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Related Experiment Video

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A tutorial on conducting genome-wide association studies: Quality control and statistical analysis.

Andries T Marees1,2,3,4,5, Hilde de Kluiver6, Sven Stringer7

  • 1Department of Psychiatry, Amsterdam Medical Center, Amsterdam, The Netherlands.

International Journal of Methods in Psychiatric Research
|February 28, 2018
PubMed
Summary

This tutorial guides researchers through genome-wide association studies (GWAS) and polygenic risk score (PRS) analysis. It provides hands-on practice with genetic analysis software for identifying SNP-trait associations.

Keywords:
GitHubPLINKgenome-wide association study (GWAS)polygenic risk score (PRS)tutorial

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Area of Science:

  • Genetics
  • Bioinformatics
  • Social Sciences

Background:

  • Genome-wide association studies (GWAS) identify links between single nucleotide polymorphisms (SNPs) and traits.
  • GWAS is increasingly used in social sciences, requiring careful statistical analysis and specialized software.
  • This tutorial offers a practical guide for conducting genetic analyses.

Purpose of the Study:

  • To provide a guideline for conducting genetic analyses, including standard GWAS and polygenic risk score (PRS) analysis.
  • To make GWAS more accessible to researchers without formal genetics training.
  • To offer theoretical background and hands-on experience with genetic analysis tools.

Main Methods:

  • Explanation of key concepts in GWAS and PRS analysis.
  • Illustration of standard GWAS and polygenic risk score (PRS) analysis using example scripts.
  • Utilizing freely available software: PLINK, PRSice, and R.

Main Results:

  • Provided simulated data and example scripts for hands-on practice in genetic analyses.
  • Demonstrated the application of standard GWAS and PRS analysis.
  • Scripts are accessible for novice users.

Conclusions:

  • The tutorial aims to demystify GWAS and PRS analysis for a broader research audience.
  • Hands-on experience with provided data and scripts enhances understanding and application.
  • Researchers can gain practical skills in genetic analysis without extensive prior training.