Genomic regions and enrichment analyses associated with carcass composition indicator traits in Nellore cattle

Abstract

The aim of this study was to estimate genetic parameters and identify genomic regions associated with carcass traits obtained by ultrasound and visual scores in Nellore cattle. Data from ~66,000 animals from the National Association of Breeders and Researchers (ANCP) were used. The variance components for backfat thickness, rump fat thickness and Longissimus muscle area (LMA) were estimated considering a linear model whereas a threshold model for body structure (BS), finishing precocity (FP) and musculature (MS) traits. The SNP solutions were estimated using the ssGBLUP approach by considering windows of 10 consecutive SNPs. Regions that accounted for more than 1.0% of the additive genetic variance were used. Genes identified within the significant windows, such as FOXA3, AP2S1, FKRP, NPASI and ATP6V1G1, were found to be related with MS, while OMA1 and FFGY with BS and FP traits. The PLTP, TNNC2 and GPAT2 genes were found in the regions associated with LMA, as well as TKT, FNDC5 and CHRND can strongly be related with fat deposition. Gene enrichment analysis revealed processes that might be directly influenced the organism growth and development. These results should help to better understand the genetic and physiological mechanisms regulating growth and body composition, muscle tissue development and subcutaneous fat expression, and this information might be useful for future genomic studies in Nellore cattle.

Genome‐wide association study and pathway analysis for fat deposition traits in nellore cattle raised in pasture–based systems

“…Furthermore, the fact that these are complex and polygenic traits could also explain the lack of common genes, as they might be genetically correlated at the polygenic level (numerous genes with very small effects). The same occurred to Santana et al (2015) and Silva et al (2018), which also worked with BFT and RFT in beef cattle.…”

“…The “SNP moving average” option from the postGSF90 package (Aguilar et al., 2014) was used to back‐solve the genomic estimated breeding values and obtain SNP effects for each trait separately. The window size was defined based on the average linkage disequilibrium decay in Nellore cattle and on the density of the SNP panel used, following similar studies in the literature (e.g., Silva et al., 2018; Stafuzza et al., 2020).…”

Identification of genetic loci associated with growth traits at weaning in yak through a genome‐wide association study

“…In cattle, IL7R (interleukin 7 receptor) is associated with variations with respect to residual feed intake (Salleh et al 2017). OR14J1 (olfactory receptor, family 14, subfamily J, member 1) is associated with a range of carcass-associated traits in Nellore cattle (Silva et al 2019) and dairy cattle (Mao et al 2016).…”

“…HS6ST3 (heparan sulfate 6-O-sulfotransferase) has been associated with residual feed intake (Yao et al 2013), body weight and size (Wang et al 2018), and the fatty acid profile of beef (de Lemos et al 2018). DOCK1 (dedicator of cytokinesis 1) has been reported as a candidate gene for carcass composition indicator traits (Silva et al 2019), marbling score (Ryu & Lee 2016), feed efficiency (Lu et al 2018) and body weight (Li et al 2018a;Reis Neto et al 2019). We identified nine SNPs corresponding to eight genes associated with yak CG at time of weaning.…”

Genomic studies in Montana Tropical Composite cattle

“…The efficiency in the selection process of genetically superior animals in breeding programs, whether purebred, crossbreds or composite animals, depends on the accuracy of estimation of the genetic value of the animals at a young age. The methodological procedures used in the genomic evaluation for multi-breed animals allow more accurate predictions of genetic values through direct comparison between animals of different breed compositions and for the additive and non-additive genetic components BUENO et al 2011, BOCCHI et al, 2016DUENK et al, 2020;GRIGOLETTO et al, 2019;. However, as reported by Elzo and Borjas (2004), , and Misztal et al (2013), these statistical genetic models are complex, providing mismatch between genetic components and inclusion of non-additive genetic effects, dominance and epistasis.…”