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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A Bayesian hierarchical variable selection prior for pathway-based GWAS using summary statistics.

Yi Yang1, Saonli Basu1, Lin Zhang1

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota.

Statistics in Medicine
|November 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Bayesian method for pathway-based genome-wide association studies (GWASs). The method effectively identifies disease-associated pathways by leveraging genetic variant data and prior biological knowledge, outperforming existing approaches.

Keywords:
generalized fused lassogroup lassohierarchical variable selectionpathway-based GWASsummary statistics

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Standard SNP-level genome-wide association studies (GWASs) may lack power to detect variants with moderate effect sizes that collectively influence disease risk.
  • Pathway-based GWAS methods offer a complementary strategy to enhance the detection of complex genetic contributions to diseases.

Purpose of the Study:

  • To develop and validate a novel Bayesian method for pathway-based GWASs.
  • To identify disease-associated pathways using SNP-level summary statistics and incorporating pathway structural information.

Main Methods:

  • A Bayesian approach utilizing a generalized fused hierarchical structured variable selection prior.
  • Incorporation of pathway structural information to model gene-level correlations based on prior biological knowledge.
  • Simulation studies to compare performance against existing methods.

Main Results:

  • The proposed Bayesian method demonstrated superior performance compared to competing methods in various simulation scenarios.
  • Performance was particularly enhanced when pathway structural information, including complex gene-gene interactions, was utilized.
  • Successful application to real-world Crohn's disease GWAS data from the Wellcome Trust Case Control Consortium.

Conclusions:

  • The developed Bayesian method offers a powerful and flexible approach for pathway-based GWASs.
  • Integrating prior biological knowledge and pathway structure improves the identification of disease-associated pathways.
  • The method provides a valuable tool for dissecting the genetic architecture of complex diseases.