Genomic association using principal components of morphometric traits in horses: identification of genes related to bone growth

The measurement of morphometric traits in horses is important for determining breed qualification and is one of the main selection criteria for the species. The development of an index (HPC) that consists of principal components weighted by additive genetic values allows to explore the most relevant relationships using a reduced number of variables that explain the greatest amount of variation in the data. Genome-wide association studies (GWAS) using HPC are a relatively new approach that permits to identify regions related to a set of traits. The aim of this study was to perform GWAS using HPC for 15 linear measurements as the explanatory variable in order to identify associated genomic regions and to elucidate the biological mechanisms linked to this index in Campolina horses. For GWAS, weighted single-step GBLUP was applied to HPC. The eight genomic windows that explained the highest proportion of additive genetic variance were identified. The sum of the additive variance explained by the eight windows was 95.89%. Genes involved in bone and cartilage development were identified (SPRY2, COL9A2, MIR30C, HEYL, BMP8B, LTBP1, FAM98A, and CRIM1). They represent potential positional candidates for the HPC of the linear measurements evaluated. The HPC is an efficient alternative to reduce the 15 usually measured traits in Campolina horses. Moreover, candidate genes inserted in region that explained high additive variance of the HPC were identified and might be fine-mapped for searching putative mutation/markers.

Selection effect for growth traits on energy requirements in beef Nelore steers

Growth data of 77,372 Nelore steers were used to estimate the selection effect on energy requirements considering two beef production systems: cow-calf and slaughter cycles. All the animals had measures from 120 days to 7 years old. The parameters necessary to evaluate the selection effect on energy requirements were obtained by random regression analysis using Legendre polynomials. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered random covariables. The coefficients from the model M3353_5 were used to calculate the genetic gains necessary to predict the increase in phenotypes. The selection was simulated for body weight (BW) and weight gain (WG) at different ages and energy requirements were calculated using NRC equations. The cost of feed was calculated for a cow-calf and slaughter cycle of production considering a system of Brachiaria decumbens pasture without supplementation. In slaughter system, the selection for weight of 365 days of age is the best option. In cow-calf systems, the selection W120 is the best choice.

44 Development and Application of Novel Phenotyping Technologies to Swine Production and Breeding

Advancement within numerous categories of technology are beginning to see application for improving production and genetic advancement in pigs. Digital technologies, particularly video, are creating a step change in improved animal environments, biosecurity, vaccine management and disease intervention. For breeders, video technology creates opportunity to begin assessing animal behavior on a useful scale and to expand the quantity and type of animals measured for growth rate, body composition, structural integrity and feed efficiency. A seemingly endless stream of ‘omics’ data is becoming less costly to obtain and includes such datasets as common SNP arrays in addition to DNA sequence, transcriptome, proteome, metabolome, gut microbiome and more. Leveraging the capabilities of these data to predict a phenotype is key to unlocking the full potential of ‘omics’ data. Novel, at least to animal scientists, analytical techniques are required to understand gene networks and their associated expression profiles. These techniques apply equally to video data, which in its purest form, is an interpretation of complex patterns. Scaling and applying these techniques will be important over the coming decade. There exists today a very applied approach to using these technologies in the swine industry. One of the most complex and well-trained pattern recognition technologies lies within our own brain. Experienced managers reviewing video of pigs within facilities, team members entering and performing critical activities impacting biosecurity and coordinating these observations with simple interventions can make large, positive improvements in productivity. Investing in hardware and time will produce results that reduce stressors, improve processes that protect the farm from pathogens and result in better overall animal well-being. This talk will focus on examples of current applications that are of benefit today. In addition, we hope to promote discussion around priorities for genetic improvement, pig management and the practical needs to move a variety of technologies from research to development in the field.

The effect of mitochondrial DNA polymorphisms on cattle reproduction

The aim of this study was to identify SNPs located in mitochondrial DNA that are associated with reproductive traits in beef cows. A total of 1999 Nelore females genotyped with the high-density Illumina BovineHD BeadChip (Illumina Inc., San Diego, CA, USA) were used to study the association of mitochondrial DNA variants with reproductive traits using a single-step procedure. In a preliminary analysis, the present results indicate a small participation of the mitogenome in the expression of reproductive traits in beef cattle. However, possible difficulties related to the biological characteristics of mitochondrial DNA and its inheritance, genotyping, and annotation of the phenotypes studied may also explain the results.

