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

Dennis J Hazelett1, David V Conti2, Ying Han2

  • 1a Bioinformatics and Computational Biology Research Center, Biomedical Sciences, Cedars-Sinai Medical Center , Los Angeles , CA , USA.

Cell Cycle (Georgetown, Tex.)
|January 16, 2016
PubMed
Summary
This summary is machine-generated.

Genome-wide association studies (GWAS) identify genomic regions linked to complex traits. This study proposes matching risk single nucleotide polymorphisms (SNPs) to chromatin features to pinpoint functional variants, simplifying downstream research.

Keywords:
CancerGWASSNPchromatinenhancerfine-mappingnon-coding DNA

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Area of Science:

  • Genetics
  • Genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) have identified numerous genetic loci associated with complex phenotypes.
  • Identifying the causal variants within these loci is challenging, as most associated single nucleotide polymorphisms (SNPs) are not in coding genes.

Purpose of the Study:

  • To reduce the complexity of identifying functional/causal SNPs from GWAS.
  • To propose a method for prioritizing candidate SNPs for mechanistic studies.

Main Methods:

  • Fine-mapping of GWAS risk loci.
  • Matching risk SNPs to regulatory elements and chromatin biofeatures.
  • Prioritizing candidate SNPs based on their association with chromatin states.

Main Results:

  • The proposed method significantly reduces the number of candidate functional/causal SNPs.
  • On average, only two SNPs per prostate cancer risk locus are identified as likely candidates.
  • This manageable number facilitates further mechanistic investigation.

Conclusions:

  • Matching risk SNPs to chromatin biofeatures is an effective strategy for prioritizing functional variants.
  • This approach simplifies the interpretation of GWAS findings for complex traits.
  • The identified candidate SNPs provide a focused set for future mechanistic studies.