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

16.4K
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...
16.4K
Heritability01:06

Heritability

771
Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
771
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.2K
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...
1.2K
Human Genetics01:28

Human Genetics

1.8K
Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
1.8K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.2K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.2K
Genomics02:02

Genomics

41.4K
Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
41.4K

You might also read

Related Articles

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

Sort by
Same author

Evaluating sequence-to-function deep learning models for ancestry-stratified regulatory variant effect prediction using multi-ancestry blood eQTLs.

bioRxiv : the preprint server for biology·2026
Same author

Combining post-mortem and neuroimaging measures of brain amyloidosis to accelerate genomic discovery.

Brain : a journal of neurology·2026
Same author

The gut-brain axis in Alzheimer's disease: early detection, microbial metabolites, mechanisms, and therapeutic opportunities.

Frontiers in molecular biosciences·2026
Same author

Leadership, Informatics Expertise, and Resources: Determinants of Institutional Data Sharing in the National Clinical Cohort Collaborative (N3C).

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Multi-ancestry transcriptome-wide association study reveals shared and population-specific genetic effects in Alzheimer disease.

American journal of human genetics·2026
Same author

Genetic correlation analysis of Alzheimer's disease and stroke implicates PHLPP1 as a shared locus in individuals of African ancestry.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Related Experiment Video

Updated: Mar 13, 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

4.9K

Analysis of Heritability Using Genome-Wide Data.

Jacob B Hall1, William S Bush1

  • 1Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio.

Current Protocols in Human Genetics
|October 12, 2016
PubMed
Summary

This study introduces mixed linear models for genome-wide association studies. These models account for genetic background and population stratification, improving heritability estimation and analysis of genetic data.

Keywords:
GCTAheritabilitymixed-model analysis

More Related Videos

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.5K
Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

17.7K

Related Experiment Videos

Last Updated: Mar 13, 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

4.9K
Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

19.5K
Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
10:08

Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis

Published on: August 12, 2019

17.7K

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) often analyze variants independently, neglecting the genome's complex genetic background.
  • Confounding factors like population stratification can bias traditional GWAS results.
  • Accurate heritability estimation is crucial for understanding genetic contributions to traits.

Purpose of the Study:

  • To describe the principles and practical application of mixed linear models for GWAS data analysis.
  • To highlight how mixed models can account for genetic background and population stratification.
  • To explain the computation of genetic sharing for improved heritability approximation.

Main Methods:

  • Utilizing mixed linear models to relate genotypic and phenotypic sharing.
  • Employing representations of genomic sharing among individuals in a dataset.
  • Focusing on efficient computation of genetic sharing for large-scale genomic datasets.

Main Results:

  • Mixed models provide a robust framework for analyzing GWAS data by incorporating genetic background.
  • These models effectively address confounding factors such as population stratification.
  • Efficient computation of genetic sharing enables better heritability approximation.

Conclusions:

  • Mixed linear models offer a significant advancement for genome-wide association studies.
  • The described methods enhance the accuracy of heritability estimation and genetic analysis.
  • This approach is essential for a comprehensive understanding of genetic architecture.