Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.

The Brazilian Guzerá population originated from a few founders introduced from India. These animals adapted well to the harsh environments in Brazil, were selected for beef, milk, or dual-purpose (beef and milk), and were extensively used to produce crossbred animals. Here, the impact of these historical events with regard to the population structure and genetic diversity in a Guzerá meta-population was evaluated. DNA samples of 744 animals (one dairy, nine dual-purpose, and five beef herds) were genotyped for 21 microsatellite loci. Ho, He, PIC, F_is, F_it, and F_st estimates were obtained considering either farms or lineages as subpopulations. Mean Ho (0.73) and PIC (0.75) suggest that genetic diversity was efficiently conserved. F_it, F_is and F_st values (95% CI) pointed to a low fixation index, and large genetic diversity: F_it (Farms = 0.021–0.100; lineages = 0.021–0.100), F_is (Farms = –0.007–0.076; lineages = −0.014–0.070), and F_st (Farms = 0.0237–0.032; lineages = 0.029–0.038). The dual-purpose herds/selection lines are the most uniform subpopulation, while the beef one preserved larger amounts of genetic diversity among herds. In addition, the dairy herd showed to be genetically distant from other herds. Taken together, these results suggest that this Guzerá meta-population has high genetic diversity, a low degree of population subdivision, and a low inbreeding level.

To implement an animal breeding program, it is important to define the production circumstances of the animals of interest to determine which traits of economic interest will be selected for the breeding goal. The present study defined breeding goals and proposed selection indices for milk production and quality traits of Gir dairy cattle. First, a bioeconomic model was developed to calculate economic values. The genetic and phenotypic parameters were estimated based on records from 22,468 first-lactation Gir dairy cows and their crosses for which calving occurred between 1970 and 2011. Statistical analyses were carried out for the animal model, with multitrait analyses using the restricted maximum likelihood method. Two situations were created in the present study to define the breeding goals: 1) including only milk yield in the breeding goal (HGL1) and 2) including fat and protein in addition to the milk yield (HGL2). The heritability estimates for milk, protein, and fat production were 0.33 ± 0.02, 0.26 ± 0.02, and 0.24 ± 0.02, respectively. All phenotypic and genetic correlations were highly positive. The economic values for milk, fat, and protein were US$0.18, US$0.27, and US$7.04, respectively. The expected economic responses for HGL2 and for HGL1 were US$126.30 and US$79.82, respectively. These results indicate that milk component traits should be included in a selection index to rank animals evaluated in the National Gir Dairy Breeding Program developed in Brazil.

Objective: The aim of this study was to estimate genetic parameters for 305-day cumulative milk yield and components, growth, and reproductive traits in Guzerá cattle.Methods: The evaluated traits were 305-day first-lactation cumulative yields (kg) of milk (MY305), fat (FY305), protein (PY305), lactose (LY305), and total solids (SY305); age at first calving (AFC) in days; adjusted scrotal perimeter (cm) at the ages of 365 (SP365) and 450 (SP450) days; and adjusted body weight (kg) at the ages of 210 (W210), 365 (W365), and 450 (W450) days. The (co)variance components were estimated using the restricted maximum likelihood method for single-trait, bi-trait and tri-trait analyses. Contemporary groups and additive genetic effects were included in the general mixed model. Maternal genetic and permanent environmental effects were also included for W210.Results: The direct heritability estimates ranged from 0.16 (W210) to 0.32 (MY305). The maternal heritability estimate for W210 was 0.03. Genetic correlation estimates among milk production traits and growth traits ranged from 0.92 to 0.99 and from 0.92 to 0.99, respectively. For milk production and growth traits, the genetic correlations ranged from 0.33 to 0.56. The genetic correlations among AFC and all other traits were negative (–0.43 to –0.27). Scrotal perimeter traits and body weights showed genetic correlations ranging from 0.41 to 0.46, and scrotal perimeter and milk production traits showed genetic correlations ranging from 0.11 to 0.30. The phenotypic correlations were similar in direction (same sign) and lower than the corresponding genetic correlations.Conclusion: These results suggest the viability and potential of joint selection for dairy and beef traits in Guzerá cattle, taking into account reproductive traits.