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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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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

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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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Genome-wide Association Studies-GWAS01:11

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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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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Frequency-dependent Selection01:21

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
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Updated: Oct 2, 2025

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.

Juhyun Kim1, Judong Shen2, Anran Wang2

  • 1Department of Biostatistics, University of California, Los Angeles.

The Annals of Applied Statistics
|February 24, 2022
PubMed
Summary
This summary is machine-generated.

We introduce VCSEL, a novel method for prioritizing sets of single nucleotide polymorphisms (SNPs) in genetic association studies. VCSEL outperforms existing methods in simulations and real-world applications for identifying important genetic sets.

Keywords:
Rare variantsgroup selectionmajorization-minimization (MM)multiple phenotypesnonconvex penaltiespenalized estimationrestricted maximum likelihood (REML)variance components model

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Area of Science:

  • Genetics and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Single nucleotide polymorphism (SNP) set analysis is crucial for aggregating common and rare variants to test associations with phenotypes.
  • Current methods often test numerous SNP sets (genes, pathways) independently, necessitating stringent multiple testing adjustments and potentially missing complex associations.

Purpose of the Study:

  • To develop a novel statistical framework, Variance Component Selection (VCSEL), for prioritizing SNP sets within a joint multivariate model.
  • To extend VCSEL to naturally handle multivariate traits (VCSEL-M) and SNP-set interactions (VCSEL-I).

Main Methods:

  • Proposed a joint multivariate variance component model where each SNP set represents a variance component (kernel).
  • Employed convex or non-convex penalties for model selection.
  • Developed a scalable optimization algorithm using the majorization-minimization (MM) principle for efficient computation.

Main Results:

  • Simulation studies showed VCSEL superior to marginal testing and group penalization in model selection, measured by the area under the precision-recall (PR) curve.
  • Applied VCSEL to pharmacogenomics and whole exome sequencing data, identifying top-ranked genes missed by marginal tests.
  • Highlighted VCSEL's ability to provide alternative biological insights for prioritizing follow-up studies and developing polygenic risk scores.

Conclusions:

  • VCSEL offers a powerful and flexible framework for SNP set association analysis, outperforming conventional methods.
  • The method effectively prioritizes genetic regions and identifies interactions, providing valuable insights for genetic research.
  • VCSEL enhances the discovery of biologically relevant genetic variants and supports the development of predictive models.