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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...
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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.
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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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Pharmacogenomics: Identification of New Drug Targets

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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Effective selection of informative SNPs and classification on the HapMap genotype data.

Nina Zhou1, Lipo Wang

  • 1Electrical and Electronic Engineering, Nanyang Technology University, Block S1, 50 Nanyang Avenue, 639798 Singapore. zhounina@ntu.edu.sg

BMC Bioinformatics
|December 21, 2007
PubMed
Summary

This study identifies key genetic markers, or single nucleotide polymorphisms (SNPs), for accurately determining an individual's population of origin. The new method efficiently selects a minimal set of informative SNPs for population identification.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Population Studies

Background:

  • Single nucleotide polymorphisms (SNPs) are genetic variations used for individual identification.
  • Efficiently identifying source populations requires selecting a minimal set of informative SNPs from large datasets.
  • Previous methods, like Park et al. (2006), used 82 SNPs to classify three populations.

Purpose of the Study:

  • To develop an efficient method for selecting a minimal set of informative SNPs for population identification.
  • To improve upon existing methods for classifying individuals into their source populations.

Main Methods:

  • Ranked SNPs using modified t-test or F-statistics.
  • Formed feature subsets by sequentially selecting top-ranked SNPs.
  • Used Support Vector Machine (SVM) classifier to evaluate subsets and find the most accurate one.

Main Results:

  • Achieved good classification of three populations (CEU, YRI, CHB+JPT) using an average of 64 SNPs.
  • The proposed ranking and subset selection method outperformed previous approaches.

Conclusions:

  • Modified t-test and F-statistics are effective for ranking SNPs by classification capability.
  • The greedy approach combined with SVM efficiently identifies informative SNP subsets.
  • The method successfully identifies a small number of crucial SNPs for determining individual populations.