Linkage disequilibrium and effective population size in Gir cattle selected for yearling weight.

Linkage disequilibrium (LD) plays an important role in genomic selection and mapping of quantitative trait loci (QTL). This study investigated the pattern of LD and effective population size (Ne ) in Gir cattle selected for yearling weight. For this purpose, 173 animals with imputed genotypes (from 18 animals genotyped with the Illumina BovineHD BeadChip and 155 animals genotyped with the Bovine LDv4 panel) were analysed. The LD was evaluated at distances of 25-50 kb, 50-100 kb, 100-500 kb and 0.5-1 Mb. The Ne was estimated based on 5 past generations. The r2 values (a measure of LD) were, respectively, .35, .29, .18 and .032 for the distances evaluated. The LD estimates decreased with increasing distance of SNP pairs and LD persisted up to a distance of 100 kb (r2  = .29). The Ne was greater in generations 4 and 5 (24 and 30 animals, respectively) and declined drastically after the last generation (12 animals). The results showed high levels of LD and low Ne , which were probably due to the loss of genetic variability as a consequence of the structure of the Gir population studied.

Genomic association for sexual precocity in beef heifers using pre-selection of genes and haplotype reconstruction.

Reproductive traits are of the utmost importance for any livestock farming, but are difficult to measure and to interpret since they are influenced by various factors. The objective of this study was to detect associations between known polymorphisms in candidate genes related to sexual precocity in Nellore heifers, which could be used in breeding programs. Records of 1,689 precocious and non-precocious heifers from farms participating in the Conexão Delta G breeding program were analyzed. A subset of single nucleotide polymorphisms (SNP) located in the region of the candidate genes at a distance of up to 5 kb from the boundaries of each gene, were selected from the panel of 777,000 SNPs of the High-Density Bovine SNP BeadChip. Linear mixed models were used for statistical analysis of early heifer pregnancy, relating the trait with isolated SNPs or with haplotype groups. The model included the contemporary group (year and month of birth) as fixed effect and parent of the animal (sire effect) as random effect. The fastPHASE® and GenomeStudio® were used for reconstruction of the haplotypes and for analysis of linkage disequilibrium based on r2 statistics. A total of 125 candidate genes and 2,024 SNPs forming haplotypes were analyzed. Statistical analysis after Bonferroni correction showed that nine haplotypes exerted a significant effect (p<0.05) on sexual precocity. Four of these haplotypes were located in the Pregnancy-associated plasma protein-A2 gene (PAPP-A2), two in the Estrogen-related receptor gamma gene (ESRRG), and one each in the Pregnancy-associated plasma protein-A gene (PAPP-A), Kell blood group complex subunit-related family (XKR4) and mannose-binding lectin genes (MBL-1) genes. Although the present results indicate that the PAPP-A2, PAPP-A, XKR4, MBL-1 and ESRRG genes influence sexual precocity in Nellore heifers, further studies are needed to evaluate their possible use in breeding programs.

Genetic parameters for test-day yield of milk, fat and protein in buffaloes estimated by random regression models.

The test-day yields of milk, fat and protein were analysed from 1433 first lactations of buffaloes of the Murrah breed, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, born between 1985 and 2007. For the test-day yields, 10 monthly classes of lactation days were considered. The contemporary groups were defined as the herd-year-month of the test day. Random additive genetic, permanent environmental and residual effects were included in the model. The fixed effects considered were the contemporary group, number of milkings (1 or 2 milkings), linear and quadratic effects of the covariable cow age at calving and the mean lactation curve of the population (modelled by third-order Legendre orthogonal polynomials). The random additive genetic and permanent environmental effects were estimated by means of regression on third- to sixth-order Legendre orthogonal polynomials. The residual variances were modelled with a homogenous structure and various heterogeneous classes. According to the likelihood-ratio test, the best model for milk and fat production was that with four residual variance classes, while a third-order Legendre polynomial was best for the additive genetic effect for milk and fat yield, a fourth-order polynomial was best for the permanent environmental effect for milk production and a fifth-order polynomial was best for fat production. For protein yield, the best model was that with three residual variance classes and third- and fourth-order Legendre polynomials were best for the additive genetic and permanent environmental effects, respectively. The heritability estimates for the characteristics analysed were moderate, varying from 0·16±0·05 to 0·29±0·05 for milk yield, 0·20±0·05 to 0·30±0·08 for fat yield and 0·18±0·06 to 0·27±0·08 for protein yield. The estimates of the genetic correlations between the tests varied from 0·18±0·120 to 0·99±0·002; from 0·44±0·080 to 0·99±0·004; and from 0·41±0·080 to 0·99±0·004, for milk, fat and protein production, respectively, indicating that whatever the selection criterion used, indirect genetic gains can be expected throughout the lactation curve.

