Related Experiment Video
Updated: Jun 12, 2026
05:53
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
TEAM: efficient two-locus epistasis tests in human genome-wide association study.
Xiang Zhang1, Shunping Huang, Fei Zou
1Department of Computer Science, University of North Carolina at Chapel Hill, USA. xiang@cs.unc.edu
Bioinformatics (Oxford, England)
|June 10, 2010
Summary
We developed TEAM, an efficient algorithm for detecting gene-gene interactions in genome-wide association studies (GWAS). TEAM significantly speeds up epistasis detection for large human datasets, overcoming limitations of existing methods.
Area of Science:
- Genetics
- Bioinformatics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) are crucial for identifying genetic markers linked to phenotypic variations.
- Detecting epistasis (gene-gene interactions) is vital for understanding complex traits and diseases, surpassing single-locus studies.
- Current epistasis detection methods struggle with large human datasets due to heterozygous markers and sample size, limiting their applicability.
Purpose of the Study:
- To propose an efficient and exhaustive algorithm, TEAM, for accelerating epistasis detection in human genome-wide association studies.
- To address the computational challenges posed by large sample sizes and heterozygous genotypes in human GWAS.
- To provide a scalable solution for identifying complex gene-gene interactions relevant to human diseases.
Main Methods:
- Developed the TEAM algorithm, which utilizes minimum spanning tree structures for efficient epistasis detection.
- Implemented an incremental update approach for contingency tables, avoiding exhaustive scanning of all individuals.
- Ensured the algorithm supports various contingency table-based statistical tests and error rate controls (family-wise error rate and false discovery rate).
Main Results:
- TEAM achieves significant speed-ups (at least an order of magnitude) compared to brute-force methods for epistasis detection.
- The algorithm efficiently updates contingency tables by examining only a small subset of individuals.
- Demonstrated broader applicability and improved efficiency for large-scale human GWAS datasets.
Conclusions:
- TEAM offers a computationally efficient and exhaustive solution for detecting epistasis in large human GWAS.
- The algorithm overcomes the limitations of existing methods, enabling more comprehensive analysis of complex genetic interactions.
- TEAM enhances the ability to identify genetic markers underlying complex traits and diseases in human populations.
More Related Videos
11:35Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
Published on: August 21, 2016
10:17An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
Related Concept Videos
01:11Genome-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...
GWAS does not require the identification of the target gene involved in...
01:09Epistasis 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...
01:28Human Genetics
Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
The complex relationship between genetics and psychology is observable through common biological components such...
01:29Pharmacogenomics: Identification of New Drug Targets
Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
01:43Incomplete Dominance
Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
01:49Multiple Allele Traits
The Concept of Multiple Allelism