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. 2024 Jul 1;25(1):654.
doi: 10.1186/s12864-024-10465-1.

Breed of origin analysis in genome-wide association studies: enhancing SNP-based insights into production traits in a commercial Brangus population

Gabriel A Zayas  1 , Eduardo Rodriguez  2 , Aakilah Hernandez  3 , Fernanda M Rezende  2 , Raluca G Mateescu  2
Affiliations

Breed of origin analysis in genome-wide association studies: enhancing SNP-based insights into production traits in a commercial Brangus population

Gabriel A Zayas et al. BMC Genomics. .

Abstract

Background: Carcass weight (HCW) and marbling (MARB) are critical for meat quality and market value in beef cattle. In composite breeds like Brangus, which meld the genetics of Angus and Brahman, SNP-based analyses have illuminated some genetic influences on these traits, but they fall short in fully capturing the nuanced effects of breed of origin alleles (BOA) on these traits. Focus on the impacts of BOA on phenotypic features within Brangus populations can result in a more profound understanding of the specific influences of Angus and Brahman genetics. Moreover, the consideration of BOA becomes particularly significant when evaluating dominance effects contributing to heterosis in crossbred populations. BOA provides a more comprehensive measure of heterosis due to its ability to differentiate the distinct genetic contributions originating from each parent breed. This detailed understanding of genetic effects is essential for making informed breeding decisions to optimize the benefits of heterosis in composite breeds like Brangus.

Objective: This study aims to identify quantitative trait loci (QTL) influencing HCW and MARB by utilizing SNP and BOA information, incorporating additive, dominance, and overdominance effects within a multi-generational Brangus commercial herd.

Methods: We analyzed phenotypic data from 1,066 genotyped Brangus steers. BOA inference was performed using LAMP-LD software using Angus and Brahman reference sets. SNP-based and BOA-based GWAS were then conducted considering additive, dominance, and overdominance models.

Results: The study identified numerous QTLs for HCW and MARB. A notable QTL for HCW was associated to the SGCB gene, pivotal for muscle growth, and was identified solely in the BOA GWAS. Several BOA GWAS QTLs exhibited a dominance effect underscoring their importance in estimating heterosis.

Conclusions: Our findings demonstrate that SNP-based methods may not detect all genetic variation affecting economically important traits in composite breeds. BOA inclusion in genomic evaluations is crucial for identifying genetic regions contributing to trait variation and for understanding the dominance value underpinning heterosis. By considering BOA, we gain a deeper understanding of genetic interactions and heterosis, which is integral to advancing breeding programs. The incorporation of BOA is recommended for comprehensive genomic evaluations to optimize trait improvements in crossbred cattle populations.

Keywords: Angus; Brahman; Breed of origin; Dominance; GWAS; Heterosis.

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Conflict of interest statement

The authors declare no competing interests.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
Manhattan plots for SNP GWAS on HCW. SNP GWAS modeling markers with additive, dominance from the major allele, dominance from the minor allele and overdominance effects on hot carcass weight, with significance thresholds indicating 1% of the genetic variance (grey dashed line). The variance explained by 10 kb genomic windows was estimated using single-trait WGBLUP using SNP information
Fig. 2
Fig. 2
: Manhattan plots for SNP GWAS on marbling. SNP GWAS modeling markers with additive, dominance from the major allele, dominance from the minor allele and overdominance effects on marbling, with significance thresholds indicating 1% of the genetic variance (grey dashed line). The variance explained by 10 kb genomic windows was estimated using single-trait WGBLUP using SNP information
Fig. 3
Fig. 3
Manhattan plots for BOA GWAS on HCW. BOA GWAS modeling markers with additive, dominance from Angus, dominance from Brahman and overdominance effects on hot carcass weight, with significance thresholds indicating 1% of the genetic variance (grey dashed line). The variance explained by 10 kb genomic windows was estimated using single-trait WGBLUP using BOA information
Fig. 4
Fig. 4
Manhattan plots for BOA GWAS on MARB. BOA GWAS modeling markers with additive, dominance from Angus, dominance from Brahman and overdominance effects on marbling, with significance thresholds indicating 1% of the genetic variance (grey dashed line). The variance explained by 10 kb genomic windows was estimated using single-trait WGBLUP using BOA information
See this image and copyright information in PMC

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