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Related Concept Videos

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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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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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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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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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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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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Related Experiment Video

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Population genetics identifies challenges in analyzing rare variants.

Henry Richard Johnston1, Yijuan Hu, David J Cutler

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.

Genetic Epidemiology
|February 3, 2015
PubMed
Summary
This summary is machine-generated.

Analyzing rare variants in genetic studies requires careful consideration. Unlike common variants, rare variants present unique challenges, especially regarding sequencing errors, which can inflate false positives and complicate disease association studies.

Keywords:
error ratesrare variantswhole genome sequencing

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Area of Science:

  • Genetics
  • Genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) traditionally focused on common genomic variants.
  • Recent research emphasizes the importance of rare variants in genetic studies.
  • Rare variants exhibit different behaviors compared to common variants, challenging established genetic intuition.

Purpose of the Study:

  • To highlight the critical differences between common and rare variants in genetic analyses.
  • To explain the potential for inflated Type I error rates when analyzing rare variants without proper caution.
  • To address the impact of next-generation sequencing errors on the false-positive rates for rare variants.

Main Methods:

  • Comparative analysis of common versus rare variant behavior in genetic studies.
  • Illustrative examples demonstrating the impact of study design on rare variant analysis.
  • Assessment of next-generation sequencing error rates in the context of rare variant detection.

Main Results:

  • Failure to account for the origin of rare variants can lead to significantly inflated Type I error rates.
  • Even low error rates in next-generation sequencing can dramatically increase false-positive findings for rare variants.
  • Increasing sample size may exacerbate, rather than alleviate, false-positive issues with rare variants due to error proportions.

Conclusions:

  • Careful study design and a thorough understanding of rare variant characteristics are crucial for accurate genetic association studies.
  • Mitigating sequencing errors is paramount to avoid misinterpreting noise as true biological signals for rare variants.
  • Researchers must adopt specialized strategies for rare variant analysis to overcome challenges and ensure reliable disease association findings.