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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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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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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.
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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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.
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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A unified sparse representation for sequence variant identification for complex traits.

Shaolong Cao1, Huaizhen Qin, Hong-Wen Deng

  • 1Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, United States of America; Center for Bioinformatics and Genomics, Tulane University, New Orleans, Louisiana, United States of America.

Genetic Epidemiology
|September 9, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a unified sparse regression (USR) method to jointly analyze genetic data, improving the identification of causal variants for complex diseases like hypertension by incorporating prior biological information.

Keywords:
Mexican Americanspopulation structureprior biological informationrelatednesssparse regression

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Area of Science:

  • Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • DNA sequence analysis is prone to bias from cryptic relatedness and population structure.
  • Existing sparse regression methods often address these confounders independently.
  • Prior biological information can enhance statistical power but is frequently omitted in current models.

Purpose of the Study:

  • To develop a unified sparse regression (USR) model that integrates prior biological information.
  • To jointly adjust for cryptic relatedness, population structure, and environmental covariates in genetic analyses.
  • To improve the accuracy and power of identifying genetic variants associated with diseases.

Main Methods:

  • Developed a unified sparse regression (USR) framework.
  • Modeled cryptic relatedness as a random effect and population structure as a fixed effect.
  • Employed weighted penalties to incorporate prior biological knowledge.

Main Results:

  • USR demonstrated superior performance in simulations compared to existing methods.
  • The algorithm identified more true causal variants and maintained a lower false discovery rate.
  • USR effectively handles both rare and common genetic variants simultaneously.
  • Application to GAW18 Mexican American data identified three hypertension pathways.

Conclusions:

  • The unified sparse regression (USR) offers a powerful approach for DNA sequence analysis.
  • USR enhances the discovery of susceptibility genetic variants by integrating diverse data types.
  • This method shows promise for understanding genetic underpinnings of complex diseases.