Effect of the X chromosome in genomic evaluations of reproductive traits in beef cattle

The aim of this study was to evaluate whether there are predictive advantages for breeding values with inclusion of X chromosome genomic markers for reproductive (occurrence of early pregnancy - P16 and age at first calving - AFC) and andrological (scrotal circumference -SC) variables in beef cattle. There were 3263 genotypes of females and males evaluated. There were breeding value estimates for SC, AFC and P16 considering two scenarios: 1) only autosomal markers or 2) autosomal and X chromosome markers. To evaluate effects of inclusion of X chromosome markers on selection, responses to selection were compared including or not including genomic marker information from the X chromosome. There were greater heritability estimates for SC (0.40 and 0.31), AFC (0.11 and 0.09) and P16 (0.43 and 0.38) when analyses included, compared with not including, genomic marker information from the X chromosome. When selection is based on results from analyses that did not include information for the X chromosome, there was about a 7 % lesser mean genomic breeding value for the SC traits for selected animals. For P16, there was an approximate 4% lesser breeding value without inclusion of genomic marker information from the X chromosome, while this inclusion did not have as great an effect on the breeding value for AFC. There was an average predictive correlation of 0.79, 0.98 and 0.84 for SC, AFC and P16, respectively. These estimates indicate inclusion of the X chromosome genomic marker information in the analysis can improve prediction of genomic breeding values, especially for SC.

Principal components for morphometric traits in Campolina horses

Principal component analysis (PCA) was applied to evaluate the genetic variability and relationship between 15 morphometric traits in 91,483 Campolina horses, as well as to propose an index based on an aggregate genotype that promotes a particular selection objective. PCA was applied to the genetic (co)variance matrix among variables. After calculation of the principal components, the breeding values were estimated to obtain an index related to the component that explained most of the variation. The first principal component (PC1) accounted for 97.8% of the total additive genetic variance of the traits. PC1 contrasted animals in terms of body size (wither, back and croup heights, body length, and thoracic girth). PC1 traits showed higher heritabilities and positive and high genetic correlations. An index was obtained (HPC1) with the combination of the breeding values of different traits from PC1 which permitted the use of this index as an aggregate genotype to identify the best animals for selection. The second principal component (PC2) was much smaller and grouped traits related to head and neck morphometry, among others. These traits are commonly used for breed qualification, a fact explaining the small variation in this component. An evaluation of the effect of HPC1 on withers height in two-trait analysis was also made which provided positive genetic correlations of moderate to high magnitude (0.73-0.86), indicating that selection for this trait (important in Campolina horses) is accounted for in the index. The use of HCP1 could be considered as an important alternative to selection since it does not consider a single trait but rather a set of variables that capture body proportions.

Mapping genomic regions for reproductive traits in beef cattle: Inclusion of the X chromosome

Although the second largest chromosome of the genome, the X chromosome is usually excluded from genome-wide association studies (GWAS). Considering the presence and importance of genes on this chromosome that are involved in reproduction, the aim of this study was to evaluate the effect of its inclusion in GWAS on reproductive traits (scrotal circumference [SC], early pregnancy [P16] and age at first calving [AFC]) in a Nelore herd. Genotype data from 3,263 animals with the above-mentioned phenotypes were used. The results showed an increase in the variances explained by the autosomal markers for all traits when the X chromosome was not included. For SC, there was an increase of more than 10% for the windows on chromosomes 2 and 6. For P16, the effect was increased by almost 20% for windows on chromosome 5. The same pattern was found for AFC, with an increase of more than 10% for the most important windows. The results indicate that the noninclusion of the X chromosome can overestimate the effects of autosomes on SC, P16 and AFC not only because of the additive effect of the X chromosome itself but also because of its epistatic effect on autosomal genes.

Genetic association of productive and reproductive traits with stayability in Nellore cattle: analysis using Bayesian models

We evaluated the genetic association of growth traits [weight adjusted to 205 days of age (W205), 365 days of age (W365), and 550 days of age (W550); weight gain between 205 days of age and 365 days of age (WG1) and between 365 days of age and 550 days of age (WG2)] and reproductive traits [age at first calving (AFC); first calving interval (FCI)] with stayability in the herd (STAY), using Bayesian inference in linear and threshold models. We defined STAY as the probability of a cow calving three or more times before the age of 76 months, given that she had calved at least once. We assigned binary codes (0, failure; 1, success) to each female. We used a sire model for analysis and formed different contemporary groups for the investigated traits. We analyzed the results by applying a two-trait sire model that included STAY (threshold trait) and linear traits (W205, W365, W550, WG1, WG2, AFC, and FCI). We used Gibbs sampling to estimate variance components and heritabilities. In all the analyses, we found that the mean heritability estimates for STAY were of moderate magnitude (0.20-0.25). The mean heritabilities for W205, W365, W550, WG1, WG2, AFC, and FCI were 0.20, 0.23, 0.39, 0.08, 0.14, 0.12, and 0.11, respectively. We observed wide variation in the posterior distributions of genetic correlations; however, with the exception of those obtained for the reproductive traits, the mean estimates were of low magnitude. Selection for WG2 can results in favorable correlated response in STAY.

