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

Fiche du document

Date

2018

Types de document
Périmètre
Langue
Identifiants
Relations

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1017/S0033291717002318

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/pmid/28847336

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/eissn/1469-8978

Ce document est lié à :
info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_7E2AD99A92041

Licences

info:eu-repo/semantics/openAccess , Copying allowed only for non-profit organizations , https://serval.unil.ch/disclaimer




Citer ce document

R.M. Maier et al., « Embracing polygenicity: a review of methods and tools for psychiatric genetics research. », Serveur académique Lausannois, ID : 10.1017/S0033291717002318


Métriques


Partage / Export

Résumé 0

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.

document thumbnail

Par les mêmes auteurs

Sur les mêmes sujets

Exporter en