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

Genetic Variation01:25

Genetic Variation

1.5K
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,...
1.5K
Principles of Pharmacogenetics: Types of Genetic Variants01:27

Principles of Pharmacogenetics: Types of Genetic Variants

73
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...
73
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.4K
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...
4.4K
Genetic Screens02:46

Genetic Screens

5.8K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.8K
Multiple Allele Traits01:49

Multiple Allele Traits

38.6K
The Concept of Multiple Allelism
38.6K
Multiple Allele Traits01:49

Multiple Allele Traits

14.8K
14.8K

You might also read

Related Articles

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

Sort by
Same author

Epigenetic Aging and Cognitive Performance in General and Psychiatric Populations: A Systematic Review and Narrative Synthesis.

Biological psychiatry global open science·2026
Same author

Post-traumatic stress disorder.

Nature reviews. Disease primers·2026
Same author

SMARCB1 missense mutants disrupt SWI/SNF complex stability and remodeling activity.

Nature communications·2026
Same author

Unraveling the interaction between stress and genetic risk in psychiatric disorders: Challenges, mechanisms, and new advances.

Neuron·2026
Same author

Schizophrenia risk variants modulate transcription factor binding and gene expression in cortical cell types.

Cellular and molecular life sciences : CMLS·2026
Same author

Glucocorticoid Receptor-Regulated Gene Networks and Mental Health.

Annual review of neuroscience·2026
Same journal

Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight.

Genetic epidemiology·2026
Same journal

Individualized Bayesian Inference Identifies Novel Genetic Variants for Parkinson's Disease.

Genetic epidemiology·2026
Same journal

DRIVE v3: Command Line Application for Identity-by-Descent Haplotype Clustering in Large Biobank Scale Data.

Genetic epidemiology·2026
Same journal

Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.

Genetic epidemiology·2026
Same journal

Polygenic Risk Scores for Incident Dementia in the Multi-Ethnic Study of Atherosclerosis.

Genetic epidemiology·2026
Same journal

Outcome and Exposure Polygenic Risk Scores Can Help Reduce Information Bias and Selection Bias in Regression Estimates From Biobank Data.

Genetic epidemiology·2026
See all related articles

Related Experiment Video

Updated: Mar 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Prioritizing individual genetic variants after kernel machine testing using variable selection.

Qianchuan He1, Tianxi Cai2, Yang Liu1

  • 1Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.

Genetic Epidemiology
|August 5, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to pinpoint specific genetic variants (SNPs) driving trait associations identified by SNP-set kernel association tests (SKAT). This approach enhances gene set analysis by identifying key SNPs, bridging the gap between set-level findings and functional studies.

Keywords:
KNIFEgenetic association studieskernel machine methodsset-basedvariable selection

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
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 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.8K
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:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Kernel machine learning, including SNP-set kernel association test (SKAT), is used for trait-genotype associations.
  • SKAT examines joint SNP effects within sets but doesn't identify specific causal SNPs.
  • Identifying driver SNPs is crucial for bridging SNP set analysis and functional studies.

Purpose of the Study:

  • To adapt the KerNel Iterative Feature Extraction (KNIFE) procedure for genetic association studies.
  • To develop an approach for identifying driver SNPs after SKAT gene set analysis.
  • To prioritize SNPs by integrating variable selection into kernel machine methods.

Main Methods:

  • Adapted the KNIFE procedure for genetic association studies focusing on quantitative traits and common SNPs.
  • Developed a method to identify driver SNPs following SKAT gene set analysis.
  • Accommodated common kernels like linear and Identity by State (IBS) kernels.

Main Results:

  • The proposed approach effectively identifies driver SNPs within associated SNP sets.
  • Demonstrated utility in prioritizing SNPs for further investigation.
  • Successfully bridged the gap between SNP set analysis and biological functional studies.

Conclusions:

  • The developed method provides practical tools for prioritizing SNPs in genetic association studies.
  • Enhances the interpretability of SKAT results by identifying specific contributing SNPs.
  • Facilitates the transition from statistical association to biological understanding.