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Multiple Allele Traits

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Interval Level of Measurement

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between 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...

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”.

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Related Experiment Video

Updated: Jun 25, 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

Multiple-interval mapping for quantitative trait loci with a spike in the trait distribution.

Wenyun Li¹, Zehua Chen

¹School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou, People's Republic of China.

|March 5, 2009

Summary

A new multiple-interval mapping (MIM) method improves quantitative trait loci (QTL) analysis for traits with common values. This approach enhances efficiency and accuracy in genetic studies, outperforming standard methods.

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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
Statistical Genetics
Bioinformatics

Background:

Phenotypic distributions with common values, like survival time, present challenges for quantitative trait loci (QTL) mapping.
Standard QTL mapping methods perform suboptimally when faced with these 'spiked' distributions.

Purpose of the Study:

To develop an improved multiple-interval mapping (MIM) procedure for QTL analysis in the presence of phenotypic spikes.
To enhance the efficiency and accuracy of QTL mapping for complex traits.

Main Methods:

Developed a novel MIM procedure utilizing mixture generalized linear models (GLIMs).
Employed an extended Bayesian information criterion (EBIC) for robust model selection.
Validated the method using real-world data from Listeria infection and simulation studies.

Main Results:

The proposed MIM procedure significantly improved the positive selection rate compared to single-QTL models.
The method demonstrated a superior false discovery rate, indicating greater accuracy.
The new approach effectively handles phenotypic distributions with common values.

Conclusions:

The developed MIM procedure offers a more efficient and accurate approach to QTL mapping, particularly for traits with spiked distributions.
This statistical genetics method provides a valuable tool for genetic research, implemented in R and freely available.