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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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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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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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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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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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GESLM algorithm for detecting causal SNPs in GWAS with multiple phenotypes.

Ruiqi Lyu1, Jianle Sun1, Dong Xu1

  • 1Shanghai Jiao Tong University, Department of Bioinformatics and Biostatistics, Shanghai, 200240, China.

Briefings in Bioinformatics
|July 29, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces Greedy Equivalence Search with Local Modification (GESLM), a new algorithm for identifying genome-wide epistasis (gene interactions) across multiple phenotypes. GESLM improves accuracy and efficiency in genetic association studies, especially with complex data.

Keywords:
DAGGWASglobal searchmultiple-phenotype analysis

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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) generate large datasets, posing challenges for traditional analysis methods.
  • Identifying loci-to-loci interactions (epistasis) is crucial but often overlooked by standard approaches.
  • Previous epistasis studies typically focused on single phenotypes and local genetic information.

Purpose of the Study:

  • To develop a novel algorithm, Greedy Equivalence Search with Local Modification (GESLM), for global search of epistatic interactions.
  • To identify genome-wide epistasis involving multiple phenotypes within a case-control study design.
  • To integrate score-based and constraint-based methods for learning phenotype-related Bayesian networks.

Main Methods:

  • Developed a two-stage global search algorithm, GESLM.
  • Implemented a global search of directed acyclic graphs to detect epistasis.
  • Integrated score-based and constraint-based methods to learn phenotype-associated Bayesian networks.

Main Results:

  • GESLM demonstrated improved accuracy and efficiency compared to common phenotype-related loci detection methods in simulations.
  • The algorithm showed particular robustness in unbalanced case-control studies.
  • Application to the UK Biobank dataset confirmed GESLM's strong performance with multi-phenotype GWAS data.

Conclusions:

  • GESLM is a powerful and robust method for identifying genome-wide epistatic interactions associated with multiple phenotypes.
  • The algorithm effectively captures complex genetic architectures, including gene-gene interactions.
  • GESLM offers a significant advancement for analyzing large-scale GWAS data with multiple outcome variables.