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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

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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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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...
Principles of Pharmacogenetics: Types of Genetic Variants01:27

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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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A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
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Published on: June 15, 2011

Selecting SNPs to identify ancestry.

Joshua N Sampson1, Kenneth K Kidd, Judith R Kidd

  • 1National Cancer Institute, Bethesda, MD, USA. joshua.sampson@nih.gov

Annals of Human Genetics
|June 15, 2011
PubMed
Summary
This summary is machine-generated.

Predicting human ancestry using genetic data is crucial for medical and forensic science. This study introduces an Improved Bayesian Estimate to select optimal single-nucleotide polymorphisms (SNPs) for accurate ancestry prediction with minimal error.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Population Genetics

Background:

  • Genotyping single-nucleotide polymorphisms (SNPs) enables prediction of individual ethnicity or ancestry.
  • Ancestry information is vital for minimizing confounding in medical studies and directing forensic investigations.
  • Millions of SNPs exist, necessitating a method for selecting a small, informative subset.

Purpose of the Study:

  • To develop a method for selecting a minimal set of SNPs to accurately predict human ancestry.
  • To improve upon existing error rate estimation methods for SNP selection.

Main Methods:

  • Utilized a training dataset to estimate expected error rates for various SNP sets.
  • Proposed and implemented a novel 'Improved Bayesian Estimate' for error rate calculation.
  • Evaluated SNP selection procedures based on the new estimation method.

Main Results:

  • The Improved Bayesian Estimate enhances the accuracy of SNP selection for ancestry prediction.
  • Selection procedures using this estimate yield small SNP sets with high predictive accuracy.
  • A list of 100 optimal SNPs for ancestry identification is provided.

Conclusions:

  • The proposed Improved Bayesian Estimate is effective for selecting informative SNPs for ancestry prediction.
  • Accurate ancestry prediction can be achieved using a small, carefully selected subset of SNPs.
  • The identified optimal SNPs offer a valuable resource for genetic and forensic applications.