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Identifying candidate genetic variants for egg number by analyzing over 1,000 fully sequenced layers
Aixin Ni1,2, Henk Bovenhuis2, Mario P L Calus2
1State Key Laboratory of Animal Biotech Breeding, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
This study analyzed 1,004 chickens to find genetic variants for egg production, incorporating dominance effects and multiomics data. Findings reveal key genes influencing traits and highlight the role of dominant gene action in heterosis for improved layer breeding.
Area of Science:
- Animal Genetics
- Poultry Breeding
- Genomics
Background:
- Modern layer chicken breeding prioritizes long laying cycles up to 700 days.
- Egg production is influenced by laying onset, peak stability, and persistence.
- Conventional genetic analyses often overlook dominance effects and multiomics integration for egg production traits.
Purpose of the Study:
- Investigate the genetic basis of egg production traits from onset to 700 days of age.
- Explore the roles of additive and dominance genetic effects in egg production.
- Integrate multiomics data (genomics and transcriptomics) to identify candidate genes and mechanisms.
Main Methods:
- Whole-genome sequencing of 1,004 chickens in a full diallel cross.
- Genome-wide association study (GWAS) using an additive-dominance model for egg production traits.
- Expression quantitative trait loci (eQTL) and transcriptome-wide association studies (TWAS) integrating ovary transcriptome data.
Main Results:
- Identified 5,892 significant single nucleotide polymorphisms (SNPs), including 805 additive and 360 dominance SNPs.
- Associated 27 genes with significant SNPs via GWAS and eQTL mapping.
- Discovered 4 novel candidate genes through TWAS and identified a positive correlation between dominance effects and heterosis.
Conclusions:
- Identified candidate genetic variants for egg production traits in layers using a comprehensive genomic approach.
- Incorporating dominance effects in GWAS improved the detection of genetic variants influencing egg production.
- Multiomics data integration successfully connected genetic variants, gene expression, and egg number, elucidating underlying genetic mechanisms and highlighting the importance of dominant gene action in heterosis.
