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Related Concept Videos

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...
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...
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,...
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...
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Random forests for genetic association studies.

Benjamin A Goldstein1, Eric C Polley, Farren B S Briggs

  • 1Quantitative Sciences Unit, Department of Medicine, Stanford University, USA.

Statistical Applications in Genetics and Molecular Biology
|August 15, 2012
PubMed
Summary
This summary is machine-generated.

Random Forests (RF) is a powerful machine learning tool for genetic association studies. This review clarifies RF

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Random Forests (RF) is increasingly utilized in genetic association studies due to its computational efficiency and ability to model complex genetic mechanisms.
  • Inconsistent application of RF in existing literature necessitates a clearer understanding of its theoretical and statistical underpinnings.

Purpose of the Study:

  • To provide a comprehensive review of the theoretical and statistical basis of the Random Forests algorithm.
  • To guide practitioners in the optimal application of RF for genetic studies.
  • To elucidate the impact of RF components on bias and variance, and to discuss variable importance measures.

Main Methods:

  • Review of the theoretical framework of Random Forests.
  • Statistical analysis of bias and variance in RF models.
  • Evaluation of variable importance metrics within the RF context.
  • Comparative analysis of RF against other machine learning algorithms.

Main Results:

  • Detailed explanation of how Random Forests' components influence model bias and variance.
  • Discussion on the interpretation and application of variable importance measures derived from RF.
  • Highlighting specific applications and considerations for Random Forests in genetic association studies.

Conclusions:

  • A thorough understanding of RF's statistical properties is crucial for its effective implementation in genetic research.
  • This review aims to standardize and improve the application of Random Forests, enhancing the reliability of genetic association findings.
  • Comparison with other algorithms provides context for selecting appropriate machine learning methods in genetics.