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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...
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%...
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,...

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Related Experiment Video

Updated: Jun 14, 2026

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

SNP array analysis in hematologic malignancies: avoiding false discoveries.

Stefan Heinrichs1, Cheng Li, A Thomas Look

  • 1Departmentsof Pediatric Oncology, Farber Cancer Institute, Boston, MA 02115, USA.

Blood
|March 23, 2010
PubMed
Summary
This summary is machine-generated.

Using matched tumor and normal DNA samples in cancer genome analysis significantly reduces false discoveries. This paired approach is crucial for accurate detection of genetic alterations in hematologic malignancies.

Related Experiment Videos

Last Updated: Jun 14, 2026

Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

Area of Science:

  • Genomics
  • Oncology
  • Molecular Biology

Background:

  • Cancer genome analysis is vital for identifying new disease targets and guiding therapies.
  • Single nucleotide polymorphism (SNP) arrays are used in hematologic malignancies to detect genomic alterations like deletions, amplifications, and loss of heterozygosity (LOH).
  • Distinguishing somatic cancer mutations from inherited variations is a key challenge in genomic studies.

Purpose of the Study:

  • To demonstrate the superiority of using matched tumor and normal DNA samples (paired studies) compared to unpaired samples.
  • To reduce false discovery rates in high-resolution single nucleotide polymorphism array analysis for cancer genomes.
  • To highlight the importance of proper controls in identifying somatic deletions and accurate LOH frequencies.

Main Methods:

  • High-resolution single nucleotide polymorphism (SNP) array analysis.
  • Comparison of paired (tumor and normal DNA) versus unpaired DNA samples.
  • Analysis of myeloid malignancies to illustrate methodological differences.

Main Results:

  • Paired studies significantly reduce false discovery rates in SNP array analysis compared to unpaired samples.
  • Failure to use matched normal DNA can lead to misidentification of inherited variations as somatic lesions.
  • Unpaired samples can result in overestimated frequencies of copy-neutral LOH.

Conclusions:

  • Matched tumor and normal DNA sample analysis is superior for accurate cancer genome profiling using SNP arrays.
  • The use of paired samples is critical for reducing false positives and correctly identifying cancer-specific genetic alterations.
  • This methodology is essential for advancing cancer genomics, including the transition to deep sequencing technologies.