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Genome-wide Association Studies-GWAS

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...

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Genome-wide association study for egg production and quality in layer chickens.

A Wolc¹, J Arango, T Jankowski

¹Department of Animal Science, Iowa State University, Ames, IA, USA; Hy-Line International, Dallas Center, IA, USA.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie

|March 18, 2014

Summary

Genome-wide association studies identified significant genomic regions for egg production and quality traits in laying hens. This research pinpoints quantitative trait loci (QTL) influencing economically important traits in poultry breeding.

Keywords:

Egg production GWAS egg quality laying hens

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Area of Science:

Animal Genetics and Breeding
Quantitative Genetics
Poultry Science

Background:

Identifying genes with substantial effects on economically important traits is crucial for livestock breeders.
High-density SNP panels and cost-effective genotyping enable genome-wide association studies (GWAS) for trait discovery.
Previous research has analyzed egg weight using similar methodologies.

Purpose of the Study:

To identify genomic regions associated with egg production and quality traits in laying hens.
To utilize whole-genome prediction models for locating quantitative trait loci (QTL).

Main Methods:

Genotyped over 4500 birds from eight generations of a brown egg layer line using a custom 42K Illumina SNP chip.
Collected phenotypes on over 13,000 hens for egg production, 11 egg quality traits, body weight, and age at first egg.
Applied the Bayesian whole-genome prediction model BayesB to identify 1 Mb regions strongly associated with each trait, using posterior probability as the criterion for QTL presence.

Main Results:

Identified 1 to 7 significant 1 Mb regions (posterior probability > 0.9) for various traits.
Discovered a major QTL on chromosome 4 (78 Mb) for body weight, explaining 32% of genetic variance and showing pleiotropic effects.
Other significant QTLs explained <7% of genetic variance, with notable regions on chromosomes 2, 12, and 17 for albumen height, shell color, and egg production, respectively. A total of 45 windows showed significant effects for at least one trait.

Conclusions:

Genome-wide association studies successfully pinpointed significant genomic regions associated with key production and quality traits in laying hens.
The findings provide valuable genetic markers for improving economically important traits in poultry breeding programs.
The study demonstrates the utility of whole-genome prediction models in dissecting the genetic architecture of complex traits in livestock.