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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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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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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A system-level pathway-phenotype association analysis using synthetic feature random forest.

Qinxin Pan1, Ting Hu, James D Malley

  • 1Department of Genetics, Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, United States of America.

Genetic Epidemiology
|February 19, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new pathway analysis method, synthetic feature random forest (SF-RF), to better understand the biological basis of diseases from genetic data. SF-RF effectively identifies disease-associated pathways and their interactions, offering novel insights for conditions like bladder cancer.

Keywords:
epistasisinteractionspathway analysisstatistical epistasis network (SEN)synthetic feature random forest (SF-RF)

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) generate vast genetic data, but linking findings to biological mechanisms is challenging.
  • Pathway analysis is crucial for interpreting high-throughput genetic data by examining gene sets related to biological processes.
  • Existing methods often overlook complex interactions between single-nucleotide polymorphisms (SNPs) and pathways.

Purpose of the Study:

  • To develop a novel system-level pathway analysis approach that accounts for complex SNP and pathway relationships.
  • To identify biological pathways associated with phenotypes without assuming independence between SNPs or pathways.
  • To investigate interactions among pathways for a deeper understanding of genetic mechanisms.

Main Methods:

  • Proposed synthetic feature random forest (SF-RF) to aggregate SNP genotypes into pathway-level synthetic features using Random Forest (RF).
  • Utilized RF to simultaneously analyze multiple synthetic features, with significance indicating pathway association.
  • Complemented SF-RF with pathway-based Statistical Epistasis Network (SEN) analysis to evaluate pathway interactions.

Main Results:

  • Applied SF-RF and SEN to a bladder cancer genetic study.
  • Identified several pathways significantly associated with bladder cancer.
  • The identified pathways were consistent with existing knowledge and suggested novel hypotheses.

Conclusions:

  • SF-RF provides a robust method for detecting pathway-phenotype associations by capturing complex genetic interactions.
  • SEN analysis enhances understanding of pathway interplay in disease etiology.
  • This approach offers a powerful tool for uncovering biological insights from genetic association studies, with potential applications in various diseases.