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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,...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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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

Extra-binomial variation approach for analysis of pooled DNA sequencing data.

Xin Yang1, John A Todd, David Clayton

  • 1Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Wellcome Trust/MRC Building, Addenbrooke's Hospital, Cambridge CB2 0XY, UK.

Bioinformatics (Oxford, England)
|September 15, 2012
PubMed
Summary

A new statistical method using an extra-binomial model improves the analysis of pooled sequencing data. This approach better handles over-dispersion, leading to more accurate identification of disease-causing genes from next-generation sequencing studies.

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Area of Science:

  • Genomics
  • Statistical Genetics

Background:

  • Next-generation sequencing enables rare variant analysis for disease gene discovery.
  • DNA pooling in sequencing experiments introduces significant between-pool variation and over-dispersion.
  • Accurate statistical modeling is crucial to manage over-dispersion and avoid false positives in pooled sequencing data.

Purpose of the Study:

  • To develop and apply a novel statistical method for analyzing over-dispersed pooled sequencing data.
  • To address the challenges of between-pool variation in case-control studies using pooled DNA.
  • To improve the accuracy of variant detection in next-generation sequencing studies.

Main Methods:

  • Development of a new statistical method based on an extra-binomial model.
  • Application of the model to pooled case-control sequencing data.
  • Comparison with standard binomial and traditional extra-binomial models.

Main Results:

  • The proposed extra-binomial model provides a superior fit to pooled sequencing data compared to existing methods.
  • The model effectively handles over-dispersion inherent in pooled sequencing.
  • Accurate analysis of both rare and common variants is achieved, even with variable pool depths.

Conclusions:

  • The new extra-binomial model offers a robust solution for analyzing over-dispersed pooled sequencing data.
  • This method enhances the reliability of identifying causal disease genes through rare variant analysis.
  • The approach is suitable for diverse variant types and varying experimental conditions in pooled sequencing studies.