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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...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Heritability01:06

Heritability

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" a trait is,...

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Related Experiment Video

Updated: Jun 8, 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

Genome-wide evaluation for quantitative trait loci under the variance component model.

Lide Han1, Shizhong Xu

  • 1Department of Botany and Plant Science, University of California, Riverside, CA 92521, USA.

Genetica
|September 14, 2010
PubMed
Summary

This study introduces a new multiple variance component model for genome-wide quantitative trait loci (QTL) mapping in outbred populations. The model efficiently estimates QTL variances and positions simultaneously, offering an improvement over existing methods.

Related Experiment Videos

Last Updated: Jun 8, 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

Area of Science:

  • Quantitative genetics
  • Genomic analysis
  • Statistical genetics

Background:

  • Identity-by-descent (IBD) based variance component analysis is crucial for quantitative trait loci (QTL) mapping in outbred populations.
  • Existing interval-mapping approaches may have limitations in evaluating whole-genome genetic variances due to complex model selection.

Purpose of the Study:

  • To develop a novel multiple variance component model for comprehensive genome-wide evaluation.
  • To simultaneously estimate QTL variances and positions within a single model.

Main Methods:

  • Developed a multiple variance component model for genome-wide analysis.
  • Employed both maximum likelihood (ML) and Markov Chain Monte Carlo (MCMC) implemented Bayesian methods.
  • Placed quantitative trait loci (QTL) across the entire genome at regular intervals (every few centimorgans) for simultaneous estimation.

Main Results:

  • The developed model effectively identified genomic regions with and without quantitative trait loci (QTL).
  • Regions with significant QTL showed strong evidence, while regions without QTL showed no significant evidence.
  • The Bayesian method yielded optimal results, whereas the maximum likelihood (ML) method demonstrated greater computational efficiency.

Conclusions:

  • The new multiple variance component model provides an effective approach for genome-wide quantitative trait loci (QTL) mapping.
  • Both ML and Bayesian methods are viable, with ML offering computational advantages and Bayesian methods providing optimal estimations.
  • Simulation experiments confirmed the efficacy of the developed methods for genetic variance evaluation.