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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

The Concept of Multiple Allelism
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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Updated: Jun 27, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Multiple-trait quantitative trait locus mapping with incomplete phenotypic data.

Zhigang Guo1, James C Nelson

  • 1Department of Plant Pathology, Kansas State University, Manhattan, Kansas 66506, USA. zhigang_guo@syngenta.com

BMC Genetics
|December 9, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an expectation-maximization (EM) algorithm to effectively utilize all data in quantitative trait locus (QTL) mapping, even with missing phenotypes. This novel approach enhances QTL detection and estimation accuracy compared to traditional methods.

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Last Updated: Jun 27, 2026

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

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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:

  • Genetics
  • Biostatistics

Background:

  • Conventional quantitative trait locus (QTL) mapping discards individuals with incomplete data, losing valuable genotypic and phenotypic information.
  • Standard missing-data mechanisms allow for the utilization of these discarded cases.

Purpose of the Study:

  • To develop and evaluate an expectation-maximization (EM) algorithm for multiple-trait QTL analysis that accommodates missing phenotypic data.
  • To improve QTL detection power and the precision of QTL location and effect estimation.

Main Methods:

  • An expectation-maximization (EM) algorithm was developed for recombinant inbred and F2 genetic models, extensible to other mating designs.
  • The algorithm supports hypothesis tests for QTL main effect, pleiotropy, and QTL-by-environment interactions.
  • Performance was assessed via simulations and a real-data example.

Main Results:

  • The EM algorithm effectively incorporates individuals with missing phenotypic data into multiple-trait QTL analyses.
  • Simulations and real-data examples demonstrated improved QTL detection power and more precise estimation of QTL location and effects.
  • The method outperforms traditional case deletion and imputation techniques.

Conclusions:

  • The developed EM method offers a significant advancement for multiple-trait QTL mapping with missing data.
  • It enhances the accuracy and power of genetic analyses, leading to more reliable QTL identification.
  • The EM approach is compatible with existing least-squares and likelihood-maximization QTL-mapping frameworks.