Related Experiment Video
Updated: May 27, 2025
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
Published on: June 21, 2018
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Novel Candidate Genes Detection Using Bayesian Network-Based Genome-Wide Association Study of Latent Traits in F2
Siavash Manzoori1, Rasoul Vaez Torshizi1, Ali Akbar Masoudi1
1Department of Animal Science, Tarbiat Modares University, Tehran, Iran.
Summary
This study reveals that Structural Equation Model Genome-Wide Association Studies (SEM-GWAS) are superior for identifying pleiotropic markers influencing multiple chicken traits like body weight and feed intake. SEM-GWAS improves genetic analysis by accounting for trait correlations.
Area of Science:
- Animal Genetics
- Quantitative Genetics
- Genomic Analysis
Background:
- Economically important traits in chickens are often polygenic and correlated, posing challenges for single-trait GWAS.
- Pleiotropy and linkage disequilibrium complicate the genetic analysis of correlated phenotypes.
- Existing methods may not adequately address the complex genetic architecture of multiple, interrelated traits.
Purpose of the Study:
- To develop and apply advanced statistical models for analyzing correlated traits in chickens.
- To identify genetic markers, including pleiotropic ones, associated with key production and health traits.
- To compare the efficacy of Multi-Trait GWAS (MT-GWAS) with Structural Equation Model GWAS (SEM-GWAS).
Main Methods:
- Utilized factor analytical models to estimate latent traits and reduce phenotype dimensionality.
- Employed a Bayesian network (BN) algorithm to infer causal relationships among latent traits.
- Performed association analyses using Multi-Trait (MT) and Structural Equation Model (SEM) approaches on genotyped chicken data (369 F2 birds, 60K SNPs).
Main Results:
- Identified candidate genes for body weight (BW), feed intake (FI), feed efficiency (FE), and blood metabolites (BMB).
- The IPMK gene was associated with BW and FI; MTERF2 with BW and FE.
- A pleiotropic marker (rs14565514) near IPMK, UBE2D1, and CISD1 was detected by both MT- and SEM-GWAS. SEM-GWAS identified NRG3 for FI and several genes for BMB.
- SEM-GWAS demonstrated superiority over MT-GWAS by considering trait causality and interdependencies.
Conclusions:
- SEM-GWAS is a more powerful approach for dissecting the genetic basis of correlated traits in livestock.
- This method effectively identifies pleiotropic genetic variants influencing multiple economically important traits in chickens.
- The findings provide valuable insights for genetic improvement programs in poultry breeding.
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