Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Genome-wide Association Studies-GWAS

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

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...

Behavioral Genetics and Its Designs

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...

Genomics

Genomics

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...

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

You might also read

Related Articles

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

Sort by

Same author

Modulation of genetic associations with serum urate levels by body-mass-index in humans.

PloS one·2015

Same author

Prevalence of refractive error in Europe: the European Eye Epidemiology (E(3)) Consortium.

European journal of epidemiology·2015

Same author

Genome-wide association analysis on five isolated populations identifies variants of the HLA-DOA gene associated with white wine liking.

European journal of human genetics : EJHG·2015

Same author

Genome of The Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels.

Nature communications·2015

Same author

Rare variants in γ-aminobutyric acid type A receptor genes in rolandic epilepsy and related syndromes.

Annals of neurology·2015

Same author

Age- and sex-specific causal effects of adiposity on cardiovascular risk factors.

Diabetes·2015

Search research articles

Related Experiment Video

Updated: Jul 9, 2026

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

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

Published on: July 27, 2021

A genomic background based method for association analysis in related individuals.

Najaf Amin¹, Cornelia M van Duijn, Yurii S Aulchenko

¹Department of Epidemiology and Biostatistics, Erasmus University Medical Center (MC) Rotterdam, Rotterdam, The Netherlands.

|December 7, 2007

Summary

We developed GRAMMAR-GC, a fast and powerful genome-wide association analysis method for related individuals. It uses genomic data to estimate relatedness, overcoming limitations of traditional methods like Measured Genotype (MG) and GRAMMAR.

More Related Videos

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

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

Published on: June 21, 2018

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jul 9, 2026

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

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

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

Published on: June 21, 2018

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) require efficient methods for analyzing large datasets with single nucleotide polymorphisms (SNPs).
Analyzing genetically related individuals presents challenges for standard GWAS methods.
Existing methods like Measured Genotype (MG) are powerful but computationally intensive, while GRAMMAR is faster but less powerful and relies on accurate pedigree data.

Purpose of the Study:

To evaluate the performance of MG, GRAMMAR, and Genomic Control (GC) methods in terms of Type 1 error and power.
To propose and validate an extended method, GRAMMAR-GC, for association analysis in related individuals.
To adapt GRAMMAR-GC to utilize genomic marker data for kinship estimation, mitigating reliance on potentially incomplete or erroneous pedigree information.

Main Methods:

Comparative analysis of Type 1 error and relative power for MG, GRAMMAR, and GC approaches.
Development and simulation-based testing of the novel GRAMMAR-GC method.
Implementation of GRAMMAR-GC using a kinship matrix derived from genomic marker data.

Main Results:

The MG approach demonstrated high power across various heritabilities and pedigree structures.
The proposed GRAMMAR-GC method achieved power comparable to the gold-standard MG approach.
GRAMMAR-GC exhibited correct Type 1 error rates in simulations involving related individuals.

Conclusions:

GRAMMAR-GC is a feasible and powerful method for genome-wide association analysis in populations with related individuals.
The method effectively addresses the limitations of existing approaches by using genomic data for relatedness estimation.
GRAMMAR-GC offers a robust alternative for genetic association studies where complete pedigree data is unavailable.