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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...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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: Jun 26, 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

Hierarchical generalized linear models for multiple quantitative trait locus mapping.

Nengjun Yi1, Samprit Banerjee

  • 1Department of Biostatistics, Section on Statistical Genetics, University of Alabama, Birmingham, Alabama 35294-0022, USA. nyi@ms.soph.uab.edu

Genetics
|January 14, 2009
PubMed
Summary
This summary is machine-generated.

We developed new statistical models and algorithms for analyzing quantitative trait loci (QTL) in experimental crosses. This approach efficiently identifies genetic effects and interactions, improving genome-wide association studies.

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

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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

Area of Science:

  • Statistical Genetics
  • Bioinformatics
  • Genomics

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding the genetic basis of traits.
  • Analyzing quantitative trait loci (QTL) in experimental crosses requires sophisticated statistical models to account for complex genetic architectures.
  • Existing methods may face challenges in fitting numerous effects, including gene-gene and gene-environment interactions.

Purpose of the Study:

  • To develop advanced hierarchical generalized linear models for genomewide QTL analysis.
  • To create computationally efficient algorithms for fitting complex models with numerous effects.
  • To provide a robust method for identifying genetic loci and their interactions influencing phenotypes.

Main Methods:

  • Hierarchical generalized linear models with continuous prior distributions favoring sparseness.
  • A fast expectation-maximization (EM) algorithm for estimating posterior modes of coefficients.
  • Integration into R package with iteratively weighted least squares and a model search strategy.

Main Results:

  • The proposed models effectively fit covariates, main locus effects, and gene-gene (epistasis) and gene-environment (G x E) interactions.
  • Simulation studies confirmed good power for detecting true effects and controlled false positive rates.
  • The method was successfully applied to three real datasets, outperforming existing multiple-QTL mapping approaches.

Conclusions:

  • The developed method offers a computationally efficient and statistically powerful approach for genomewide QTL analysis.
  • The R/qtlbim package provides a valuable tool for researchers studying the genetic architecture of complex traits.
  • This work enhances existing methods by incorporating advanced modeling and efficient algorithms for QTL mapping.