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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...
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,...
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%...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Investigation of Disease Outbreaks01:23

Investigation of Disease Outbreaks

Multistate foodborne outbreaks pose significant public health risks and require meticulous investigation to identify sources and implement control measures. The Centers for Disease Control and Prevention (CDC) utilizes a dynamic seven-step process for these investigations, integrating data from laboratories, interviews, and environmental assessments to protect public health.Outbreak Detection: The detection of multistate outbreaks typically begins with PulseNet, the CDC's national laboratory...
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Pharmacogenomics: Identification of New Drug Targets

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Related Experiment Video

Updated: May 30, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Identifying disease-associated SNP clusters via contiguous outlier detection.

Can Yang1, Xiaowei Zhou, Xiang Wan

  • 1Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.

Bioinformatics (Oxford, England)
|July 26, 2011
PubMed
Summary

We developed Contiguous Outlier DEtection (CODE) to find small-effect disease-associated single-nucleotide polymorphisms (SNPs). CODE uses neighborhood information and graph cuts, improving detection power and identifying replicable SNP clusters.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) identify disease-susceptibility SNPs but explain limited genetic contributions to complex diseases, known as missing heritability.
  • Small-effect genetic variants may be missed due to imperfect linkage disequilibrium and challenges in detecting low-effect causal SNPs.
  • Detecting causal SNPs with small effects is difficult, even when directly genotyped.

Purpose of the Study:

  • To increase statistical power for detecting disease-associated SNPs with small effects.
  • To propose a novel method leveraging neighborhood information for enhanced SNP detection.
  • To address the challenge of missing heritability in complex disease genetics.

Main Methods:

  • Formulated the problem as Contiguous Outlier DEtection (CODE), a discrete optimization problem.
  • Treated disease-associated SNPs as outliers with a spatial continuity constraint, solved using graph cuts.
  • Employed stability selection to control false positives from parameter tuning.

Main Results:

  • Demonstrated the advantage of CODE in simulations and real-world experiments.
  • Successfully identified novel SNP clusters associated with diseases.
  • Validated the replicability of identified SNP clusters across two independent datasets.

Conclusions:

  • CODE effectively increases statistical power for detecting small-effect disease-associated SNPs.
  • The method provides a robust approach to address missing heritability by identifying previously undetected variants.
  • CODE offers a valuable tool for genetic research, with identified SNP clusters showing strong replicability.