Cargando…

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...

Descripción completa

Detalles Bibliográficos
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
Descripción
Sumario: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.