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Related Concept Videos

Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

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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...
X-linked Traits01:19

X-linked Traits

In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
X-linked Traits01:19

X-linked Traits

In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.

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Related Experiment Video

Updated: Jul 2, 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

Bayesian quantitative trait loci mapping for multiple traits.

Samprit Banerjee1, Brian S Yandell, Nengjun Yi

  • 1Departments of Biostatistics, Section on Statistical Genetics, University of Alabama, Birmingham, AL 35294, USA.

Genetics
|August 12, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces novel Bayesian methods for quantitative trait loci (QTL) mapping in correlated traits. The developed R/qtlbim package enables efficient genomewide analysis of complex genetic architectures.

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

  • Genetics and Bioinformatics
  • Statistical Genomics

Background:

  • Quantitative trait loci (QTL) mapping often involves analyzing multiple correlated complex traits.
  • Existing genomewide mapping strategies for correlated traits are limited.

Purpose of the Study:

  • To develop and present Bayesian multiple-QTL mapping methods for correlated continuous traits.
  • To provide a comprehensive genomewide mapping strategy for complex traits.

Main Methods:

  • Development of two multivariate Bayesian models: a traditional multivariate model and a seemingly unrelated regression (SUR) model.
  • Implementation of computationally efficient Markov chain Monte Carlo (MCMC) algorithms for joint analysis.
  • Comparison with conventional single-trait models through extensive simulation studies.

Main Results:

  • The proposed Bayesian methods offer a comprehensive approach to QTL mapping for correlated traits.
  • The R/qtlbim package provides an accessible tool for applying these advanced methodologies.
  • Simulations demonstrate the performance and utility of the developed methods.

Conclusions:

  • The new Bayesian methodology enhances the understanding of genetic architecture for complex traits.
  • The R/qtlbim package facilitates the application of advanced statistical genetics methods.
  • This work addresses a critical gap in genomewide mapping strategies for correlated traits.