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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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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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Related Experiment Video

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Genome-wide pathway-based quantitative multiple phenotypes analysis.

Yamin Deng1,2, Shiman Wu1, Huifang Fan1

  • 1Statistics Center, First Hospital of Shanxi Medical University, Taiyuan, China.

Plos One
|November 11, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing complex diseases by examining pathways associated with multiple phenotypes. The FC-mSKU approach improves genetic discovery by considering combined effects, unlike single-phenotype analyses.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide pathway association studies are crucial for understanding complex diseases.
  • Current methods often focus on single phenotypes, limiting comprehensive analysis.
  • Complex diseases involve intricate physiological processes that require multi-phenotype evaluation.

Purpose of the Study:

  • To develop a novel model for evaluating pathway associations with multiple phenotypes.
  • To enhance the efficiency of pathway-based association analysis using asymptotic results.
  • To identify pathways specific to single phenotypes versus those contributing to common genetic architectures.

Main Methods:

  • Proposed a semi-supervised maximum kernel-based U-statistics (mSKU) for single-phenotype analysis.
  • Introduced a Fisher combination function for dependent phenotypes (FC) to integrate multiple phenotypes.
  • Applied the FC-mSKU method to real-world data from the Alzheimer Disease Neuroimaging Initiative (ADNI) and Human Liver Cohort (HLC) studies.

Main Results:

  • The FC-mSKU method effectively evaluates pathway associations across multiple phenotypes.
  • Demonstrated the ability to distinguish between phenotype-specific and common genetic pathways.
  • Simulation studies confirmed the advantages of FC-mSKU over existing methods.

Conclusions:

  • The FC-mSKU method offers a more comprehensive approach to pathway-based association analysis for complex diseases.
  • This approach can uncover genetic findings missed by single-phenotype analyses.
  • The method provides valuable insights into the genetic underpinnings of complex diseases by considering multiple phenotypes.