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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

17.7K
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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Genome Copying Errors02:46

Genome Copying Errors

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DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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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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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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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: Jul 14, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

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OSCAA: A Two-Dimensional Gaussian Mixture Model for Copy Number Variation Association Analysis.

Xuanxuan Yu, Xizhi Luo, Guoshuai Cai

    Biorxiv : the Preprint Server for Biology
    |October 9, 2023
    PubMed
    Summary

    We developed OSCAA, a new algorithm for identifying copy number variants (CNVs) associated with diseases. OSCAA improves accuracy in detecting disease-linked CNVs, especially for shorter variants or those with weak signals.

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    Area of Science:

    • Genomics
    • Statistical Genetics
    • Bioinformatics

    Background:

    • Copy number variants (CNVs) significantly impact genome organization and human diseases.
    • Identifying disease-associated CNVs is crucial for understanding disease pathogenesis, diagnosis, and treatment.
    • Traditional two-stage methods for CNV association studies can lead to biased estimates and reduced statistical power.

    Conclusions:

    • OSCAA is a powerful and flexible approach for CNV association testing.
    • It offers improved accuracy and power compared to existing methods.
    • The algorithm is readily applicable to diverse traits and clinical risk predictions.