Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

16.2K
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...
16.2K
Unusual Results01:16

Unusual Results

4.0K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
4.0K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

19.1K
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,...
19.1K
Probability Laws01:49

Probability Laws

44.7K
Overview
44.7K
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

77.0K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
77.0K
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

65
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...
65

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Group-Penalized Exponential Tilt Model for Identification of Differentially Methylated Genes in Epigenetic Association Studies.

Journal of computational biology : a journal of computational molecular cell biology·2025
Same author

Weighted overlapping group lasso for integrating prior network knowledge into gene set analysis.

BMC bioinformatics·2025
Same author

Variability in the serial interval of COVID-19 in South Korea: a comprehensive analysis of age and regional influences.

Frontiers in public health·2024
Same author

Weighted Selection Probability to Prioritize Susceptible Rare Variants in Multi-Phenotype Association Studies with Application to a Soybean Genetic Data Set.

Journal of computational biology : a journal of computational molecular cell biology·2023
Same author

New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits.

BMC bioinformatics·2023
Same author

Genetic Diversity and Genome-Wide Association Study of Seed Aspect Ratio Using a High-Density SNP Array in Peanut (<i>Arachis hypogaea</i> L.).

Genes·2020
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Mar 6, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.6K

Selection Probability for Rare Variant Association Studies.

Gira Lee1, Hokeun Sun1

  • 1Department of Statistics, Pusan National University , Busan, Korea.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 11, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to quantify the certainty of rare genetic variants in human genome research. The proposed approach improves the selection power for identifying outcome-related rare variants, enhancing genetic association studies.

Keywords:
genetic association studyrare variantselection probabilitysequencing data

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.8K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K

Related Experiment Videos

Last Updated: Mar 6, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.6K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.8K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.5K

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • High-throughput DNA sequencing enables genetic association studies of rare variants.
  • Detecting outcome-related rare variants is statistically challenging due to their extreme rarity.
  • Existing power set-based methods can select rare variants but lack certainty measurement.

Purpose of the Study:

  • To propose a method for quantifying the certainty of individual rare variant selection.
  • To introduce a novel selection approach based on selection probability thresholds.
  • To compare the proposed method with existing rare variant selection procedures.

Main Methods:

  • Utilizing bootstrap resampling to compute selection frequencies of rare variants.
  • Developing a selection probability metric to quantify variant certainty.
  • Conducting extensive simulation studies and real sequencing data analysis.

Main Results:

  • The proposed selection probability method quantifies the certainty of both selected and unselected rare variants.
  • The new selection approach demonstrated superior performance compared to existing methods.
  • The approach showed improved selection power in identifying outcome-related rare variants.

Conclusions:

  • The proposed selection probability method effectively quantifies the certainty of rare variant identification.
  • This novel approach enhances the statistical power in genetic association studies for rare variants.
  • The method offers a more reliable way to detect risk and protective rare variants in human genome research.