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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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

Principles of Pharmacogenetics: Types of Genetic Variants

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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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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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%...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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Genetic Variation01:25

Genetic Variation

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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.
Genes exist in different versions called alleles,...
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Incomplete Dominance01:43

Incomplete Dominance

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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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Related Experiment Video

Updated: Mar 21, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between

Yi-Juan Hu1, Peizhou Liao1, H Richard Johnston1

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

Plos Genetics
|May 7, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing DNA sequencing data to find rare genetic variants linked to complex diseases. The approach avoids common errors by directly using sequencing reads, improving accuracy and power in genetic association studies.

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Area of Science:

  • Genetics and Genomics
  • Statistical Bioinformatics
  • Disease Association Studies

Background:

  • Next-generation sequencing (NGS) enables discovery of rare genetic variants for complex diseases.
  • Standard genotype calling methods are susceptible to false positives due to systematic errors, especially with varying sequencing quality between cases and controls.
  • Differential sequencing depth, platforms, or batches can introduce biases in genotype calls.

Purpose of the Study:

  • To develop a robust statistical method for testing rare genetic variant associations that directly models sequencing reads, bypassing genotype calling.
  • To address challenges posed by differential sequencing quality in case-control studies.
  • To identify novel rare variants associated with complex traits.

Main Methods:

  • A likelihood-based approach was developed to test rare variant associations using raw sequencing reads.
  • A weighted burden test statistic was employed, summing score statistics for individual variants.
  • A computationally efficient screening algorithm was created to identify potential variant loci, coupled with a novel bootstrap procedure for significance testing.

Main Results:

  • Extensive simulations demonstrated the proposed method's robustness to differential sequencing quality.
  • The new approach showed comparable or superior power to standard genotype calling methods when type I error was controlled.
  • Application to UK10K data identified novel rare variants in the BTBD18 gene associated with childhood onset obesity.

Conclusions:

  • The proposed likelihood-based method offers a more accurate and powerful alternative for rare variant association studies, particularly under heterogeneous sequencing conditions.
  • Directly modeling sequencing reads mitigates biases introduced by genotype calling errors.
  • The findings highlight the potential for discovering novel disease-associated variants using this approach, with publicly available software.