Background: In cattle, base color is assumed to depend on the enzymatic activity specified by the MC1R locus, i.e. the extension locus, with alleles coding for black ( E ^D ), red ( e ), and wild-type ( E ⁺ ). In most mammals, these alleles are presumed to follow the dominance model of E ^D > E ⁺ > e , although exceptions are found. In Bos indicus x Bos taurus F ₂ cattle, some E ^D E ⁺ heterozygotes are discordant with the dominance series for MC1R and display various degrees of red pigmentation on an otherwise predicted black background. The objective of this study was to identify loci that modify black coat color in these individuals. Results: Reddening was classified with a subjective scoring system. Interval analyses identified chromosome-wide suggestive ( P < 0.05) and significant ( P < 0.01) QTL on bovine chromosomes (BTA) 4 and 5, although these were not confirmed using single-marker association or Bayesian methods. Evidence of a major locus ( F = 114.61) that affects reddening was detected between 60 and 73 Mb on BTA 6 (Btau4.0 build), and at 72 Mb by single-marker association and Bayesian methods. The posterior mean of the genetic variance for this region accounted for 43.75% of the genetic variation in reddening. This region coincided with a cluster of tyrosine kinase receptor genes ( PDGFRA , KIT and KDR ). Fitting SNP haplotypes for a 1 Mb interval that contained all three genes and centered on KIT accounted for the majority of the variation attributed to this major locus, which suggests that one of these genes or associated regulatory elements, is responsible for the majority of variation in degree of reddening. Conclusions: Recombinants in a 5 Mb region surrounding the cluster of tyrosine kinase receptor genes implicated PDGFRA as the strongest positional candidate gene. A higher density marker panel and functional analyses will be required to validate the role of PDGFRA or other regulatory variants and their interaction with MC1R for the modification of black coat color in Bos indicus influenced cattle.

The animal’s reaction to human handling (i.e., temperament) is critical for work safety, productivity, and welfare. Subjective phenotyping methods have been traditionally used in beef cattle production. Even so, subjective scales rely on the evaluator’s knowledge and interpretation of temperament, which may require substantial experience. Selection based on such subjective scores may not precisely change temperament preferences in cattle. The objectives of this study were to investigate the underlying genetic interrelationships among temperament measurements using genetic factor analytic modeling and validate a movement-based objective method (four-platform standing scale, FPSS) as a measure of temperament. Relationships among subjective methods of docility score (DS), temperament score (TS), 12 qualitative behavior assessment (QBA) attributes and objective FPSS including the standard deviation of total weight on FPSS over time (SSD) and coefficient of variation of SSD (CVSSD) were investigated using 1,528 calves at weaning age. An exploratory factor analysis (EFA) identified two latent variables account for TS and 12 QBA attributes, termed difficult and easy from their characteristics. Inclusion of DS in EFA was not a good fit because it was evaluated under restraint and other measures were not. A Bayesian confirmatory factor analysis inferred the difficult and easy scores discovered in EFA. This was followed by fitting a pedigree-based Bayesian multi-trait model to characterize the genetic interrelationships among difficult, easy, DS, SSD, and CVSSD. Estimates of heritability ranged from 0.18 to 0.4 with the posterior standard deviation averaging 0.06. The factors of difficult and easy exhibited a large negative genetic correlation of -0.92. Moderate genetic correlation was found between DS and difficult (0.36), easy (-0.31), SSD (0.42), and CVSSD (0.34) as well as FPSS with difficult (CVSSD: 0.35; SSD: 0.42) and easy (CVSSD: -0.35; SSD: -0.4). Correlation coefficients indicate selection could be performed with either and have similar outcomes. We contend that genetic factor analytic modeling provided a new approach to unravel the complexity of animal behaviors and FPSS-like measures could increase the efficiency of genetic selection by providing automatic, objective, and consistent phenotyping measures that could be an alternative of DS, which has been widely used in beef production.

Ewe lifetime productivity has economic implications for producers because shorter lifetime productivity results in less profit. Productive years of ewes from extensive, range-based systems of the United States West are generally less than ewes from more temperate regions of the United States. Accordingly, ewes from range-based systems, especially those employing shed-lambing strategies, have been selected for increased litter size to offset decreased lifetime productivity. However, the relationship of the ewe's birth litter size (ELS_B) has not been considered a potential contributor to lifetime productivity. Longevity (number of productive years, n = 1 per population) and stayability (probability to survive to the next age; ages 2-7 years, n = 6 per population) were investigated to understand ELS_B effects on productive life. Columbia, Polypay, Rambouillet, and Targhee breeds were used in this study. Across-breed (n = 11 550) and within-breed (Columbia, n = 4 398; Polypay, n = 4 534; Rambouillet, n = 5 922; Targhee, n = 6 482) analyses were used. Depending on the population, records spanned from 1950 to 2008, where ewe's birth year was included as a fixed effect in the animal model using restricted maximum likelihood estimation procedures. Fixed effects investigated included ELS_B (single-, twin-, or triplet-born) and ewe breed (across-breed analyses only). Regardless of trait or population used, heritability ranged from 0.06 ± 0.02 to 0.34 ± 0.03, where stayability at younger ages had the highest estimates. The breed effect was significant in all across-breed analyses (0.0001 ≤ P ≤ 0.038; n = 7), where Polypay, a breed selected for accelerated lambing and increased fertility, averaged shorter productive life or had a lower probability of survival to the next age compared with other breeds (longevity: 0.009 ≤ P ≤ 0.223; stayability: 0.000 ≤ P ≤ 0.842). The ELS_B was significant in 60% (n = 5) and 37% (n = 30) of longevity and stayability analyses, respectively. Except for Targhee, all analyses showed ewes born in smaller litter sizes were associated with longer productive lives or higher probability of surviving to the next age, particularly in across-breed analyses (e.g., longevity: single- vs twin-born ewes, P = 0.004; vs triplet-born ewes, P = 0.003). This study provides evidence that increasing prolificacy in ewes from extensive, range-based production systems may impact productive life. Due to the low heritability of these traits, additional investigation into modeling these traits with dominance effects and litter size needs to be conducted.

