then meta-analysed (meta-analysis n=4,213, cases=358). For stages of antidepressant resistance, a GWAS on GS:SFHS only was performed (n=3,452). Additionally, we conducted gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis. We did not identify any significant loci, genes or gene-sets associated with antidepressant treatment resistance or stages of resistance. Significant positive genetic correlations of antidepressant treatment resistance and stages of resistance with neuroticism, psychological distress, schizotypy and mood disorder traits were identified. These findings suggest that larger sample sizes are needed to identify the genetic architecture of antidepressant treatment response, and that population based observational studies may provide a tractable approach to achieving the necessary statistical power.

U2 - 10.1038/s41397-019-0067-3

DO - 10.1038/s41397-019-0067-3

M3 - Article

SN - 1470-269X

JO - Pharmacogenomics Journal

JF - Pharmacogenomics Journal

ER -

Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP

Abstract / Description of output

Access to Document

Fingerprint

Projects

Leveraging routinely collected and linked research data to study the causes and consequences of common mental disorders

Stratifying Resilience and Depression Longitudinally

RA2661 Centre for Cognitive Ageing and Cognitive Epidemiology Phase 2. Main Budget.

Datasets

Genome-wide association summary statistics of antidepressant treatment resistance meta-analysis

Cite this