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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,...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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%...
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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Related Experiment Video

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Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

A multi-array multi-SNP genotyping algorithm for Affymetrix SNP microarrays.

Yuanyuan Xiao1, Mark R Segal, Y H Yang

  • 1Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, San Francisco, CA 94107, USA. yxiao@itsa.ucsf.edu

Bioinformatics (Oxford, England)
|April 27, 2007
PubMed
Summary

We developed a new genotype calling algorithm for Affymetrix SNP arrays that improves accuracy by combining single-array and multi-array analyses. This method is scalable and does not require training data, offering competitive performance for disease locus mapping.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Efficient genotyping is crucial for disease locus mapping.
  • DNA microarray technology, particularly Affymetrix SNP arrays, enables high-throughput interrogation of numerous single nucleotide polymorphisms (SNPs).
  • Analyzing data from these arrays presents platform-specific statistical challenges, especially in combining probe intensities for accurate genotype calls.

Purpose of the Study:

  • To develop an integrated genotype calling algorithm for Affymetrix SNP arrays.
  • To improve the accuracy of genotype calls without requiring training data or intensive normalization.
  • To create a scalable algorithm suitable for the increasing number of SNPs per array.

Main Methods:

  • Developed the Multi-SNP, Multi-Array Genotype Calling (MAMS) algorithm.
  • Integrated single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calling strategies.
  • Employed resampling techniques and model-based clustering for genotype calls, with MASS refinement for atypical SNPs.

Main Results:

  • The MAMS algorithm improves genotype call accuracy.
  • The method is scalable due to its resampling scheme, efficiently handling large numbers of SNPs.
  • Performance is competitive with existing genotyping methods, validated on HapMap data.

Conclusions:

  • The MAMS algorithm offers an accurate and scalable solution for genotype calling on Affymetrix SNP arrays.
  • It effectively refines genotype calls, particularly for challenging SNPs, by integrating information across arrays.
  • The approach provides a valuable tool for genetic studies requiring high-throughput genotyping.