Study of lipid metabolism-related genes as candidate genes of sexual precocity in Nellore cattle

The objective of this study was to identify associations between known polymorphisms in genes related to adipose tissue and sexual precocity in Nellore cattle. A total of 1689 precocious and non-precocious heifers belonging to farms participating in Conexão Delta G breeding program were studied. SNPs from the Illumina High-Density Bovine SNP BeadChip were used. This chip contains 777,000 SNPs located within the region of the candidate genes at a distance of up to 5 kb, considering that linkage disequilibrium (LD) exists at this distance. Linear models were used for statistical analysis. The fastPHASE and GenomeStudio programs were used for haplotype reconstruction and LD analysis based on r2 statistics. Fifty-seven candidate genes and 443 SNPs were analyzed: among the latter, 370 SNPs formed 83 haplotypes, while the remaining SNPs were studied separately. Statistical analysis showed that only three haplotypes, one haplotype consisting of two SNPs located in the FABP4 gene and two haplotypes consisting of four and two SNPs located in the PPP3CA gene, had a significant effect on sexual precocity at P < 0.05. It can be concluded that the FABP4 and PPP3CA genes influence sexual precocity and may therefore be used in selection programs designed to improve sexual precocity in Nellore cattle.

Cluster and meta-analyses of genetic parameters for feed intake traits in growing beef cattle

A data set based on 50 studies including feed intake and utilization traits was used to perform a meta-analysis to obtain pooled estimates using the variance between studies of genetic parameters for average daily gain (ADG); residual feed intake (RFI); metabolic body weight (MBW); feed conversion ratio (FCR); and daily dry matter intake (DMI) in beef cattle. The total data set included 128 heritability and 122 genetic correlation estimates published in the literature from 1961 to 2012. The meta-analysis was performed using a random effects model where the restricted maximum likelihood estimator was used to evaluate variances among clusters. Also, a meta-analysis using the method of cluster analysis was used to group the heritability estimates. Two clusters were obtained for each trait by different variables. It was observed, for all traits, that the heterogeneity of variance was significant between clusters and studies for genetic correlation estimates. The pooled estimates, adding the variance between clusters, for direct heritability estimates for ADG, DMI, RFI, MBW and FCR were 0.32 ± 0.04, 0.39 ± 0.03, 0.31 ± 0.02, 0.31 ± 0.03 and 0.26 ± 0.03, respectively. Pooled genetic correlation estimates ranged from -0.15 to 0.67 among ADG, DMI, RFI, MBW and FCR. These pooled estimates of genetic parameters could be used to solve genetic prediction equations in populations where data is insufficient for variance component estimation. Cluster analysis is recommended as a statistical procedure to combine results from different studies to account for heterogeneity.

Genotype by environment interaction in Nelore cattle from five Brazilian states

Records from 75,941 Nelore cattle were used to determine the importance of genotype by environment interaction (GEI) in five Brazilian states. (Co)variance components were estimated by single-trait analysis (with yearling weight, W450, considered to be the same trait in all states) and multiple-trait analysis (with the record from each state considered to be a different trait). The direct heritability estimates for yearling weight were 0.51, 0.39, 0.44, 0.37 and 0.41 in the states of Goiás, Mato Grosso, São Paulo, Mato Grosso do Sul and Minas Gerais, respectively. The across-state genetic correlation estimates between Goiás and Mato Grosso, Goiás and Minas Gerais, São Paulo and Minas Gerais, and Mato Grosso do Sul and Minas Gerais ranged from 0.67 to 0.75. These estimates indicate that GEIs are biologically important. No interactions were observed between Goiás and São Paulo, Goiás and Mato Grosso do Sul, Mato Grosso and São Paulo, Mato Grosso and Mato Grosso do Sul, Mato Grosso and Minas Gerais, or São Paulo and Mato Grosso do Sul (0.82 to 0.97). Comparison of single and multiple-trait analyses showed that selection based on the former was less efficient in the presence of GEI, with substantial losses (up to 10%) during selection.