Genetic parameters of total milk yield and factors describing the shape of lactation curve in dairy buffaloes.

The objective of this study was to apply factor analysis to describe lactation curves in dairy buffaloes in order to estimate the phenotypic and genetic association between common latent factors and cumulative milk yield. A total of 31 257 monthly test-day milk yield records from buffaloes belonging to herds located in the state of São Paulo were used to estimate two common latent factors, which were then analysed in a multi-trait animal model for estimating genetic parameters. Estimates of (co)variance components for the two common latent factors and cumulated 270-d milk yield were obtained by Bayesian inference using a multiple trait animal model. Contemporary group, number of milkings per day (two levels) and age of buffalo cow at calving (linear and quadratic) as covariate were included in the model as fixed effects. The additive genetic, permanent environmental and residual effects were included as random effects. The first common latent factor (F1) was associated with persistency of lactation and the second common latent factor (F2) with the level of production in early lactation. Heritability estimates for F1 and F2 were 0·12 and 0·07, respectively. Genetic correlation estimates between F1 and F2 with cumulative milk yield were positive and moderate (0·63 and 0·52). Multivariate statistics employing factor analysis allowed the extraction of two variables (latent factors) that described the shape of the lactation curve. It is expected that the response to selection to increase lactation persistency is higher than the response obtained from selecting animals to increase lactation peak. Selection for higher total milk yield would result in a favourable correlated response to increase the level of production in early lactation and the lactation persistency.

Genetic parameters for stayability in Murrah buffaloes.

In order to contribute to the breeding programmes of Asian water buffalo, the aim of this study was to analyse the influence of genetic effects in the stayability of Murrah dairy buffaloes. The stayability trait (ST) was defined as the female's ability to stay in the herd for one (ST1), two (ST2), three (ST3), four (ST4), five (ST5) or six years (ST6) after the first calving. The same trait was also considered as continuous and was designated stayability in days up to one (STD1), two (STD2), three (STD3), four (STD4), five (STD5) or six years (STD6) after the first calving. Data from 1016 females reared in nine herds located in the State of São Paulo, Brazil, were analysed. Statistical models included the additive genetic effect of the animal and the fixed effects of the buffalo breeding herd, birth year and birth season. Additive effects for ST were estimated by approximate restricted maximum likelihood using a threshold model, while for STD, the additive effects were estimated by restricted maximum likelihood. Heritability estimates were lower for ST, except for ST1, (0.11+/-0.07, 0.17+/-0.06, 0.23+/-0.06, 0.16+/-0.08, 0.14+/-0.09 and 0.16+/-0.10 for ST1, ST2, ST3, ST4, ST5 and ST6, respectively) when compared with STD (0.05+/-0.06, 0.18+/-0.08, 0.40+/-0.10, 0.49+/-0.11, 0.41+/-0.11 and 0.30+/-0.13, for STD1, STD2, STD3, STD4, STD5 and STD6, respectively). Considering the values of heritability and owing to the serial nature of STD to a specific age, selection for STD3 should have a favourable influence on STD to other ages.