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

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...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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%...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

You might also read

Related Articles

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

Sort by
Same author

Effect of Genetic Architecture and Partitioning of Training Population on GEBVs, SNP Effects and GWAS: A Simulation Study.

Genes·2026
Same author

Identifying condition-related cell-cell communication events using supervised tensor analysis.

American journal of human genetics·2026
Same author

An integrated germline and somatic genomic model for coronary artery disease.

Nature communications·2026
Same author

Multi-tissue transcriptome-wide association study identifies 29 risk genes associated with attention-deficit/hyperactivity disorder.

medRxiv : the preprint server for health sciences·2026
Same author

Integrative transcriptome-wide association analyses reveal PRKCG-linked GABAergic dysfunction in Fragile X-associated tremor/ataxia syndrome.

Nature communications·2026
Same author

Epigenome-wide association study of nuclear DNA methylation in relation to mitochondrial heteroplasmy.

Nature communications·2025
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: May 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

SNP set association analysis for familial data.

Elizabeth D Schifano1, Michael P Epstein, Lawrence F Bielak

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts.

Genetic Epidemiology
|September 13, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for genome-wide association studies (GWAS) using kernel machines to analyze sets of single nucleotide polymorphisms (SNPs) in families. The approach enhances the detection of genetic associations with diseases, accounting for family structures.

Keywords:
family association studieskernel machinelinear mixed modelmultilocus testscore statisticsvariance component testwithin-family correlation

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

Related Experiment Videos

Last Updated: May 18, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

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

Area of Science:

  • Genetics
  • Biostatistics
  • Epidemiology

Background:

  • Genome-wide association studies (GWAS) traditionally analyze single nucleotide polymorphisms (SNPs) individually.
  • Kernel machine methods offer a more flexible approach to model joint SNP effects within sets.
  • Existing methods may not fully account for relatedness within families.

Purpose of the Study:

  • To extend the kernel machine framework for analyzing SNP sets in related individuals across multiple families.
  • To develop a score-based variance component test for association with continuous phenotypes.
  • To adjust for covariates and within-family correlation in genetic association analyses.

Main Methods:

  • Utilized a kernel machine framework extended to handle related subjects from multiple independent families.
  • Developed a score-based variance component test for assessing SNP set association.
  • Incorporated adjustments for covariates and accounted for within-family correlation.

Main Results:

  • The proposed method effectively assesses the association of SNP sets with continuous phenotypes in family-based studies.
  • Simulation studies demonstrated the utility and power of the new approach.
  • Application to the Genetic Epidemiology Network of Arteriopathy (GENOA) study showcased its practical relevance.

Conclusions:

  • The extended kernel machine framework provides a robust method for genetic association studies involving related individuals.
  • This approach improves the ability to detect complex genetic effects of SNP sets on disease phenotypes.
  • The method is valuable for genetic epidemiology research, particularly in family studies.