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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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...

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Related Experiment Videos

SNP interaction detection with Random Forests in high-dimensional genetic data.

Stacey J Winham1, Colin L Colby, Robert R Freimuth

  • 1Department of Health Sciences Research, Mayo Clinic, 200 First Street Southwest, Rochester, MN 55905, USA. winham.stacey@mayo.edu

BMC Bioinformatics
|July 17, 2012
PubMed
Summary
This summary is machine-generated.

Random Forests (RF) can identify gene interactions in low-dimensional data. However, in high-dimensional genome-wide association studies, RF variable importance measures often fail to detect these interactions, limiting their use as a filtering technique.

Related Experiment Videos

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) aim to identify genetic variants linked to complex human traits.
  • Univariate analysis in GWAS often overlooks gene-gene interactions, a key factor in complex trait etiology.
  • Random Forests (RF) offer a data-mining approach capable of modeling interactions in high-dimensional datasets.

Purpose of the Study:

  • To investigate the effectiveness of RF variable importance measures in detecting gene-gene interactions within high-dimensional genetic data.
  • To compare the performance of RF-based filtering against traditional p-values from univariate logistic regression, especially under increasing data dimensionality.

Main Methods:

  • Utilized Random Forests (RF) analysis to assess variable importance for single nucleotide polymorphisms (SNPs).
  • Evaluated the power of RF variable importance rankings to detect gene-gene interaction effects.
  • Compared RF performance with p-values derived from univariate logistic regression in simulated high-dimensional datasets.

Main Results:

  • RF successfully identified interactions in low-dimensional datasets.
  • As data dimensionality increased, the detection probability for interacting SNPs decreased more rapidly than for non-interacting SNPs.
  • RF variable importance measures in high-dimensional data primarily captured marginal effects, not interaction effects.

Conclusions:

  • RF is a valuable technique for analyzing multiple variables simultaneously, extending beyond univariate methods.
  • RF variable importance measures are not effective for detecting gene-gene interactions in high-dimensional genomic data without a strong marginal effect.
  • The utility of RF as a filtering approach for identifying interactions in genome-wide data is limited.