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Polygenic Traits01:18

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Genome-wide Association Studies-GWAS01:11

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Related Experiment Videos

PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods.

Chen Cao1, Shuting Zhang1, Jianhua Wang2

  • 1Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China.

Nucleic Acids Research
|November 12, 2023
PubMed
Summary

PGS-Depot offers a comprehensive resource for polygenic scores (PGS) using GWAS summary statistics. It standardizes PGS computation and comparison across multiple methods and populations, simplifying genetic prediction research.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Polygenic scores (PGS) are crucial for predicting complex traits genetically.
  • Existing resources lack comprehensive PGS computed from published summary statistics.
  • Implementing diverse PGS methods is challenging due to pipeline complexity.

Purpose of the Study:

  • Introduce PGS-Depot, a novel resource for publicly available disease-related GWAS summary statistics.
  • Standardize the computation and comparison of PGS across various methods and populations.
  • Facilitate genetic prediction research by providing accessible tools and data.

Main Methods:

  • Curated 5585 high-quality GWAS summary statistics (1933 quantitative, 3652 binary) for 1564 traits.
  • Implemented 11 summary statistics-based PGS methods using a standardized pipeline.
  • Enabled in- and cross-ancestry prediction performance comparison and custom PGS generation.

Main Results:

  • PGS-Depot provides a comprehensive collection of disease-related GWAS summary statistics.
  • A standardized pipeline facilitates the application and comparison of 11 PGS methods.
  • Users can download scores, effect sizes, and summary statistics, and search by various criteria.

Conclusions:

  • PGS-Depot addresses the need for a centralized, standardized resource for polygenic scores.
  • The platform simplifies the implementation and comparison of PGS methods.
  • It empowers researchers with accessible data and tools for genetic prediction studies.