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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...

Dihybrid Crosses

Dihybrid Crosses

Pleiotropy

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...

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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Related Experiment Video

Updated: May 8, 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

Selecting informative traits for multivariate quantitative trait locus mapping helps to gain optimal power.

Riyan Cheng¹, Justin Borevitz, R W Doerge

¹Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory 0200, Australia.

|August 28, 2013

Summary

This study introduces variable selection for multitrait quantitative trait locus (QTL) mapping to identify informative traits. This approach enhances statistical power for QTL identification and simplifies complex analyses.

Keywords:

multitrait mapping quantitative trait locus (QTL)statistical power variable selection

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

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Published on: June 21, 2018

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Last Updated: May 8, 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

Area of Science:

Genetics
Bioinformatics
Statistical Genomics

Background:

Multitrait analysis is crucial for understanding complex genetic architectures.
Current strategies often involve analyzing trait pairs or all traits, which can be computationally intensive or miss key interactions.

Purpose of the Study:

To propose a variable selection method for identifying informative traits in multitrait quantitative trait locus (QTL) mapping.
To enhance statistical power and simplify the analysis of large numbers of traits.

Main Methods:

Developed a variable selection approach to choose informative traits for multitrait QTL mapping.
Investigated the impact of selection bias and utilized permutation tests for genome scanning.
Implemented and validated the procedure using simulated and real data in a backcross population.

Main Results:

The proposed variable selection method effectively identifies relevant traits for QTL mapping.
Demonstrated increased statistical power for QTL identification compared to traditional methods.
Provided a practical strategy for handling a large number of traits in multivariate analyses.

Conclusions:

Variable selection is a powerful tool for optimizing multitrait QTL mapping.
The method offers improved power and practicality, especially when dealing with numerous traits.
The approach is adaptable to various experimental mapping populations.