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Related Experiment Video

Updated: Apr 7, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Strategies for fine-mapping complex traits.

Sarah L Spain¹, Jeffrey C Barrett²

¹Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1HH, UK.

Human Molecular Genetics

|July 10, 2015

Summary

This summary is machine-generated.

Identifying causal genetic variants for complex diseases remains challenging. New methods combine statistical association data with functional genomics to pinpoint these variants and their target genes, improving disease risk understanding.

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Area of Science:

Genetics
Genomics
Bioinformatics

Background:

Genome-wide association studies (GWAS) have identified numerous genetic associations for complex diseases.
Pinpointing the specific causal variants responsible for these associations is a significant challenge in genetic research.

Purpose of the Study:

To review recent advancements in fine-mapping genetic association signals.
To highlight the integration of statistical and functional genomic approaches for identifying causal variants and target genes.

Main Methods:

Utilizing statistical evidence from large genetic association datasets.
Incorporating functional genomic annotations for both coding and non-coding genetic variations.
Analyzing data from custom genotyping arrays like Immunochip.

Main Results:

The combination of statistical and functional data enables the identification of causal variants and their associated target genes.
Advanced statistical methods help define credible sets of causal variants.
Functional annotations prioritize variants and elucidate their role in disease risk.

Conclusions:

Integrating statistical association data with functional genomics is crucial for fine-mapping complex disease risk variants.
This integrated approach enhances the understanding of the biological basis of disease.
Future research can leverage these methods for more precise genetic risk prediction.