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

TLDR

Genome‑wide association studies (GWAS) are widely used to link SNPs with phenotypic traits, especially in social sciences, but require careful statistical analysis and specialized genetics software, and polygenic risk score (PRS) methods aggregate genome‑wide SNP information to generate individual genetic risk scores. The tutorial aims to guide researchers through conducting GWAS and PRS analyses, providing theoretical background and hands‑on scripts to make these methods accessible to those without formal training. The tutorial explains key concepts and demonstrates GWAS and PRS analyses using example scripts on GitHub, built with freely available tools PLINK, PRSice, and R for novice users. Simulated data and accompanying scripts give hands‑on practice, demonstrating that the tutorial effectively makes GWAS and PRS analyses accessible to researchers lacking formal training.

Abstract

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.