Embracing polygenicity: a review of methods and tools for psychiatric genetics research.

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State: Public
Version: Final published version
Serval ID
serval:BIB_7E2AD99A9204
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
Embracing polygenicity: a review of methods and tools for psychiatric genetics research.
Journal
Psychological Medicine
Author(s)
Maier R.M., Visscher P.M., Robinson M.R., Wray N.R.
ISSN
1469-8978 (Electronic)
ISSN-L
0033-2917
Publication state
Published
Issued date
2018
Peer-reviewed
Oui
Volume
48
Number
7
Pages
1055-1067
Language
english
Abstract
The availability of genome-wide genetic data on hundreds of thousands of people has led to an equally rapid growth in methodologies available to analyse these data. While the motivation for undertaking genome-wide association studies (GWAS) is identification of genetic markers associated with complex traits, once generated these data can be used for many other analyses. GWAS have demonstrated that complex traits exhibit a highly polygenic genetic architecture, often with shared genetic risk factors across traits. New methods to analyse data from GWAS are increasingly being used to address a diverse set of questions about the aetiology of complex traits and diseases, including psychiatric disorders. Here, we give an overview of some of these methods and present examples of how they have contributed to our understanding of psychiatric disorders. We consider: (i) estimation of the extent of genetic influence on traits, (ii) uncovering of shared genetic control between traits, (iii) predictions of genetic risk for individuals, (iv) uncovering of causal relationships between traits, (v) identifying causal single-nucleotide polymorphisms and genes or (vi) the detection of genetic heterogeneity. This classification helps organise the large number of recently developed methods, although some could be placed in more than one category. While some methods require GWAS data on individual people, others simply use GWAS summary statistics data, allowing novel well-powered analyses to be conducted at a low computational burden.

Keywords
Genetics, methods, polygenic, review
Pubmed
Web of science
Open Access
Yes
Create date
06/12/2017 12:42
Last modification date
20/08/2019 14:39
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