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Related Concept Videos

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Polygenic Traits

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 Traits

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 Traits

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 Traits

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

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Study on mapping quantitative trait loci for animal complex binary traits using Bayesian-Markov chain Monte Carlo

Jianfeng Liu¹, Yuan Zhang, Qin Zhang

¹College of Animal Science and Technology, China Agricultural University, Beijing 100094, China.

Science in China. Series C, Life Sciences

|February 23, 2007

Summary

Mapping quantitative trait loci (QTL) for complex binary traits is challenging. The Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach effectively maps QTL, improving accuracy with larger family sizes and outbred designs for small-effect QTL.

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Published on: December 10, 2012

Area of Science:

Genetics
Statistical genetics
Animal breeding

Background:

Mapping quantitative trait loci (QTL) for complex discrete traits presents significant statistical challenges.
Conventional methods struggle with discontinuous distributions and limited information inherent in binary traits.

Purpose of the Study:

To demonstrate the utility of the Bayesian-Markov chain Monte Carlo (Bayesian-MCMC) approach for QTL mapping in complex binary traits.
To evaluate the robustness of Bayesian-MCMC under various family structures and QTL effects in simulated animal outbred full-sib families.

Main Methods:

Utilized Bayesian-MCMC, incorporating Gibbs sampling, Metropolis algorithm, and reversible jump MCMC.
Employed an Identity-by-Descent-Based variance component random model.
Simulated complex binary traits influenced by both a major QTL and polygenes.

Main Results:

The Bayesian-MCMC approach proved effective and robust across different family structures and QTL effect sizes.
Increased family size and decreased number of families enhanced the accuracy of parameter estimation.
Outbred population designs with large family sizes are optimal for mapping QTL with small effects.

Conclusions:

Bayesian-MCMC is a powerful statistical inference tool for QTL mapping of complex binary traits.
Experimental design, specifically large family sizes in outbred populations, is crucial for accurate QTL detection, especially for genes with minor effects.