Genome-wide association study for backfat thickness in Canchim beef cattle using Random Forest approach

Area of Science:

• Animal Genetics
• Livestock Breeding
• Quantitative Trait Loci (QTL) Analysis

Background:

• Meat quality traits like marbling and tenderness are crucial in beef cattle production.
• Improving backfat thickness in Canchim cattle via traditional methods is difficult due to low heritability and late measurement.
• Identifying genetic markers for backfat thickness is key for enhancing carcass and meat quality.

Purpose of the Study:

• To identify single nucleotide polymorphisms (SNPs) associated with backfat thickness in Canchim beef cattle.
• To explore novel methodologies for genetic improvement of meat quality traits.
• To lay the groundwork for genomic selection programs in beef cattle.

Main Methods:

• Utilized the random forest approach to identify SNPs linked to backfat thickness.
• Analyzed the variance explained by identified SNPs in deregressed estimated breeding values (dEBVs).
• Investigated quantitative trait loci (QTL) and genes involved in lipid metabolism in SNP-surrounding regions.

Main Results:

• A set of SNPs identified by random forest explained up to 50% of the genetic variance for backfat thickness.
• A focused group of 5 SNPs accounted for 34% of the dEBV variance for backfat thickness.
• Discovered QTLs for fat-related traits and relevant lipid metabolism genes near the identified SNPs.

Conclusions:

• The identified SNPs offer a better understanding of backfat deposition and regulation pathways.
• These findings serve as a foundation for implementing genomic selection for backfat thickness in Canchim cattle.
• This research facilitates genetic improvement strategies for carcass and meat quality in beef production.