Changes in variance explained by top SNP windows over generations for three traits in broiler chicken

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Genomic Selection

Background:

• Understanding genetic variation is crucial for livestock breeding.
• Identifying genomic regions linked to quantitative traits aids selection.
• Long-term stability of these regions is key for effective breeding programs.

Purpose of the Study:

• To assess the stability of genomic regions associated with major genetic variation over multiple generations in broiler chickens.
• To evaluate the consistency of top single nucleotide polymorphism (SNP) windows across different generational analyses.

Main Methods:

• Utilized a dataset of five generations of broiler chickens with phenotypes for body weight, breast meat, and leg score.
• Genomic data from 4,866 animals with 41,036 SNPs were analyzed.
• Employed a Genome-Wide Association Study (GWAS) approach with a single-step genomic Best Linear Unbiased Prediction (BLUP) to calculate SNP effects across generational combinations (1-3, 2-4, 3-5, 1-5).
• Variance explained by 20-SNP windows was calculated to identify top regions.

Main Results:

• The top 10 SNP windows consistently explained between 0.5% and 1% of the total genetic variance across generations.
• A significant lack of consistency was observed in the identified top genomic windows across different generational combinations.
• Windows identified as top contributors in one generational analysis often explained less than 0.1% of the variance in others.

Conclusions:

• The genomic regions responsible for substantial genetic variation in quantitative traits like body weight, breast meat yield, and leg score are not stable across multiple generations of selection in this broiler chicken population.
• The findings suggest that genomic regions identified as important in early generations may have limited predictive value for genetic selection in subsequent generations.
• This highlights a potential challenge for long-term genomic selection strategies in rapidly evolving commercial populations.