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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
Epistasis01:39

Epistasis

In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Pleiotropy01:33

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...

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Related Experiment Video

Updated: Jun 4, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

eCEO: an efficient Cloud Epistasis cOmputing model in genome-wide association study.

Zhengkui Wang1, Yue Wang, Kian-Lee Tan

  • 1NUS Graduate School for Integrative Sciences and Engineering, Department of Computer Science, School of Computing, National University of Singapore, Singapore and Department of Computer Science, University of California, Santa Barbara, 93106-5110, USA. wangzhengkui@nus.edu.sg

Bioinformatics (Oxford, England)
|March 4, 2011
PubMed
Summary
This summary is machine-generated.

We developed an efficient Cloud-based Epistasis cOmputing (eCEO) model to address the computational challenges of identifying epistatic interactions between single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS). The eCEO model demonstrates efficiency, scalability, and practicality for large-scale genetic analyses.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying epistatic interactions among numerous single nucleotide polymorphisms (SNPs) is crucial for understanding complex phenotypes but poses significant computational challenges.
  • Genome-wide association studies (GWAS) aim to uncover genetic variants associated with diseases, where epistasis plays a vital role.

Purpose of the Study:

  • To develop an efficient and effective computational model for identifying epistatic interactions of SNPs in large-scale GWAS.
  • To overcome the computational burden associated with analyzing vast numbers of SNP combinations.

Main Methods:

  • Propose an efficient Cloud-based Epistasis cOmputing (eCEO) model for large-scale epistatic interaction analysis.
  • Implement a load-balancing strategy to distribute SNP combinations across processing nodes.
  • Develop efficient methods for processing SNP combinations and assessing their statistical significance.

Main Results:

  • The eCEO model demonstrates computational efficiency, flexibility, scalability, and practicality.
  • Evaluation on a private cluster (40+ nodes) and Amazon Elastic Compute Cloud confirms the model's performance.
  • The model effectively identifies significant epistatic interactions in GWAS data.

Conclusions:

  • The eCEO model provides an efficient and scalable solution for detecting epistasis in large-scale GWAS.
  • Cloud computing offers a practical platform for computationally intensive genetic analyses.
  • The developed model facilitates deeper insights into genotype-phenotype relationships through epistasis analysis.