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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,...

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Updated: Jun 17, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Evaluation of single-nucleotide polymorphism imputation using random forests.

Daniel F Schwarz¹, Silke Szymczak, Andreas Ziegler

¹Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Scleswig-Holstein, Campus Lübeck, Maria-Goeppert-Str, 1, 23562 Lübeck, Germany. schwarz@imbs.uni-luebeck.de.

BMC Proceedings

|December 19, 2009

Summary

This summary is machine-generated.

Random forests (RF) offer a faster method for imputing missing genotypes in genome-wide association studies (GWAS). However, this approach proved less accurate than other methods for identifying single-nucleotide polymorphisms (SNPs).

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Published on: August 3, 2018

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies (GWAS) are crucial for understanding complex diseases.
Genotyping technologies identify millions of single-nucleotide polymorphisms (SNPs), but causative variants are often not directly genotyped.
Imputing untyped SNPs using reference populations enhances GWAS power.

Purpose of the Study:

To evaluate the efficacy of Random Forests (RF) as an internal method for imputing untyped SNPs.
To compare the speed and accuracy of RF imputation against alternative methods.

Main Methods:

Utilized Random Forests (RF) classification trees to assess proband genotype similarities.
Employed genotype data from the Framingham Heart Study and Caucasian HapMap samples as a reference population.
Applied RF proximities to impute genotypes for untyped SNPs.

Main Results:

Random Forests (RF) demonstrated a faster imputation process compared to alternative approaches.
The accuracy of RF for imputing untyped SNPs was found to be lower than other methods.
Evaluation was performed using Framingham Heart Study data and HapMap reference samples.

Conclusions:

Random Forests (RF) present a computationally efficient option for SNP imputation in GWAS.
Further research may be needed to improve the accuracy of RF-based imputation methods.
The trade-off between speed and accuracy should be considered when selecting imputation techniques.