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Related Concept Videos

Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
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.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Genome-wide Association Studies-GWAS01:11

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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.
GWAS does not require the identification of the target gene involved in...
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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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Fast orthogonal search for genetic feature selection.

Layan Imad Nahlawi1, Parvin Mousavi

  • 1School of Computing, Queen's University, Kingston, Ontario, Canada. lnahlawj@cs.queensu.ca

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a fast method using multivariate regression to identify key genetic markers called Single Nucleotide Polymorphisms (SNPs). This approach accurately models complex genetic data and is efficient for large-scale genomic analysis.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single Nucleotide Polymorphisms (SNPs) are crucial for understanding genetic variation and disease association.
  • Analyzing large SNP datasets presents computational challenges for feature selection and modeling.
  • Existing methods may lack efficiency or accuracy in identifying informative genetic markers.

Purpose of the Study:

  • To apply a multivariate regression approach, fast orthogonal search (FOS), for effective feature selection in SNP data.
  • To accurately model entire SNP datasets using the selected informative features.
  • To demonstrate the efficiency and scalability of the proposed methodology for genome-wide association studies.

Main Methods:

  • Utilized fast orthogonal search (FOS), a multivariate regression technique.
  • Applied the method to two publicly available SNP datasets.
  • Evaluated the accuracy of the model in capturing hidden genetic information.

Main Results:

  • Achieved very high accuracies in modeling SNP data.
  • Demonstrated the capability of FOS to select the most informative genetic features.
  • The methodology exhibited a very short execution time, indicating high efficiency.

Conclusions:

  • The fast orthogonal search approach is a powerful tool for feature selection in SNP data analysis.
  • This method accurately models complex genetic information and is computationally efficient.
  • The developed methodology is suitable for application to large-scale genome-wide datasets, facilitating future genetic research.