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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Revisiting genome-wide association studies from statistical modelling to machine learning.

Shanwen Sun1, Benzhi Dong2, Quan Zou1

  • 1Institute of Fundamental and Frontier Sciences at the University of Electronic Science and Technology of China, Chengdu, China.

Briefings in Bioinformatics
|October 30, 2020
PubMed
Summary

Genome-wide association studies (GWAS) identify genetic variants for diseases and traits. This review explores statistical and machine learning methods to overcome GWAS limitations and enhance variant discovery.

Keywords:
BayesianGWASSNPsepistasisfine-mapmachine learningregression

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWAS) have identified numerous genetic variants linked to complex diseases and traits.
  • Current GWAS methodologies face limitations in detecting epistasis, small-effect SNPs, and distinguishing causal variants from linked SNPs.
  • Advancements are needed to fully leverage the potential of GWAS for biological insights and applications.

Purpose of the Study:

  • To systematically review statistical modeling and machine learning approaches for GWAS.
  • To discuss methods for overcoming existing GWAS limitations.
  • To provide guidelines for robust GWAS analysis and the development of new methods.

Main Methods:

  • Review of statistical modeling techniques in GWAS.
  • Review of machine learning approaches applied to GWAS.
  • Discussion of recent advancements and tools for enhanced GWAS analysis.

Main Results:

  • Both statistical modeling and machine learning offer distinct advantages and limitations for GWAS.
  • Recent efforts focus on mitigating weaknesses in both approaches.
  • State-of-the-art tools exist for detecting missed signals, rare mutations, and gene-gene interactions.

Conclusions:

  • Addressing methodological limitations is crucial for maximizing GWAS potential.
  • Integrating statistical and machine learning methods can improve variant detection and prioritization.
  • This review offers practical guidance for current and future GWAS analyses.