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Laying a solid foundation for Manhattan--'setting the functional basis for the post-GWAS era'.
Xiaoyang Zhang1, Swneke D Bailey2, Mathieu Lupien3
1Department of Genetics, Norris Cotton Cancer Center, Dartmouth Medical School, Lebanon, NH, USA.
Trends in Genetics : TIG
|March 26, 2014
Summary
Genome-wide association studies (GWAS) have linked thousands of genetic variants to human traits and diseases. Understanding non-coding variants is key to interpreting disease risk and guiding future genetic research.
Area of Science:
- Genetics
- Genomics
- Molecular Biology
Background:
- Genome-wide association studies (GWAS) have identified numerous genetic variants, primarily single-nucleotide polymorphisms (SNPs), linked to human traits and diseases.
- While coding variants' functional impact is often clear, most disease-associated variants reside in non-coding regions, posing interpretation challenges.
Purpose of the Study:
- To review recent advancements in annotating non-coding genomic regions.
- To discuss how these annotations aid in identifying functional targets of GWAS risk loci.
- To explore the implications for interpreting disease-associated mutations from whole-genome sequencing.
Main Methods:
- Review of current literature on non-coding genome annotation and its application in post-GWAS analysis.
- Analysis of how genetic variants modulate cis-regulatory elements to affect gene expression.
- Integration of findings with the interpretation of whole-genome sequencing data for disease mutations.
Main Results:
- Non-coding variants, particularly those affecting cis-regulatory elements, are crucial for understanding gene expression changes.
- Genome annotation facilitates the functional characterization of risk-associated loci identified by GWAS.
- These findings are vital for interpreting disease-associated mutations found through whole-genome sequencing.
Conclusions:
- Understanding non-coding variants is essential for advancing the post-GWAS era.
- Functional annotation of the non-coding genome is critical for disease gene discovery and interpretation.
- This knowledge bridges the gap between genetic associations and biological mechanisms underlying disease.
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