Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-13T18:48:27.106Z Has data issue: false hasContentIssue false
  • English
  • Français

Consequences of a network view for genetic association studies

Published online by Cambridge University Press:  29 June 2010

Sophie van der Sluis ,
Kees-Jan Kan  and
Conor V. Dolan
Sophie van der Sluis
Affiliation:
Department of Functional Genomics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije University Amsterdam, 1081 HV Amsterdam, The Netherlands. sophie.van.der.sluis@cncr.vu.nl
Kees-Jan Kan
Affiliation:
Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, 1018 WB Amsterdam, The Netherlands. k.j.kan@uva.nlhttp://home.uva.nl/kees-jan.kan/c.v.dolan@uva.nlhttp://users.fmg.uva.nl/cdolan/
Conor V. Dolan
Affiliation:
Department of Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, 1018 WB Amsterdam, The Netherlands. k.j.kan@uva.nlhttp://home.uva.nl/kees-jan.kan/c.v.dolan@uva.nlhttp://users.fmg.uva.nl/cdolan/
Get access Rights & Permissions [Opens in a new window]

Abstract

Cramer et al's proposal to view mental disorders as the outcome of network dynamics among symptoms obviates the need to invoke latent traits to explain co-occurrence of symptoms and syndromes. This commentary considers the consequences of such a network view for genetic association studies.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 33 , Issue 2-3 , June 2010 , pp. 173 - 174
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Balding, D. J. (2006) A tutorial on statistical methods for population association studies. Nature Reviews: Genetics 7(10):781–91.CrossRefGoogle ScholarPubMed
Barrett, J. C., Clayton, D. G., Concannon, P., Akolkar, B., Cooper, J. D., Erlich, H. A., Julier, C., Morahan, G., Nerup, J., Nierras, C., Plagnol, V., Pociot, F., Schuilenburg, H., Smyth, D. J., Stevens, H., Todd, J. A., Walker, N. M., Rich, S. S. & the Type 1 Diabetes Genetics Consortium. (2009) Genome-wide association study and meta-analysis find that over 40 loci affect risk of Type 1 diabetes. Nature Genetics 41(6):703–707.CrossRefGoogle ScholarPubMed
Falconer, D. S. (1989) Introduction to quantitative genetics. Longman.Google Scholar
Hirschhorn, J. N. & Daly, M. J. (2005) Genome-wide association studies for common diseases and complex traits. Nature Reviews: Genetics 6(2):95–108.CrossRefGoogle ScholarPubMed
Lux, V. & Kendler, K. S. (in press) Deconstructing major depression: A validation study of the DSM-IV symptomatic criteria. Psychological Medicine. [Epub: January 11, 2010; doi: 10.1017/S0033291709992157].Google Scholar
Purcell, S. (2002) Variance components models for gene-environment interaction in twin analysis. Twin Research 5(6):554–71.CrossRefGoogle ScholarPubMed
Van der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H. M. & Raijmakers, M. E. J. (2006) A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review 113(4):842–61.CrossRefGoogle ScholarPubMed
Zhang, H. F., Qiu, L. X., Chen, Y., Zu, W. L., Mao, C., Zhu, L. G., Zheng, M. H., Wang, Y., Lei, L. & Shi, J. (2009) ATG16L1 T300A polymorphism and Crohn's disease susceptibility: Evidence from 13.022 cases and 17.532 controls. Human Genetics 125(5–6):627–31.CrossRefGoogle Scholar