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Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies

To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic hete...

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Autores principales: Schwabe, I., Milaneschi, Y., Gerring, Z., Sullivan, P. F., Schulte, E., Suppli, N. P., Thorp, J. G., Derks, E. M., Middeldorp, C. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6877467/
https://www.ncbi.nlm.nih.gov/pubmed/31559935
http://dx.doi.org/10.1017/S0033291719002502
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author Schwabe, I.
Milaneschi, Y.
Gerring, Z.
Sullivan, P. F.
Schulte, E.
Suppli, N. P.
Thorp, J. G.
Derks, E. M.
Middeldorp, C. M.
author_facet Schwabe, I.
Milaneschi, Y.
Gerring, Z.
Sullivan, P. F.
Schulte, E.
Suppli, N. P.
Thorp, J. G.
Derks, E. M.
Middeldorp, C. M.
author_sort Schwabe, I.
collection PubMed
description To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression.
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spelling pubmed-68774672019-12-04 Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies Schwabe, I. Milaneschi, Y. Gerring, Z. Sullivan, P. F. Schulte, E. Suppli, N. P. Thorp, J. G. Derks, E. M. Middeldorp, C. M. Psychol Med Review Article To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression. Cambridge University Press 2019-12 2019-09-27 /pmc/articles/PMC6877467/ /pubmed/31559935 http://dx.doi.org/10.1017/S0033291719002502 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by/4.0/ This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Schwabe, I.
Milaneschi, Y.
Gerring, Z.
Sullivan, P. F.
Schulte, E.
Suppli, N. P.
Thorp, J. G.
Derks, E. M.
Middeldorp, C. M.
Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies
title Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies
title_full Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies
title_fullStr Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies
title_full_unstemmed Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies
title_short Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies
title_sort unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6877467/
https://www.ncbi.nlm.nih.gov/pubmed/31559935
http://dx.doi.org/10.1017/S0033291719002502
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