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Substantial SNP-based heritability estimates for working memory performance

Working memory (WM) is an important endophenotype in neuropsychiatric research and its use in genetic association studies is thought to be a promising approach to increase our understanding of psychiatric disease. As for any genetically complex trait, demonstration of sufficient heritability within...

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Autores principales: Vogler, C, Gschwind, L, Coynel, D, Freytag, V, Milnik, A, Egli, T, Heck, A, de Quervain, D J-F, Papassotiropoulos, A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4203010/
https://www.ncbi.nlm.nih.gov/pubmed/25203169
http://dx.doi.org/10.1038/tp.2014.81
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author Vogler, C
Gschwind, L
Coynel, D
Freytag, V
Milnik, A
Egli, T
Heck, A
de Quervain, D J-F
Papassotiropoulos, A
author_facet Vogler, C
Gschwind, L
Coynel, D
Freytag, V
Milnik, A
Egli, T
Heck, A
de Quervain, D J-F
Papassotiropoulos, A
author_sort Vogler, C
collection PubMed
description Working memory (WM) is an important endophenotype in neuropsychiatric research and its use in genetic association studies is thought to be a promising approach to increase our understanding of psychiatric disease. As for any genetically complex trait, demonstration of sufficient heritability within the specific study context is a prerequisite for conducting genetic studies of that trait. Recently developed methods allow estimating trait heritability using sets of common genetic markers from genome-wide association study (GWAS) data in samples of unrelated individuals. Here we present single-nucleotide polymorphism (SNP)-based heritability estimates (h(2)(SNP)) for a WM phenotype. A Caucasian sample comprising a total of N=2298 healthy and young individuals was subjected to an N-back WM task. We calculated the genetic relationship between all individuals on the basis of genome-wide SNP data and performed restricted maximum likelihood analyses for variance component estimation to derive the h(2)(SNP) estimates. Heritability estimates for three 2-back derived WM performance measures based on all autosomal chromosomes ranged between 31 and 41%, indicating a substantial SNP-based heritability for WM traits. These results indicate that common genetic factors account for a prominent part of the phenotypic variation in WM performance. Hence, the application of GWAS on WM phenotypes is a valid method to identify the molecular underpinnings of WM.
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spelling pubmed-42030102014-11-06 Substantial SNP-based heritability estimates for working memory performance Vogler, C Gschwind, L Coynel, D Freytag, V Milnik, A Egli, T Heck, A de Quervain, D J-F Papassotiropoulos, A Transl Psychiatry Original Article Working memory (WM) is an important endophenotype in neuropsychiatric research and its use in genetic association studies is thought to be a promising approach to increase our understanding of psychiatric disease. As for any genetically complex trait, demonstration of sufficient heritability within the specific study context is a prerequisite for conducting genetic studies of that trait. Recently developed methods allow estimating trait heritability using sets of common genetic markers from genome-wide association study (GWAS) data in samples of unrelated individuals. Here we present single-nucleotide polymorphism (SNP)-based heritability estimates (h(2)(SNP)) for a WM phenotype. A Caucasian sample comprising a total of N=2298 healthy and young individuals was subjected to an N-back WM task. We calculated the genetic relationship between all individuals on the basis of genome-wide SNP data and performed restricted maximum likelihood analyses for variance component estimation to derive the h(2)(SNP) estimates. Heritability estimates for three 2-back derived WM performance measures based on all autosomal chromosomes ranged between 31 and 41%, indicating a substantial SNP-based heritability for WM traits. These results indicate that common genetic factors account for a prominent part of the phenotypic variation in WM performance. Hence, the application of GWAS on WM phenotypes is a valid method to identify the molecular underpinnings of WM. Nature Publishing Group 2014-09 2014-09-09 /pmc/articles/PMC4203010/ /pubmed/25203169 http://dx.doi.org/10.1038/tp.2014.81 Text en Copyright © 2014 Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-nd/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Original Article
Vogler, C
Gschwind, L
Coynel, D
Freytag, V
Milnik, A
Egli, T
Heck, A
de Quervain, D J-F
Papassotiropoulos, A
Substantial SNP-based heritability estimates for working memory performance
title Substantial SNP-based heritability estimates for working memory performance
title_full Substantial SNP-based heritability estimates for working memory performance
title_fullStr Substantial SNP-based heritability estimates for working memory performance
title_full_unstemmed Substantial SNP-based heritability estimates for working memory performance
title_short Substantial SNP-based heritability estimates for working memory performance
title_sort substantial snp-based heritability estimates for working memory performance
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4203010/
https://www.ncbi.nlm.nih.gov/pubmed/25203169
http://dx.doi.org/10.1038/tp.2014.81
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