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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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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Hierarchical structural component model for pathway analysis of common variants.

Nan Jiang1, Sungyoung Lee2, Taesung Park3,4

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea.

BMC Medical Genomics
|February 26, 2020
PubMed
Summary

This study introduces HisCoM-PCA, a new method for analyzing genetic data from genome-wide association studies (GWAS). It effectively identifies significant biological pathways, helping to solve the missing heritability problem in genetics.

Keywords:
Common variantsGenome-wide association studyHierarchical componentsPathway analysis

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to traits but explain limited heritability.
  • Existing gene and pathway analyses often ignore correlations between genes and pathways.
  • The 'missing heritability' in GWAS remains a significant challenge in genetic research.

Purpose of the Study:

  • To develop a novel pathway analysis method for GWAS datasets that accounts for gene and pathway correlations.
  • To address the 'missing heritability' by integrating biological pathways into genetic analysis.
  • To provide a method with intuitive biological interpretation for variant-phenotype associations.

Main Methods:

  • Constructed a hierarchical component model accounting for gene and pathway correlations.
  • Developed Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA).
  • Summarized common variants at the gene level, then analyzed pathways simultaneously using ridge-type penalization and permutation tests.

Main Results:

  • HisCoM-PCA demonstrated controlled type I error and higher empirical power in simulation studies (GAW17).
  • Applied to KARE dataset for type 2 diabetes, hypertension, and blood pressure traits.
  • Successfully identified significant biological pathways with superior statistical and biological relevance.

Conclusions:

  • HisCoM-PCA offers an intuitive biological interpretation of common variant-phenotype associations.
  • The method aids in addressing the missing heritability conundrum by leveraging pathway information.
  • HisCoM-PCA provides a robust framework for pathway analysis in GWAS.