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Int J Methods Psychiatr Res. 2018 Jun;27(2):e1608. doi: 10.1002/mpr.1608. Epub 2018 Feb 27.

A tutorial on conducting genome-wide association studies: Quality control and statistical analysis.

Marees AT1,2,3,4,5, de Kluiver H6, Stringer S7, Vorspan F1,2,3,4,8,9, Curis E3,10,11, Marie-Claire C2,3,4, Derks EM1,5.

Author information

1
Department of Psychiatry, Amsterdam Medical Center, Amsterdam, The Netherlands.
2
Inserm, UMR-S 1144, Paris, France.
3
Université Paris Descartes, UMR-S 1144, Paris, France.
4
Université Paris Diderot, Sorbonne Paris Cité, UMR-S 1144, Paris, France.
5
QIMR Berghofer, Translational Neurogenomics Group, Brisbane, Australia.
6
GGZ inGeest and Department of Psychiatry, Amsterdam Public Health research institute, VU University Medical Center, Amsterdam, The Netherlands.
7
Department of Complex Trait Genetics, VU University, Amsterdam, The Netherlands.
8
Service de Médecine Addictologique, APHP, Hôpital Fernand Widal, Paris, France.
9
Faculté de Médecine, Université Paris Diderot, Paris, France.
10
Laboratoire de biomathématiques, faculté de pharmacie de Paris, Université Paris Descartes, Paris, France.
11
Service de biostatistiques et informatique médicales, Hôpital Saint-Louis, APHP, Paris, France.

Abstract

OBJECTIVES:

Genome-wide association studies (GWAS) have become increasingly popular to identify associations between single nucleotide polymorphisms (SNPs) and phenotypic traits. The GWAS method is commonly applied within the social sciences. However, statistical analyses will need to be carefully conducted and the use of dedicated genetics software will be required. This tutorial aims to provide a guideline for conducting genetic analyses.

METHODS:

We discuss and explain key concepts and illustrate how to conduct GWAS using example scripts provided through GitHub (https://github.com/MareesAT/GWA_tutorial/). In addition to the illustration of standard GWAS, we will also show how to apply polygenic risk score (PRS) analysis. PRS does not aim to identify individual SNPs but aggregates information from SNPs across the genome in order to provide individual-level scores of genetic risk.

RESULTS:

The simulated data and scripts that will be illustrated in the current tutorial provide hands-on practice with genetic analyses. The scripts are based on PLINK, PRSice, and R, which are commonly used, freely available software tools that are accessible for novice users.

CONCLUSIONS:

By providing theoretical background and hands-on experience, we aim to make GWAS more accessible to researchers without formal training in the field.

KEYWORDS:

GitHub; PLINK; genome-wide association study (GWAS); polygenic risk score (PRS); tutorial

PMID:
29484742
PMCID:
PMC6001694
DOI:
10.1002/mpr.1608
[Indexed for MEDLINE]
Free PMC Article

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