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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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PCGA: a comprehensive web server for phenotype-cell-gene association analysis.

Chao Xue1, Lin Jiang2, Miao Zhou1

  • 1Program in Bioinformatics, Zhongshan School of Medicine and The Fifth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.

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Summary

PCGA is a new web server that identifies disease-associated genes and tissues using genome-wide association studies (GWAS) summary statistics. It integrates transcriptome data to pinpoint regulatory elements for complex traits.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) often identify disease-associated loci in non-coding regions.
  • Identifying the specific genes and cell types regulated by these loci remains a challenge.

Purpose of the Study:

  • To develop a comprehensive web server, PCGA, for jointly estimating associated tissues/cell types and susceptibility genes for complex phenotypes from GWAS summary statistics.
  • To provide a searchable landscape of phenotype-cell-gene associations.

Main Methods:

  • Utilized the published DESE method integrating GWAS summary statistics and transcriptome data.
  • Processed extensive bulk and single-cell RNA sequencing datasets for human and mouse cell types.
  • Developed a framework for sequentially estimating associated tissues and cell types based on curated hierarchical relationships.

Main Results:

  • PCGA integrates expression profiles from 54 human tissues, 2,214 human cell types, and 4,384 mouse cell types.
  • Constructed a phenotype-cell-gene association landscape for 1,871 public GWAS datasets.
  • The generated landscape demonstrates consistency with existing biological knowledge.

Conclusions:

  • PCGA serves as a valuable resource for understanding the genetic architecture of complex diseases.
  • The web server facilitates the identification of regulatory mechanisms underlying complex traits by linking GWAS loci to specific genes and cell types.