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13:33
Infinium Assay for Large-scale SNP Genotyping Applications
Published on: November 19, 2013
Clinical utility of single nucleotide polymorphism arrays
1Cytogenetics Laboratory, Laboratory Corporation of America, 1904 Alexander Drive, Research Triangle Park, NC 27709, USA. schwas1@labcorp.com
Clinics in Laboratory Medicine
|November 29, 2011
Summary
Single nucleotide polymorphism (SNP) arrays offer 100x greater resolution for detecting chromosomal abnormalities than traditional methods. This technology enables precise copy number and copy-neutral analyses for various genetic studies.
Area of Science:
- Genetics
- Molecular Biology
- Bioinformatics
Background:
- Chromosomal abnormality detection has advanced significantly over 30 years.
- Microarray analysis provides 100-fold higher resolution compared to conventional chromosomal analysis.
- Single nucleotide polymorphism (SNP) arrays represent a key advancement in microarray technology.
Purpose of the Study:
- To review the capabilities of single nucleotide polymorphism (SNP) arrays for detecting chromosomal abnormalities.
- To highlight the utility of SNP arrays in various clinical and research applications.
- To discuss the advantages of SNP arrays over traditional methods.
Main Methods:
- Review of single nucleotide polymorphism (SNP) array technology.
- Analysis of copy number variations (CNVs) using SNP arrays.
- Genotyping for copy-neutral abnormalities including uniparental disomy and consanguinity.
Main Results:
- SNP arrays offer enhanced resolution for detecting copy number changes.
- The genotyping feature of SNP arrays allows for the detection of copy-neutral abnormalities.
- Applications span constitutional (postnatal, prenatal, products of conception) and oncology studies.
Conclusions:
- Single nucleotide polymorphism (SNP) arrays provide superior resolution and comprehensive detection of chromosomal abnormalities.
- This technology is valuable for constitutional genetic testing and cancer research.
- SNP arrays represent a significant improvement in genomic analysis.
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