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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...
Genetic Screens02:46

Genetic Screens

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.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Polygenic Traits01:18

Polygenic Traits

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...
Polygenic Traits01:18

Polygenic Traits

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...
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...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
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Published on: November 12, 2012

Detecting genetic interactions for quantitative traits with U-statistics.

Ming Li1, Chengyin Ye, Wenjiang Fu

  • 1Department of Epidemiology, Michigan State University, East Lansing, MI 48824, USA.

Genetic Epidemiology
|May 28, 2011
PubMed
Summary
This summary is machine-generated.

A new Forward U-Test method effectively identifies multiple genetic variants associated with complex diseases, outperforming existing approaches. This statistical tool aids in understanding gene-gene interactions for conditions like nicotine dependence.

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Area of Science:

  • Human genetics
  • Statistical genomics
  • Complex disease research

Background:

  • Complex human diseases arise from multiple genetic variants, environmental factors, and their interactions.
  • Existing statistical methods like multifactor dimensionality reduction (MDR) and generalized MDR (GMDR) analyze joint genetic associations but can be computationally intensive.

Purpose of the Study:

  • To introduce a novel statistical method, the Forward U-Test, for evaluating combined genetic effects on quantitative traits.
  • To incorporate gene-gene and gene-environment interactions into the analysis of complex diseases.
  • To assess the performance and computational efficiency of the Forward U-Test compared to GMDR.

Main Methods:

  • A U-statistic-based forward algorithm selects potential disease-susceptibility loci.
  • A weighted U-statistic tests the joint association of selected loci with the trait.
  • Simulation studies and real-world data from the Study of Addiction: Genetics and Environment were used for validation.

Main Results:

  • The Forward U-Test demonstrated greater statistical power than GMDR in simulations.
  • The method is less computationally intensive, suitable for high-dimensional genetic data.
  • Analysis of nicotine dependence identified two SNPs (rs16969968 in CHRNA5 and rs1122530 in NTRK2) jointly associated with dependence levels, with consistent replication across independent datasets.

Conclusions:

  • The Forward U-Test is a powerful and efficient statistical approach for identifying joint genetic effects in complex diseases.
  • Significant gene-gene interaction between CHRNA5 and NTRK2 was found to be associated with nicotine dependence.
  • The findings suggest a potential joint action of CHRNA5 and NTRK2 gene products in the biological pathways underlying nicotine dependence.