American Aberdeen (AD) cattle in the USA descend from an Aberdeen Angus herd originally brought to the Trangie Agricultural Research Centre, New South Wales, AUS. Although put under specific selection pressure for yearling growth rate, AD remain genomically uncharacterized. The objective was to characterize the genetic diversity and structure of purebred and crossbred AD cattle relative to seven common USA beef breeds using available whole-genome SNP data. A total of 1140 animals consisting of 404 purebred (n = 8 types) and 736 admixed individuals (n = 10 types) was used. Genetic diversity metrics, an analysis of molecular variance, and a discriminant analysis of principal components were employed. When linkage disequilibrium was not accounted for, markers influenced basic diversity parameter estimates, especially for AD cattle. Even so, intrapopulation and interpopulation estimates separate AD cattle from other purebred types (e.g., Latter’s pairwise F_ST ranged from 0.1129 to 0.2209), where AD cattle were less heterozygous and had lower allelic richness than other purebred types. The admixed AD-influenced cattle were intermediate to other admixed types for similar parameters. The diversity metrics separation and differences support strong artificial selection pressures during and after AD breed development, shaping the evolution of the breed and making them genomically distinct from similar breeds.

Despite high-throughput and large-scale phenotyping becoming easier, interpretation of such data in cattle production remains challenging due to the complex and highly correlated nature of many traits. Underlying biological traits (UBT) of economic importance are defined by a subset of easy-to-measure traits, leading to challenges in making appropriate selection decisions on them. Research on UBT in beef cattle is limited. In this study, the phenotypic data of admixed beef heifers (n = 336) for reproductive, body conformation, and carcass-related traits (traits, t = 35) were used to identify latent variables from factor analysis (FA) that can be characterized as UBT. Given sample size constraints for carcass (n = 161) and other body size-related traits (n = 336), two models were explored. In Model 1, all individual traits were considered (n = 161), while in Model 2, the dataset was split into body size (n = 336) and carcass (n = 161) traits to maximize available heifers per dataset. A combination of FA and Bayesian network (BN) learning was adopted to develop UBT and infer BN structure for subsequent analyses. All heifers (n = 336) were genotyped using GeneSeek Genomic Profiler 150K for Beef Cattle. Following quality checks, 117,373 autosomal SNP markers were retained and used for genomic estimated breeding values (gEBV) in BN learning steps. Using exploratory and confirmatory FA, Body Size (BS) and Body Composition (BC) were identified as UBT for Model 1, explaining 14 phenotypic traits (t = 14). For Model 2, BS, Ovary Size, and Yield Grade (YG) were identified as UBT, explaining 12 phenotypic traits (t = 12). When using gEBV, the causal network structure inferred showed BS contributed to BC in Model 1 and to Ovary Size in Model 2. Therefore, a structure equation-based approach should be used in subsequent modeling for these traits. From Model 2, YG should be modeled univariately. This study is the first to identify UBT in growing admixed heifers using body size, conformation, and carcass traits. We also identified that BC and YG did not explain intra-muscular fat and body density, indicating these two traits should also be modeled univariately.

Selection for better reproductive performance is a time-consuming process. As reproduction is a complex trait (i.e., controlled by many genes and environmental factors), some genomic locations might account for large amounts of genetic variation, but this is not well understood. Molecular markers in or around genes may be involved directly or indirectly in reproduction. Therefore, selection programs using specific genetic markers could be a good strategy for precise and improved genetic changes of these traits. Many studies reported that leptin protein may affect the hypothalamo–pituitary–gonadal axis through specific hypothalamic receptors (e.g., Williams et al., 2002). Leptin (LEP/Sau3AI) and leptin receptor (LEPR/T945M) have known association with milk production traits, calving interval (CI), and age at first calving (AgeFC) in Slovak spotted and Pinzgauer cows ( Trakovicka et al., 2013). Almeida et al. (2003) also found that the CI and weight at first calving increased when considering different markers (LEP/Sau3AI and IDVGA-51) in LEP for Bos indicus × Bos taurus cattle. In addition, Clempson et al. (2011) further supported the role of LEP genotype in reproductive traits by finding association of fertility traits (e.g., age at first service, total number of artificial insemination services, days to conception, and CI) with LEP single nucleotide polymorphism markers in Holstein heifers and cows. Therefore, it has been established that selection using the LEP marker can be performed in cattle. However, there is limited information on the association of the LEP genotype with reproductive characteristics such as gestation length, pregnancy status, weaning success, and reproductive success over time in commercial beef cows. In addition, little is known on the effect of the LEP genotype and circulating leptin hormone (LEPH) concentration on antral follicle count, reproductive tract score, and ovary measurements in forage-fed developing beef heifers. Thus, this study was conducted to determine the association of the LEP genotype and circulating LEPH concentrations with reproductive characteristics in commercial beef cows and developing heifers.