Genome-wide association study for reproduction traits in Colombian Creole Blanco Orejinegro cattle.

The profitability of the beef cattle production system relies heavily on reproductive traits. Unfortunately, certain traits, such as age at first calving (AFC), calving interval (CI), and gestation length (GL), can pose challenges in traditional breeding programs because of their low heritability (0.01-0.12) and sex-limited characteristics. Another important aspect is the conservation of the genetic resources of animals adapted to the Colombian regions, which implies the preservation and rational use of the creole breeds in the country market. Therefore, this study aimed to identify genomic regions in the creole cattle breed Blanco Orejinegro (BON) that influence the reproductive traits in females. The dataset comprised 439 animals and 118,116 single-nucleotide polymorphisms' (SNPs) markers. The GS3 program was used to identify the SNP effects employing the BAYES Cπ methodology. The number of SNPs with effect for AFC was 25, 1527 for CI, and 23 for GL. Some of the genes found associated with reproductive and growth traits as well as immune response and environmental adaptation ECE1, EPH, EPHB2, SMARCAL1, IGFBP5, IGFBP2, FCGRT, EGFR, MUL1, PINK1, STPG1, CNGB1, TGFB1, OXTR, IL22RA1, MYOM3, OXTR, CNR2, HIVEP3, CTPS1, CXCL8, FCGRT, MREG, TMEM169, PECR, and MC1R. Our results evidenced a high contribution of the genetic architecture of the Colombian creole cattle breed Blanco Orejinegro that may impact should be included in implementing genetic improvement and conservation programs.

Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy.

The main definition for meat quality should include factors that affect consumer appreciation of the product. Physical laboratory analyses are necessary to identify factors that affect meat quality and specific equipment is used for this purpose, which is expensive and destructive, and the analyses are usually time consuming. An alternative method to performing several beef analyses is near-infrared reflectance spectroscopy (NIRS), which permits to reduce costs and to obtain faster, simpler, and nondestructive measurements. The objective of this study was to evaluate the feasibility of NIRS to predict shear force [Warner-Bratzler shear force (WBSF)], marbling, and color (*a = redness; b* = yellowness; and L* = lightness) in meat samples of uncastrated male Nelore cattle, that were approximately 2-yr-old. Samples of longissimus thoracis (n = 644) were collected and spectra were obtained prior to meat quality analysis. Multivariate calibration was performed by partial least squares regression. Several preprocessing techniques were evaluated alone and in combination: raw data, reduction of spectral range, multiplicative scatter correction, and 1st derivative. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), coefficient of determination in the calibration (R2C), and prediction (R2P) groups. Among the different preprocessing techniques, the reduction of spectral range provided the best prediction accuracy for all traits. The NIRS showed a better performance to predict WBSF (RMSEP = 1.42 kg, R2P = 0.40) and b* color (RMSEP = 1.21, R2P = 0.44), while its ability to accurately predict L* (RMSEP = 1.98, R2P = 0.16) and a* (RMSEP = 1.42, R2P = 0.17) was limited. NIRS was unsuitable to predict subjective meat quality traits such as marbling in Nelore cattle.